Why are Macro-economic Growth Forecasts so wrong?

Why are Macro-economic Growth Forecasts so wrong?

 

There are several institutions which publish economic forecasts annually/quarterly.

  • IMF
  • OECD
  • EC

Central Banks of Nations also publish economic Forecasts.  For Example:

  • Federal Reserve Bank of USA
  • Bank of England
  • Bank of Canada
  • Riksbank of Sweden
  • European Central Bank

 

There are several surveys of professional forecasters which create consensus forecasts to improve the accuracy of forecasts.

  • US Fed Reserve Survey of Professional Forecasters
  • ECB Survey of Professional Forecasters
  • Consensus Forecasts by Consensus Forecasts
  • Federal Reserve Blue Book
  • Federal Reserve Livingston Survey
  • Blue Chip Economic Forecasts by Wolters Kluwers

 

International Forecasting organizations (Private and Government)

  • IMF, “World Economic Outlook”;
  • EC, “European Economic Forecast”;
  • OECD, “OECD Economic Outlook”;
  • Consensus Economics, “Consensus Forecasts”;
  • The Economist, “The Economist pool of forecasters”.

 

USA Private and Government Economic Forecasters

  • Fed Reserve Survey of Professional Forecasters
  • Blue Chip Economic Indicators ( Wolters Kluwer)
  • Green Book
  • Livingston Survey
  • CBO
  • FOMC
  • Office of Management and Budget (OMB)
  • Western Blue Chip Economic Forecast

 

From Swiss Re Report May 2017

imfforecast

From Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors: The Federal Reserve’s Approach

RMSE GDP

 

Surveys of Economic Forecasters:

list

 

USA Private Economic Forecasters

There are many private forecasters who also publish forecasts.  For Example:

  • The Conference Board
  • Wells Fargo Bank
  • Goldman Sachs
  • Citi Group
  • Haver Analytics
  • RSQE Forecasts at University of Michigan

 

See the lists below for almost all of professional forecasters.

private forecasters USA

 

From Blue Chip Economic Forecast:

bluechip2bluechip

From Social Learning, Strategic Incentives and Collective Wisdom: An Analysis of the Blue Chip Forecasting Group

forecastersforecasters2

 

There is also in UK:

  • NIESR ( National Institute of Economic and Social Research)

 

From time to time many of these organizations review quality of their forecasts.  Results of these studies are published in papers many of which are listed in references below.

After the global financial Crisis of 2008-2009, many institutions have taken another look at their models used for forecasting economic variables.

See recent papers by

  • Bank of Canada
  • IMF
  • OECD
  • Fed Reserve
  • Bank of England
  • Riksbank of Sweden
  • US CBO
  • Bank of Portugal
  • European Commission

 

From Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors: The Federal Reserve’s Approach

Since late 2007, the Federal Open Market Committee (FOMC) of the U.S. Federal Reserve has regularly published assessments of the uncertainty associated with the projections of key macroeconomic variables made by individual Committee participants.1 These assessments, which are reported in the Summary of Economic Projections (SEP) that accompanies the FOMC minutes once a quarter, provide two types of information about forecast uncertainty. The first is qualitative in nature and summarizes the answers of participants to two questions: Is the uncertainty associated with his or her own projections of real activity and inflation higher, lower or about the same as the historical average? And are the risks to his or her own projections weighted to the upside, broadly balanced, or weighted to the downside? The second type of information is quantitative and provides the historical basis for answering the first qualitative question. Specifically, the SEP reports the root mean squared errors (RMSEs) of real-time forecasts over the past 20 years made by a group of leading private and public sector forecasters.

 

Some have blamed the entire economics profession.  Several attempts are being made to improve economic analysis.  Examples include work being done at

  • INET ( Institute for New Economic Thinking)
  • NAEC  at OECD

Heterodox schools of economics are making claims to accuracy of their approach after failure of main stream orthodox New Classical economics in predicting the Global Financial Crisis.

I will create another post later for some of these issues.

  • GDP forecasts errors have been attributed to errors in GDP components of business investments and exports.
  • Variability of GDP forecasts from short term to long term
  • Variability of GDP forecasts between forecasters – private and governments

 

Key Sources of Research:

 

The Case of Serial Disappointment

Justin‐Damien Guénette, Nicholas Labelle St‐Pierre, Martin Leduc and
Lori Rennison

Bank of Canada Staff Analytical Note 2016-10
July 2016

The Case of Serial Disappointment

 

ToTEM: The Bank of Canada’s New Quarterly Projection Model

Stephen Murchison and Andrew Rennison

Research Department
Bank of Canada

2006

ToTEM: The Bank of Canada’s New Quarterly Projection Model

 

 

ToTEM II: An Updated Version of the Bank of Canada’s Quarterly Projection Model

José Dorich, Michael Johnston, Rhys Mendes, Stephen Murchison and Yang Zhang

Canadian Economic Analysis Department
Bank of Canada

2013

ToTEM II: An Updated Version of the Bank of Canada’s Quarterly Projection Model

 

 

Introducing the Bank of Canada’s Projection Model for the Global Economy

Jeannine Bailliu, Patrick Blagrave, and James Rossiter

International Economic Analysis Department
Bank of Canada

2010

Introducing the Bank of Canada’s Projection Model for the Global Economy

Introducing the Bank of Canada’s Projection Model for the Global Economy

 

 

BoC-GEM: Modelling the World Economy

René Lalonde, International Economic Analysis Department

Dirk Muir, International Monetary Fund

BANK OF CANADA REVIEW SUMMER 2009

BoC-GEM: Modelling the World Economy

 

 

MUSE: The Bank of Canada’s New Projection Model of the U.S. Economy

Marc-André Gosselin and René Lalonde
International Department
Bank of Canada

2005

MUSE: The Bank of Canada’s New Projection Model of the U.S. Economy

 

 

OECD FORECASTS DURING AND AFTER THE FINANCIAL CRISIS: A POST MORTEM

OECD Economics Department
Policy Note no. 23
February 2014

OECD FORECASTS DURING AND AFTER THE FINANCIAL CRISIS: A POST MORTEM

 

 

THE USE OF MODELS IN PRODUCING OECD MACROECONOMIC FORECASTS

OECD ECONOMICS DEPARTMENT WORKING PAPERS NO. 1336
By David Turner

2016

THE USE OF MODELS IN PRODUCING OECD MACROECONOMIC FORECASTS

 

 

Lessons from OECD forecasts during and after the financial crisis

Christine Lewis and Nigel Pain

OECD Journal: Economic Studies
Volume 2014

Lessons from OECD forecasts during and after the financial crisis

 

 

How accurate are OECD forecasts?

12 February 2014
by Brian Keeley

 

Home Glossary About Contact Disclaimer How accurate are OECD forecasts?

 

 

Debate the Issues: Complexity and Policy making

OECD

Edited By:Patrick Love, Julia Stockdale-Otárola

06 June 2017

Debate the Issues: Complexity and Policy making

 

 

We need an empowering narrative

OECD Insights
23 June 2017
Gabriela Ramos

 

 

Final NAEC Synthesis : New Approaches to Economic Challenges

OECD

2015

Final NAEC Synthesis New Approaches to Economic Challenges

 

Debate the Issues: New Approaches to Economic Challenges

OECD

2016

Debate the Issues: New Approaches to Economic Challenges

 

OECD Forecasts During and After the Financial Crisis

A Post Mortem

Nigel Pain, Christine Lewis, Thai-Thanh Dang, Yosuke Jin, Pete Richardson

17 Mar 2014

OECD Forecasts During and After the Financial Crisis A Post Mortem

 

 Outlook for the Budget and the Economy

CBO USA

Outlook for the Budget and the Economy

 

CBO’s Economic Forecasting Record: 2015 Update

US CBO

CBO’s Economic Forecasting Record: 2015 Update

 

“Gauging the Uncertainty of the Economic
Outlook Using Historical Forecasting Errors: The Federal Reserve’s Approach,”

Finance and Economics Discussion Series 2017-020.

Washington: Board of Governors of the Federal Reserve System

“Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors: The Federal Reserve’s Approach

 

 The FRB/US Model: A Tool for Macroeconomic Policy Analysis

lint Brayton, Thomas Laubach, and David Reifschneider

2014

The FRB/US Model: A Tool for Macroeconomic Policy Analysis

 

 

 Gauging the Uncertainty of the Economic Outlook from Historical Forecasting Errors

David Reifschneider and Peter Tulip

federal Reserve

2007-60

Gauging the Uncertainty of the Economic Outlook from Historical Forecasting Errors

 

The IMF/WEO Forecast Process

Hans Genberg, Andrew Martinez, and Michael Salemi

IMF

2014

The IMF/WEO Forecast Process

 

 

On the Accuracy and Efficiency of IMF Forecasts: A Survey and Some Extensions

Hans Genberg and Andrew Martinez

IMF

2014

On the Accuracy and Efficiency of IMF Forecasts: A Survey and Some Extensions

 

 

An Evaluation of Commissioned Studies : Assessing the Accuracy of IMF Forecasts

Prepared by Charles Freedman
February 12, 2014

IMF

An Evaluation of Commissioned Studies : Assessing the Accuracy of IMF Forecasts

 

 

An Assessment of IMF Medium-Term Forecasts of GDP Growth

Carlos de Resende

2014

IMF

An Assessment of IMF Medium-Term Forecasts of GDP Growth

 

 

THE POLITICS OF IMF FORECASTS

AXEL DREHER
SILVIA MARCHESI
JAMES RAYMOND VREELAND

CESIFO WORKING PAPER NO. 2129

OCTOBER 2007

THE POLITICS OF IMF FORECASTS

 

 

Macroeconomic Forecasting: A Survey

K Wallis

1989

Macroeconomic Forecasting: A Survey

 

 

INTERNATIONAL ORGANISATIONS’ VS. PRIVATE ANALYSTS’ FORECASTS:

AN EVALUATION

Ildeberta Abreu

Banco de Portugal
July 2011

International organisations’ vs. private analysts’ forecasts: an evaluation

https://www.bportugal.pt/sites/default/files/anexos/papers/ab201105_e.pdf

 

 

On Macroeconomic Forecasting

Simon Wren-Lewis

2014

On Macroeconomic Forecasting

 

Why central banks use models to forecast

Simon Wren-Lewis

2014

Why central banks use models to forecast

USA Survey of Economic Professional Forecasters

ECB Survey of Economic Professional Forecasters

 

Trading Economics

 

Oxford Economics

 

Consensus Economics

IHS Markit

 

Evaluating forecast performance

Bank of England

Independent Evaluation Office | November 2015

Evaluating forecast performance

 

 

 Does the FederaL Reserve Staff Still beat Private Forecasters?

Makram El-Shagi, Sebastian Giesen and Alexander Jung

2014

 

DoeS THe FeDeraL reSerVe STaFF STiLL beaT PriVaTe ForeCaSTerS?

 

 

Modern Forecasting Models in Action: Improving Macroeconomic Analyses at
Central Banks

Malin Adolfson, Michael K. Andersson, Jesper Lind´e,
Mattias Villani, and Anders Vredina

Sveriges Riksbank
CEPR
Stockholm University

Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks

 

 

Updated Historical Forecast Errors (4/9/2014)

Federal Reserve

Updated Historical Forecast Errors (4/9/2014)

 

 

How good is the forecasting performance of major institutions?

Riksbank of Sweden

Monetary Policy Department.

How good is the forecasting performance of major institutions?

 

 

Best Economic Forecaster Awards

Focus Economics

Best Economic Forecaster Awards

 

Has Output Become More Predictable? Changes in Greenbook Forecast Accuracy

Federal Reserve

Has Output Become More Predictable? Changes in Greenbook Forecast Accuracy

 

Green Book

Federal Reserve Bank of Philadelphia

Green Book

 

US Federal Reserve Livingston Survey

Federal Reserve Bank of Philadelphia

Livingston Survey

 

 

The IMF and OECD versus Consensus Forecasts

by
Roy Batchelor
City University Business School, London
August 2000

The IMF and OECD versus Consensus Forecasts

 

 

CBO’s January 2017 Budget and Economic Outlook

CRFB

CBO’s January 2017 Budget and Economic Outlook

 

 

Growth Forecast Errors and Fiscal Multipliers

Prepared by Olivier Blanchard and Daniel Leigh

January 2013

IMF

http://www.nber.org/papers/w18779

Central Bank Macroeconomic Forecasting during the global Financial Crisis: the European Central Bank and Federal Reserve Bank of New York experiences

no 1688 / july 2014

 

 

Economic Forecasting and its Role in Making Monetary Policy

RB Australia

1999

 

https://www.rba.gov.au/publications/bulletin/1999/sep/pdf/bu-0999-1.pdf

 

Are Forecasting Models Usable for Policy Analysis?

1986

Chris Sims

Minneapolis Federal Reserve

 

https://www.minneapolisfed.org/research/QR/QR1011.pdf

 

Persistent Overoptimism about Economic Growth

BY KEVIN J. LANSING AND BENJAMIN PYLE

2015

 

http://www.frbsf.org/economic-research/files/el2015-03.pdf

 

 

Federal Reserve economic projections: What are they good for?

Ben S. Bernanke

Monday, November 28, 2016

https://www.brookings.edu/blog/ben-bernanke/2016/11/28/federal-reserve-economic-projections/

 

Reassessing Longer-Run U.S. Growth: How Low?

John G. Fernald
Federal Reserve Bank of San Francisco

August 2016

 

http://www.frbsf.org/economic-research/files/wp2016-18.pdf

 

 

Recent declines in the Fed’s longer-run economic projections

by Jonas D. M. Fisher,

Christopher Russo,

2017

https://www.chicagofed.org/publications/chicago-fed-letter/2017/375

 

 

“How Accurate Are Private Sector Forecasts? Cross-Country Evidence from Consensus Forecasts of Output Growth.”

Prakash Loungani

2000

IMF

https://www.imf.org/external/pubs/ft/wp/2000/wp0077.pdf

 

The Failure to Forecast the Great Recession

Simon Potter

 

Liberty Street Economics / New York Federal Reserve Bank

http://libertystreeteconomics.newyorkfed.org/2011/11/the-failure-to-forecast-the-great-recession.html

 

 

Social Learning, Strategic Incentives and Collective Wisdom: An Analysis of the Blue Chip Forecasting Group

J. Peter Ferderer Department of Economics Macalester College
St. Paul, MN 55105 ferderer@macalester.edu

Adam Freedman Chicago, IL 60601 freedman.adamj@gmail.com

July 22, 2015

 

http://muse.union.edu/lamacroworkshop2015/files/2015/01/34-Ferderer-Blue-Chip-Collective-Wisdom.pdf

 

 

CBO’s Economic Forecasting Record 2013 Update

 

https://www.cbo.gov/sites/default/files/113th-congress-2013-2014/reports/43846-ForecastingRecord.pdf

 

CBO’s Economic Forecasting Record 2015 Update

https://www.cbo.gov/sites/default/files/114th-congress-2015-2016/reports/49891-Forecasting_Record_2015.pdf

 

CBO Recurring reports

https://www.cbo.gov/about/products/major-recurring-reports#7

 

European Commission’s Forecasts Accuracy Revisited: Statistical Properties and Possible Causes of Forecast Errors

Marco Fioramanti, Laura González Cabanillas, Bjorn Roelstraete and Salvador Adrian Ferrandis Vallterra

2016

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2753854

 

 

Western Blue Chip Economic Forecast

Arizona State University

JPMorgan Chase Economic Outlook Center

https://research.wpcarey.asu.edu/economic-outlook/western-blue-chip/state-forecasts-archive-download?type=pdf&year=2017&month=08&state=Summary

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Low Interest Rates and Business Investments : Update August 2017

Low Interest Rates and Business Investments : Update August 2017

 

From  Explaining Low Investment Spending

USINVEST

globalinvest

 

Please see my earlier posts.

Business Investments and Low Interest Rates

Mergers and Acquisitions – Long Term Trends and Waves

The Decline in Long Term Real Interest Rates

Short term Thinking in Investment Decisions of Businesses and Financial Markets

Low Interest Rates and Monetary Policy Effectiveness

Low Interest Rates and Banks’ Profitability : Update July 2017

Low Interest Rates and Banks Profitability: Update – December 2016

 

Since my earlier posts on this subject there has been several new studies published highlighting weakness in business investments as one of the cause of slower economic growth and lower interest rates.

Other significant factors impacting interest rates are demographic changes, and slower economic growth.

I argue that there is mutual (circular) causality in weak business investment, slower economic growth, and lower interest rates which reinforce each other.

 

Decreased competition, increased concentration, corporate savings glut, share buybacks, paying dividends are also identified as factors.

Number of public companies have decreased significantly in USA since 1996 due to M&A activity.   See the data below.

Increased Mergers/Acquisitions, Increased Concentration, Decreased Competition, Decreased Number of Public Companies, Share buybacks, and Dividend Payouts are multiple perspectives of same problem.

 

From The Incredible Shrinking Universe of Stocks

The Causes and Consequences of Fewer U.S. Equities

USNUMUSSTAT

 

Key sources of Research:

The Low Level of Global Real Interest Rates

Remarks by
Stanley Fischer
Vice Chairman
Board of Governors of the Federal Reserve System

at the
Conference to Celebrate Arminio Fraga’s 60 Years
Casa das Garcas, Rio de Janeiro, Brazil

July 31, 2017

The Low Level of Global Real Interest Rates

 

 

INVESTMENT-LESS GROWTH: AN EMPIRICAL INVESTIGATION

German Gutierrez Thomas Philippon

Working Paper 22897

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue
Cambridge, MA 02138

December 2016

 

INVESTMENT-LESS GROWTH: AN EMPIRICAL INVESTIGATION

 

 

Explaining Low Investment Spending

The NBER Digest
NATIONAL BUREAU OF ECONOMIC RESEARCH

February 2017

Explaining Low Investment Spending

 

 

The Secular Stagnation of Investment?

Callum Jones and Thomas Philippon

December 2016

 

The Secular Stagnation of Investment?

 

 

Is there an investment gap in advanced economies? If so, why?

By Robin Dottling, German Gutierrez and Thomas Philippon

 

Is there an investment gap in advanced economies? If so, why?

 

 

The Disappointing Recovery of Output after 2009

JOHN G. FERNALD ROBERT E. HALL

JAMES H. STOCK MARK W. WATSON

May 2, 2017

The Disappointing Recovery of Output after 2009

 

 

Declining Competition and Investment in the U.S.

German Gutierrez and Thomas Philippon

NATIONAL BUREAU OF ECONOMIC RESEARCH

July 2017

 

Declining Competition and Investment in the U.S

 

 

Real Interest Rates Over the Long Run : Decline and convergence since the 1980s

Kei-Mu Yi   Jing Zhang

ECONOMIC POLICY PAPER 16-10 SEPTEMBER 2016

FEDERAL RESERVE BANK of MINNEAPOLIS

Real Interest Rates over the Long Run Decline and convergence since the 1980s, due significantly to factors causing lower investment demand

 

 

Understanding global trends in long-run real interest rates

Kei-Mu Yi and Jing Zhang

Economic Perspectives, Vol. 41, No. 2, 2017
Chicago Fed Reserve Bank

 

Understanding Global Trends in Long-run Real Interest Rates

 

 

Weakness in Investment Growth: Causes, Implications and Policy Responses

CAMA Working Paper 19/2017 March 2017

M. Ayhan Kose

Franziska Ohnsorge

Lei Sandy Ye

Ergys Islamaj

 

Weakness in Investment Growth: Causes, Implications and Policy Responses

 

 

Are US Industries Becoming More Concentrated?

Gustavo Grullon, Yelena Larkin and Roni Michaely

October 2016

 

Are US Industries Becoming More Concentrated?

 

 

Why Is Global Business Investment So Weak? Some Insights from Advanced Economies

 

Robert Fay, Justin-Damien Guénette, Martin Leduc and Louis Morel,

International Economic Analysis Department

Bank of Canada Review Spring 2017

 

Why Is Global Business Investment So Weak? Some Insights from Advanced Economies

 

 

What Is Behind the Weakness in Global Investment?

by Maxime Leboeuf and Bob Fay

2016

Bank of Canada

 

What Is Behind the Weakness in Global Investment?

 A Structural Interpretation of the Recent Weakness in Business Investment

by Russell Barnett and Rhys Mendes

 The Corporate Saving Glut in the Aftermath of the Global Financial Crisis

 

Gruber, Joseph W., and Steven B. Kamin

International Finance Discussion Papers
Board of Governors of the Federal Reserve System
Number 1150 October 2015

 

The Corporate Saving Glut in the Aftermath of the Global Financial Crisis

 

 

The Incredible Shrinking Universe of Stocks

The Causes and Consequences of Fewer U.S. Equities

March 22, 2017

GLOBAL FINANCIAL STRATEGIES

http://www.credit-suisse.com

 

The Incredible Shrinking Universe of Stocks The Causes and Consequences of Fewer U.S. Equities

 

 

They Just Get Bigger: How Corporate Mergers Strangle the Economy

Jordan Brennan

2017 February 19

They Just Get Bigger: How Corporate Mergers Strangle the Economy

 

 

Rising Corporate Concentration, Declining Trade Union Power, and the Growing Income Gap: American Prosperity in Historical Perspective

Jordan Brennan

March 2016

 

Rising Corporate Concentration, Declining Trade Union Power, and the Growing Income Gap: American Prosperity in Historical Perspective

 

 

The Oligarchy Economy: Concentrated Power, Income Inequality, and Slow Growth

Corporate concentration exacerbates income inequality

 

Jordan Brennan

March 2016

http://evonomics.com/the-oligarchy-economy/

Balance Sheets, Financial Interconnectedness, and Financial Stability – G20 Data Gaps Initiative

Balance Sheets, Financial Interconnectedness, and Financial Stability – G20 Data Gaps Initiative

 

From G-20 Data Gaps Initiative II: Meeting the Policy Challenge

In 2009, the G-20 Finance Ministers and Central Bank Governors (FMCBG) endorsed 20 recommendations to address data gaps revealed by the global financial crisis. The initiative, aimed at supporting enhanced policy analysis, is led by the Financial Stability Board (FSB) and the International Monetary Fund (IMF). The Inter-Agency Group on Economic and Financial Statistics (IAG)1 plays the global facilitator role to coordinate and monitor the implementation of the DGI recommendations.

The financial crisis which started in 2007 with problems in the U.S. subprime market, spread to the rest of the world becoming the most severe global crisis since the Great Depression. One difference between the global financial crisis and earlier post-war crises was that the crisis struck at the heart of the global financial system spreading throughout the global economy. This required global efforts for recovery. As one element of the global response, in October 2009, the G-20 Finance Ministers and Central Bank Governors (FMCBG) endorsed a DGI led by the Financial Stability Board (FSB) Secretariat and the IMF Staff. DGI was launched as an overarching initiative of 20 recommendations to address information gaps revealed by the global financial crisis.

Following the global financial crisis, in 2008, the G-20 leaders, at their meeting in Washington,9 committed to implement a fundamental reform of the global financial system to strengthen financial markets and regulatory regimes so as to avoid future crises.10 As part of the reform agenda, the FSB was established in April 2009 as the successor to the Financial Stability Forum (FSF) and started working as the central locus of coordination to take forward the financial reform program as developed by the relevant bodies. The obligations of members of the FSB were set to include agreeing to undergo periodic peer reviews, using among other inputs IMF/World Bank Financial Sector Assessment Program (FSAP) reports. The G-20 leaders noted the importance of global efforts in implementing the global regulatory reform so as to protect against adverse cross-border, regional and global developments affecting international financial stability.

The components of the G-20 regulatory reform agenda complement each other with an ultimate goal of strengthening the international financial system. The DGI has been an important element of this agenda as the regulatory reform agenda items mostly require better data. The collection of data on Global Systemically Important Banks’ (G-SIBs) exposures and funding dependencies is among the steps towards addressing the “too-big-to-fail” issue by reducing the probability and impact of G-SIBs’ failing. The FSB work on developing standards and processes for global data collection and aggregation on securities financing transactions aims to improve transparency in securitization towards the main goal of reducing risks related to the shadow banking system. Over-the-counter (OTC) derivatives markets including Credit Default Swap (CDS) were brought under greater scrutiny towards the main goal of making derivatives markets safer following the global crisis. DGI supported this goal by improving information in CDS markets. A number of other G-20 initiatives have strong links with the DGI project including the FSB work on strengthening the oversight and regulation of the shadow banking system; and on the work on global legal entity identifiers (LEI)11 which contribute to the robustness of the data frameworks with a more micro focus. The changing global regulatory reforms particularly the implementation of Basel III was also taken into consideration in the development of the DGI.

Surveillance Agenda

The importance of closing the data gaps hampering the surveillance of financial systems was also highlighted as part of the IMF’s 2014 Triennial Surveillance Review (TSR).12 The 2014 TSR emphasized that due to growing interconnectedness across borders, financial market shocks will continue to have significant spillovers via both capital flows and shifts in risk positions. Also, new dimensions to interconnectedness will continue to emerge such as through the potential short-run adverse spillovers generated by the financial regulatory reforms. To this end, the TSR recommended improving information on balance-sheets and enriching flow-of funds data. The IMF has overhauled its surveillance to make it more risk-based. To this end, the IMF Managing Director’s Action Plan for Strengthening Surveillance following the 2014 TSR13 underlined that the IMF will revive and adapt the Balance Sheet Approach (BSA) to facilitate a more in-depth analysis of the impact of shocks and their transmission across sectors, and possibly initiate the global flow of funds to better reflect global interconnections (Box 1). This work requires data from the DGI as it will help support the IMF’s macro-financial work including in the key exercises and reports (i.e., Early Warning Exercise, FSAP, and GFSR).

Global Flow of Funds

Through the use of internationally-agreed statistical standards, data on cross-border financial exposures (IBS, CPIS, and Coordinated Direct Investment Survey (CDIS)) can be linked with the domestic sectoral accounts data to build up a comprehensive picture of financial interconnections domestically and across borders, with a link back to the real economy through the sectoral accounts. This work is known as the “Global Flow of Funds (GFF).”14 The GFF project is mainly aimed at constructing a matrix that identifies interlinkages among domestic sectors and with counterpart countries (and possibly counterpart country sectors) to build up a picture of bilateral financial exposures and support analysis of potential sources of contagion. The concept of the GFF was first outlined in the Second Progress Report on the G-20 Data Gaps Initiative and initiated in 2013 as part of a broader IMF initiative aimed at strengthening the analysis of interconnectedness across borders, global liquidity flows and global financial interdependencies. In the longer term, the GFF matrix is intended to support regular monitoring of bilateral cross-border financial positions through a framework that highlight risks to national and international financial stability. IMF Staff is working towards developing a GFF matrix starting with the largest global economies.

 

How Does the DGI Address the Surveillance Agenda?

As noted above, in the wake of the 2014 TSR the IMF Managing Director published an Action Plan for Strengthening Surveillance. Among the actions to be taken was that “The Fund will revive and adapt the balance sheet approach to facilitate a more in-depth analysis of the impact of shocks and their transmission across sectors.” This responded to a call from outside experts David Li and Paul Tucker in their external study for the 2014 TSR on risks and spillovers.37

Sectoral Analysis

Even though the 2007/2008 crisis emerged in the financial sector, given its intermediary role, the problems in the financial sector also affected other sectors of an economy. To this end, analysis of balance sheet exposures is essential given the increasingly interconnected global economy. As it is pointed out in the IMF TSR 2014, the use of balance sheets to identify sources of vulnerability and the transmission of shocks, could have helped detect risks associated with European banks’ reliance on U.S. wholesale funding to finance structured products. In June 2015, the IMF set out the way forward in a paper for the IMF Executive Board on Balance Sheet Analysis in Surveillance. 38 Sectoral accounts and balance sheet data are essential, including from-whom to-whom data, in providing the context for an assessment of the links between the real economy and financial sectors. The sectoral balance sheets of the SNA is seen as the overarching framework for balance sheet analysis as the IMF Executive Board paper makes clear. Further, the paper sets out a data framework for such analysis.39 Putting the sectoral balance sheets of the SNA in a policy context, the IMF has developed a BSA, which compiles all the main balance sheets in an economy using aggregate data by sector. The BSA is based on the same conceptual principles as the sectoral accounts, providing information on a from-whom-to-whom basis with an additional focus on vulnerabilities arising from maturity and, currency mismatches as well as the capital structure of economic sectors.

While currently not that many economies compile from-whom-to-whom balance sheet data, BSA data can be compiled from the IMF’s Standardized Report Forms, IIP, and government balance sheet data—a more limited set of data than needed to compile the sectoral accounts. The DGI-2 recommendations address key data gaps that act as a constraint on a full-fledged balance sheet analysis. The DGI recommends addressing such gaps through improving G-20 economies’ dissemination of sectoral accounts and balance sheets building on 2008 SNA, including for the non-financial corporate and household sectors. (Annex 1, Recommendation II.8) Given the multifaceted character of the datasets, implementation of this recommendation is challenging and progress has been slow. However, all G-20 economies agree on the importance of having such information and have plans in place to make it happen.

Understanding Cross-border Financial Interconnections

The crisis emphasized the fact that it is not possible to isolate the problems in a single financial system as shocks propagate rapidly across the financial systems. Indeed, the IMF, since 2010, has been identifying jurisdictions with systemically important financial sectors based on a set of relevant and transparent criteria including size and interconnectedness. Within this identification framework, cross-border interconnectedness is considered an important complementary measure to the size of the economy: it captures the systemic risk that can arise through direct and indirect interlinkages among financial sectors in the global financial system (i.e., the risk that failure or malfunction of a national financial system may have severe repercussions on other countries or on overall systemic stability.48 The 2014 TSR summed up the issue succinctly in its Executive Summary: “Risks and spillovers remain first-order issues for the world economy and should be central to Fund surveillance. Recent reforms have made surveillance more risk-based, helping to better capture global interconnections. Experience so far also points to the need to build a deeper understanding of how risks map across countries, and how spillovers can quickly spread across sectors to expose domestic vulnerabilities.”49 Four existing datasets that include key information on cross-country financial linkages are the IIP, BIS IBS, IMF CPIS and IMF CDIS. Together these datasets provide a comprehensive picture of cross-border financial interconnections. This picture is especially relevant for policy makers as financial connections strengthen across border and domestic conditions are affected by financial developments in other economies to whom they are closely linked financially. DGI-2 focuses on improving the availability and cross-country comparability of these datasets (Annex1, Recommendations II.10, 11, 12 and 13). The well-known IIP is a key data source to understanding the linkages between the domestic economy and the rest of the world by providing information on both external assets and liabilities of the economy with a detailed instrument breakdown. However, the crisis revealed the need for currency and more detailed sector breakdowns, particularly for the other financial corporations (OFCs) sector. Consequently, as part of the DGI, the IIP was enhanced to support these policy needs. Significant progress has also been made in ensuring regular reporting of IIP along with the increase in frequency of reporting from annual to quarterly. By end-2015 virtually all G-20 economies reported quarterly IIP data. The IBS have been a key source of data for many decades providing information on aggregate assets and liabilities of internationally active banking systems on a quarterly frequency. The CPIS data, while on an annual frequency, provided significant insights into portfolio investment assets. That said, both datasets had limitations in terms of country coverage and granularity. CPIS also needed to be improved in terms of frequency and timeliness. To this end, the DGI supported the enhancements in these datasets.

 

Key Terms:

  • G-20 Data Gaps Initiative (DGI)
  • Financial Stability Board (FSB)
  • The Inter-Agency Group on Economic and Financial Statistics (IAG)
  • Finance Ministers and Central Bank Governors (FMCBG)
  • Financial Stability Forum (FSF)
  • Global Systemically Important Banks (G-SIBs)
  • Over-the-counter (OTC)
  • Credit Default Swap (CDS)
  • Global legal entity identifiers (LEI)
  • IMF Triennial Surveillance Review (TSR)
  • IMF Balance Sheet Approach (BSA)
  • IMF Global Flow of Funds (GFF)
  • IMF IIP (International Investment Positions)
  • BIS IBS (International Banking Statistics)
  • IMF CPIS (Coordinated Portfolio Investment Survey)
  • IMF CDIS (Coordinated Direct Investment Survey)
  • IMF GFSR ( Global Financial Stability Report)

 

Other Related Terms:

  • Global Systemically Important Financial Institutions (G-SIFIs )
  • GLOBAL SYSTEMICALLY IMPORTANT INSURERS (G-SIIS)
  • Systemically Important Financial Market Utilities (G-FMUs)
  • Nonbank Financial Companies (G-SINFC)
  • Financial Stability Oversight Council (FSOC)

     

The IAG members are

  • BIS (Bank of International Settlements)
  • G20 (Group of 20 Nations)
  • IMF (International Monetary Fund)
  • OECD (Organisation for Economic Co-operation and Development)
  • ECB (European Central Bank)
  • World Bank
  • Eurostat (European Statistics/Directorate-General of the European Commission)
  • UN (United Nations)

 

From G-20 Data Gaps Initiative II: Meeting the Policy Challenge

balancesheets

From G-20 Data Gaps Initiative II: Meeting the Policy Challenge

dgi

 

Progress of DGI ((DGI-I and DGI-II)

From G-20 Data Gaps Initiative II: Meeting the Policy Challenge

The first phase of the DGI was successfully concluded in September 2015 and the second phase of the initiative (DGI-2) was endorsed by the G-20 FMCBG. The key objective of the DGI-2 is to implement the regular collection and dissemination of comparable, timely, integrated, high quality, and standardized statistics for policy use. DGI-2 encompasses 20 new or revised recommendations, focused on datasets that support: (i) monitoring of risk in the financial sector; and (ii) analysis of vulnerabilities, interconnections and spillovers, not least cross-border.

Following the significant progress in closing some of the information gaps identified during the global financial crisis of 2007/08, the G-20 FMCBG endorsed, in September 2015, the closing of DGI-1. During the six-year implementation of DGI-1, significant achievements were obtained, particularly regarding the development of conceptual frameworks, as well as enhancements in some statistical collection and reporting. Regarding the latter, more work is needed for the implementation of some recommendations, especially in seven high-priority areas across G-20 economies, notably in government finance statistics and sectoral accounts and balance sheets.

In September 2015, the G-20 FMCBG also endorsed the launch of the second phase of the DGI. The main objective of DGI-2 is to implement the regular collection and dissemination of reliable and timely statistics for policy use. Its twenty recommendations are clustered under three main headings: (1) monitoring risk in the financial sector, (2) vulnerabilities, interconnections and spillovers, and (3) data sharing and communication of official statistics. The DGI-2 maintains the continuity with the DGI-1 recommendations while setting more specific objectives with the intention for the G-20 economies to compile and disseminate minimum common datasets for these recommendations. The DGI-2 also includes new recommendations to reflect the evolving users’ needs. Furthermore, the DGI-2 aims at strengthening the synergies with other relevant global initiatives.

The DGI-2 facilitates closing data gaps that are policy-relevant. By achieving its main objective, the DGI-2 will be instrumental in closing gaps in policy-relevant data. Most of the datasets covered by the DGI-2 are particularly relevant for meeting the emerging macro- financial policy needs, including the analysis of international positions, global liquidity, foreign currency exposures, and capital flows volatility.

The DGI-2 introduces action plans that set out specific “targets” for the implementation of its twenty recommendations through the five-year horizon of the initiative. The action plans acknowledge that countries may be at different stages of statistical development and take into account national priorities and resource constraints. The DGI-2 intends to bring the G-20 economies at higher common statistical standards through a coordinated effort; however, flexibility will be considered in terms of intermediate steps to achieve the targets based on national priorities, resource constraints, emerging data needs, and other considerations.

 

 

 

Key Sources of Research:

 

Second Phase of the G-20 Data Gaps Initiative (DGI-2) First Progress Report

 

Prepared by the Staff of the IMF and the FSB Secretariat September 2016

 

http://www.imf.org/external/np/g20/pdf/2016/090216.pdf

 

 

Sixth Progress Report on the Implementation of the G-20 Data Gaps Initiative

 

Prepared by the Staff of the IMF and the FSB Secretariat September 2015

 

http://www.fsb.org/wp-content/uploads/The-Financial-Crisis-and-Information-Gaps.pdf

 

 

Fifth Progress Report on the Implementation of the G-20 Data Gaps Initiative

 

Prepared by the Staff of the IMF and the FSB Secretariat September 2014

http://www.imf.org/external/np/g20/pdf/2014/5thprogressrep.pdf

 

 

Fourth Progress Report on the Implementation of the G-20 Data Gaps Initiative

 

Prepared by the Staff of the IMF and the FSB Secretariat September 2013

 

http://www.imf.org/external/np/G20/pdf/093013.pdf

 

 

 

Progress Report on the G-20 Data Gaps Initiative: Status, Action Plans, and Timetables

 

Prepared by the Staff of the IMF and the FSB Secretariat September 2012

http://www.imf.org/external/np/g20/pdf/093012.pdf

 

 

 

Implementation Progress Report

 

Prepared by the IMF Staff and the FSB Secretariat June 2011

http://www.imf.org/external/np/g20/pdf/063011.pdf

 

 

 

Progress Report Action Plans and Timetables

 

Prepared by the IMF Staff and the FSB Secretariat May 2010

 

http://www.imf.org/external/np/g20/pdf/053110.pdf

 

 

 

Report to the
G-20 Finance Ministers and Central Bank Governors

 

Prepared by the IMF Staff and the FSB Secretariat October 29, 2009

 

http://www.imf.org/external/np/g20/pdf/102909.pdf

 

 

 

G-20 Data Gaps Initiative II: Meeting the Policy Challenge

by Robert Heath and Evrim Bese Goksu

2016

https://www.imf.org/external/pubs/ft/wp/2016/wp1643.pdf

 

 

 

Why are the G-20 Data Gaps Initiative and the SDDS Plus Relevant for Financial Stability Analysis?

Robert Heath

http://www.imf.org/external/pubs/ft/wp/2013/wp1306.pdf

 

 

 

Toward the Development of Sectoral Financial Positions and Flows in a From-Whom-to-Whom Framework

Manik Shrestha

 

http://www.nber.org/chapters/c12835.pdf

 

 

An Integrated Framework for Financial Positions and Flows on a From-Whom-to- Whom Basis: Concepts, Status, and Prospects

Manik Shrestha, Reimund Mink, and Segismundo Fassler

 

https://www.imf.org/external/pubs/ft/wp/2012/wp1257.pdf

 

 

Financial investment and financing in a from-whom-to-whom framework

Mink, Reimund

https://www.bis.org/ifc/events/2011_dublin_61_01_mink.pdf

 

 

Users Conference on the Financial Crisis and Information Gaps

Conference co-hosted by The International Monetary Fund and The Financial Stability Board

2009

http://www.imf.org/external/np/seminars/eng/2009/usersconf/index.htm

 

 

A Status on the Availability of Sectoral Balance Sheets and Accumulation Accounts in Advanced Economies not Represented by Membership in the G-20

2011

 

https://www.imf.org/external/np/seminars/eng/2011/sta/pdf/g20a.pdf

 

 

A Status on the Availability of Sectoral Balance Sheets and Accumulation Accounts in G-20 Economies

2011

 

https://www.imf.org/external/np/seminars/eng/2011/sta/pdf/g20b.pdf

 

 

AN UPDATE ON THE IMF-OECD CONFERENCE ON STRENGTHENING SECTORAL POSITION AND FLOW DATA IN THE MACROECONOMIC ACCOUNTS

FEBRUARY 28 – MARCH 2, 2011

 

http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=COM/STD/DAF(2010)21&docLanguage=En

 

 

The Balance Sheet Approach:
Data Needs, Data at Hand, and Data Gaps (August 2009)

 

Alfredo Leone, Statistics Department, International Monetary Fund

 

https://www.czso.cz/staticke/conference2009/proceedings/data/quaterly_accounts/leone_paper.pdf

 

 

Development of financial sectoral accounts

New opportunities and challenges for supporting financial stability analysis

by Bruno Tissot

2016

 

http://www.bis.org/ifc/publ/ifcwork15.pdf

 

 

A Flow-of-Funds Perspective on the Financial Crisis Volume I: Money, Credit

edited by B. Winkler, A. van Riet, P. Bull, Ad van Riet

 

 

A Flow-of-Funds Perspective on the Financial Crisis Volume II: Macroeconomic

edited by B. Winkler, A. van Riet, P. Bull

 

 

Financial investment and financing in a from-whom-to-whom framework

Mink, Reimund

2011

http://2011.isiproceedings.org/papers/650287.pdf

 

 

Expanding the Integrated Macroeconomic Accounts’ Financial Sector

By Robert J. Kornfeld, Lisa Lynn, and Takashi Yamashita

2016

https://www.bea.gov/scb/pdf/2016/01%20January/0116_expanding_the_integrated_macroeconomic_accounts_financial_sector.pdf

 

 

Using the Balance Sheet Approach in Surveillance: Framework, Data Sources, and Data Availability

Johan Mathisen and Anthony Pellechio

2006

http://www.imf.org/external/pubs/ft/wp/2006/wp06100.pdf

 

 

Balance Sheet Analysis: A New Approach to Financial Stability

Surveillance

By Jean Christine A. Armas

2016

 

http://www.bsp.gov.ph/downloads/EcoNews/EN16-01.pdf

 

 

USING THE BALNCE SHEET APPROACH IN FINANCIAL STABILITY SURVEILLANCE:
Analyzing the Israeli economy’s resilience to exchange rate risk

 

http://www.suomenpankki.fi/en/tutkimus/konferenssit/konferenssit_tyopajat/Documents/JFS2007/JFS2007_HaimLevy_pres.pdf

http://www.boi.org.il/deptdata/stability/papers/dp0701e.pdf

 

 

 

A Balance Sheet Approach to Financial Crisis

Mark Allen, Christoph Rosenberg, Christian Keller, Brad Setser, and Nouriel Roubini

2002

https://www.imf.org/external/pubs/ft/wp/2002/wp02210.pdf

 

 

THE BALANCE SHEET APPROACH TO FINANCIAL CRISES IN EMERGING MARKETS

Giovanni Cozzi and
Jan Toporowski

2006

http://www.levyinstitute.org/pubs/wp_485.pdf

 

 

Balance-sheets. A financial/liability approach

Bo Bergman

2015

 

http://iariw.org/papers/2015/bergman_paper.pdf

 

 

Understanding Financial Crisis Through Accounting Models

Dirk J Bezemer

2009

http://www.uclm.es/actividades/2009/workshopESHET-UCLM/Bezemer_-_No_one_show_this_comming.pdf

 

 

 

Schumpeter Might Be Right Again: The Functional Differentiation of Credit

Dirk J. Bezemer
University of Groningen

https://www.rug.nl/staff/d.j.bezemer/the_functional_differentiation_of_credit.pdf

 

 

Causes of Financial Instability: Don’t Forget Finance

Dirk J. Bezemer

April 2011

 

http://www.levyinstitute.org/pubs/wp_665.pdf

 

 

THE ECONOMY AS A COMPLEX SYSTEM: THE BALANCE SHEET DIMENSION

DIRK J BEZEMER

2012

http://www.economicsofcreditanddebt.org/media/research/ACS_1250047_1st_Prf.pdf

 

 

Did Credit Decouple from Output in the Great Moderation?

Maria Grydaki and Dirk Bezemer

June 2013

https://mpra.ub.uni-muenchen.de/47424/1/MPRA_paper_47424.pdf

 

 

 

Towards an ‘accounting view’ on money, banking and the macroeconomy: history, empirics, theory

Dirk J. Bezemer

2016

http://www.economicsofcreditanddebt.org/media/research/Camb._J._Econ.-2016-Bezemer-1275-95.pdf

 

 

Modelling systemic financial sector and sovereign risk

Dale F. Gray anD anDreas a. Jobst

2011

 

http://www3.tcmb.gov.tr/konferanslar/fsr/Gray_2.pdf

 

 

BALANCE SHEET ANALYSIS IN FUND SURVEILLANCE

2015

https://www.imf.org/external/np/pp/eng/2015/061215.pdf

https://www.imf.org/external/np/pp/eng/2015/071315.pdf

 

 

The role of external balance sheets in the financial crisis

Yaser Al-Saffar, Wolfgang Ridinger and Simon Whitaker

2013

 

http://www.bankofengland.co.uk/financialstability/Documents/fpc/fspapers/fs_paper24.pdf

 

 

Global Conferences on DGI

June 2, 2016

http://www.imf.org/external/np/seminars/eng/dgi/

 

 

CAPITAL FLOWS AND GLOBAL LIQUIDITY

IMF Note for G20 IFA WG

February 2016

 

http://g20chn.org/English/Documents/Current/201608/P020160811536051676178.pdf

 

 

Introduction to Balance of Payments and International Investment Position Manual, 6th Edition and BPM6 Compilation Guide

http://www.imfmetac.org/Upload/Link3_766_105.pdf

 

 

Introduction: ‘cranks’ and ‘brave heretics’: rethinking money and banking after the Great Financial Crisis

Geoffrey Ingham Ken Coutts Sue Konzelmann

Camb J Econ (2016) 40 (5): 1247-1257.

 

 

Network Analysis of Sectoral Accounts: Identifying Sectoral Interlinkages in G-4 Economies

by Luiza Antoun de Almeida

2016

https://www.imf.org/external/pubs/ft/wp/2015/wp15111.pdf

 

 

2014 TRIENNIAL SURVEILLANCE REVIEW—EXTERNAL STUDY—RISKS AND SPILLOVERS

Prepared By David Daokui Li and Paul Tucker

 

https://www.imf.org/external/np/pp/eng/2014/073014e.pdf

https://www.imf.org/external/pubs/ft/bop/2014/pdf/14-10.pdf

 

 

 

2014 TRIENNIAL SURVEILLANCE REVIEW—OVERVIEW PAPER

 

http://www.imf.org/external/np/pp/eng/2014/073014.pdf

http://www.imf.org/external/np/spr/triennial/2014/

 

 

Measuring Global Flow of Funds and Integrating Real and Financial Accounts: Concepts, Data Sources and Approaches

Nan Zhang (Stanford University)

2015

http://iariw.org/papers/2015/zhang.pdf

 

 

Cross-border financial linkages: Identifying and measuring vulnerabilities

 

Philip R. Lane

2014

 

http://cepr.org/sites/default/files/policy_insights/PolicyInsight77.pdf

 

 

Global Flow of Funds: Mapping Bilateral Geographic Flows

Authors1: Luca Errico, Richard Walton, Alicia Hierro, Hanan AbuShanab, Goran Amidzic

 

2013

http://2013.isiproceedings.org/Files/STS083-P1-S.pdf

 

Global-Flow-of-Funds Analysis in a Theoretical Model -What Happened in China’s External Flow of Funds –

 

Nan Zhang

 

http://ns1.shudo-u.ac.jp/~zhang/08GFOF.pdf

 

 

Mapping the Shadow Banking System through a Global Flow of Funds Analysis

Hyun Song Shin

Princeton University

https://www.imf.org/external/np/seminars/eng/2013/sta/forum/pdf/Hyun-Song-Shin2.pdf

 

 

The Composition of the Global Flow of Funds in East Asia

 

Nan Zhang

 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.534.757&rep=rep1&type=pdf

 

 

What Has Capital Flow Liberalization Meant for Economic and Financial Statistics?

Robert Heath

2015

https://pdfs.semanticscholar.org/48dd/41aac8864e53b6176f7b3b7df22aba05ac0e.pdf

 

 

Global flows in a digital age: How trade, finance, people, and data connect the world economy

McKinsey & Company Report

2014

 

 

Managing global finance as a system

Speech given by

Andrew G Haldane, Chief Economist, Bank of England

At the Maxwell Fry Annual Global Finance Lecture, Birmingham University 29 October 2014

http://www.bankofengland.co.uk/publications/Documents/speeches/2014/speech772.pdf

A Brief History of Macro-Economic Modeling, Forecasting, and Policy Analysis

A Brief History of Macro-Economic Modeling, Forecasting, and Policy Analysis

 

From A History of Macroeconomics from Keynes to Lucas and Beyond

history-of-macro

 

From Modern Macroeconomic Models as Tools for Economic Policy

I believe that during the last financial crisis, macroeconomists (and I include myself among them) failed the country, and indeed the world. In September 2008, central bankers were in desperate need of a playbook that offered a systematic plan of attack to deal with fast- evolving circumstances. Macroeconomics should have been able to provide that playbook. It could not. Of course, from a longer view, macroeconomists let policymakers down much earlier, because they did not provide policymakers with rules to avoid the circumstances that led to the global financial meltdown.

Because of this failure, macroeconomics and its practitioners have received a great deal of pointed criticism both during and after the crisis. Some of this criticism has come from policymakers and the media, but much has come from other economists. Of course, macroeconomists have responded with considerable vigor, but the overall debate inevitably leads the general public to wonder: What is the value and applicability of macroeconomics as currently practiced?

 

There have been several criticisms of Main stream Economic Modeling from economists such as

  • Paul Romer
  • Willem H Buiter
  • Paul Krugman
  • R Cabellero
  • William White
  • Dirk Bezemer
  • Steve Keen
  • Jay Forrester
  • Lavoie and Godley

 

Issues with Neo Classical Models

  • No role of Money, Credit  and Finance
  • Lack of Interaction between Real and Financial sectors
  • Lack of Aggregate Demand
  • Rational Expectations and others.

 

Orthodox and Heterodox Modeling

  • Input Output Equations Models – Inter Industry Analysis
  • Structural Models
  • CGE and DSGE Models
  • VAR ( Vector Auto Regression ) Models
  • Stock flow Consistent Models
  • System Dynamics models

 

Neoclassical Models

  • Structural
  • VAR after Lucas Critique
  • DSGE (Dynamic Stochastic General Equilibrium Models)
  • DSGE – VAR

 

From HISTORY OF MACROECONOMETRIC MODELLING: LESSONS FROM PAST EXPERIENCE

The origin of macroeconometric modelling dates back to after World War II when Marschak organised a special team at the Cowles Commission by inviting luminaries such as Tjalling Koopmans, Kenneth Arrow, Trygve Haavelmo, T.W. Anderson, Lawrence Klein, G. Debreu, Leonid Hurwitz, Harry Markowitz, and Franco Modigliani (Diebold, 1998).

An interesting feature of macro modelling in this group was that there were three divisions to undertake the modelling procedures: first, economic theory or model specification; second, statistical inference (including model estimation, diagnostic tests and applications); and third, model construction which was dealing with data preparation and computations. The use of a team approach in macroeconometric modelling has been regarded as both cause and effect of large scale macroeconometric modelling (Intriligator, Bodkin and Hsiao, 1996).

Klein joined this team and conducted his first attempt in the mid 1940s to build a MEM for the US economy. See Klein (1983), Bodkin, Klein and Marwah (1991) and Intriligator, Bodkin and Hsiao (1996) for discussions of the MEMs which have been constructed for developed countries such as

  • the Klein interwar model,
  • the Klein-Goldberger model,
  • the Wharton model,
  • the DRI (Data Resources. Inc.) model,
  • the CANDIDE model,
  • the Brooking model etc.

 

 

History of Early Models

A. Klein Interwar Model

  • MODEL I
  • MODEL II
  • MODEL III
  • Developed in late 1940s

B. Klein -Goldberger Model

  • Developed at University of Michigan in 1950s.  Annual forecasts

C. BEA Model

  • Developed by L Klein.  Quarterly.  Operational in 1961. Transferred to BEA.  Eventually became BEA model.

D. Wharton Model

  • WHAR – III, with Anticipations
  • WHAR – MARK III
  • WHAR -ANNUAL
  • WEFA,  Project LINK
  • Wharton models were constantly operated until 2001.  DRI and WEFA merged to form Global Insight, Inc.

E. DRI Model

  • Built in 1969.  by Data Resources inc.  by Eckstein, Fromm, and Duessenbury.

F. Brookings Model

  • Developed by L Klein and J.S. Duessenberry. .  Quarterly.

G. MPS Model

  • FRB-MIT Model

H. The Hickman – Coen Model

  • Developed by Hickman and Coen for long term forecasting

I. FAIR model

  • Developed by Ray Fair at Princeton.  Now at Yale.  Available for free.

J. The St. Louis Model

  • Developed by FRB/ST.Louis

K. Michigan MQEM Model

  • Quarterly. DHL III

L. The Liu-HWA Model

  • Developed in 1970s.  Monthly.

M. WEFA -DRI/ Global Insight Model

  • Developed after merger of WEFA and DRI in 2001

N. Michigan MQEM /RSQE Model

  • Developed and extended in 1990s.  Replaced by Hymans RSQE model.

O. Current Quarterly Model

  • L Klein and Global Insight collaboration. L Klein died in 2013.

P. CANDIDE Model

  • Model developed for Canada

 

 

From Economic Theory, Model Size, and Model Purpose

models-7

 

 

From HISTORY OF MACROECONOMETRIC MODELLING: LESSONS FROM PAST EXPERIENCE

A Macro Econometric Model (MEM) is a set of behavioural equations, as well as institutional and definitional relationships representing the main behaviours of economic agents and the operations of an economy. The equations, or behavioural relations, can be empirically validated to capture the structure of a macroeconomy, and can then be used to simulate the effects of policy changes.

Macroeconometric modelling is multi- dimensional and both a science and an art. Bautista (1988) and Capros, Karadeloglou and Mentzas (1990) have classified macroeconomic models into broad groups: MEMS and CGE (computable general equilibrium) models.

Further, according to Challen and Hagger (1983, pp.2-22) there are five varieties of MEMs in the literature:

  • the KK (Keynes- Klein) model,
  • the PB (Phillips-Bergstrom) model,
  • the WJ (Walras-Johansen) model,
  • the WL (Walras-Leontief) model,
  • the MS (Muth-Sargent) model.

The KK model is mainly used by model builders in developing countries to explain the Keynesian demand-oriented model of macroeconomic fluctuations. They deal with the problems of short-run instability of output and employment using mainly stabilisation policies. The basic Keynesian model has been criticised as it does not consider the supply side and the incorporation of production relations. Furthermore, this modelling approach does not adequately capture the role of the money market, relative prices and expectations. As a response to the shortcomings associated with the KK model, the St Louis model was constructed by the monetarist critics (Anderson and Carlson, 1970) in order to highlight the undeniable impacts of money on the real variables in the economy.

The second type of MEM, the PB, emerged in the literature when Phillips (1954, 1957) used both the Keynesian and the Neoclassical theories within a dynamic and continuous time model to analyse stabilisation policy. Although the PB model is also a demand-oriented model, differential or difference equations are used to estimate its stochastic structural parameters. In essence, the steady state and asymptotic properties of models are thus examined in a continuous time framework. One should note that this modelling method in practice becomes onerous to implement especially for large scale models.

The third type of MEM, the WJ, can be referred to as a multi-sector model in which the economy is disaggregated into various interdependent markets, each reaching an equilibrium state by the profit maximising behaviour of producers and utility maximising actions of consumers in competitive markets. Similar to an input-output (IO) approach, different sectors in the WJ model are linked together via their purchases and sales from, and to, each other. However, it is different from an IO model as it is highly non-linear and uses logarithmic differentiation.

The fourth type of MEMs, known as the WL model, has been widely considered as the more relevant MEM for developing countries (Challen and Hagger, 1983). The WL model incorporates an IO table into the Walrasian general equilibrium system, enabling analysts to obtain the sectoral output, value added or employment given the values of the sectoral or aggregate final demand components.

Finally, the foundations of the MS model are based on the evolution of the theory of rational expectations. The MS model is similar to the KK model in that they both are dynamic, non-linear, stochastic and discrete. But in this model the formation of expectations is no longer a function of previous values of dependent variables. The forward looking expectation variables can be obtained only through solving the complete model. The New Classical School demonstrated the role of the supply side and expectations in a MEM with the aim of highlighting the inadequacy of demand management policies. To this end, Sargent (1976) formulated forward-looking variants of this model which suggest no trade-off between inflation and unemployment in the short term, which is in sharp contrast to both the Keynesian and Monetarist modelling perspectives.

It is noteworthy that the subsequent advances in the WJ and WL models led to the formulation of CGE modelling, which is categorised here as the second type of macroeconomic model. The Neoclassical CGE models are based on the optimising behaviour of economic agents. The main objectives of CGE models are to conduct policy analysis on resource economics, international trade, efficient sectoral production and income distribution (Capros, Karadeloglou and Mentzas, 1990).

The 1960s witnessed the flowering of the large scale macroeconometric modelling. This decade saw the construction of the Brookings model, in which an input-output table was incorporated into the model. Adopting the team approach in modelling procedure in the 1970s, the majority of model builders aimed at the commercialisation of the comprehensive macro models, such as DRI, Wharton and Chase, by providing information to private enterprises. Modellers designed their models on the basis of quarterly or monthly data with the goal of keeping the models up-to-date, for commercial gain. As a consequence of taking such measures, model-builders became commercially successful (Fair, 1987). It is believed that in this era, the full-grown models “would contribute substantively to enlarging our understanding of economic processes and to solving real- world economic problems” (Sowey and Hargreaves, 1991: 600).

During the last three decades, MEMs have been internationalised via Project LINK which was first operated at the University of Pennsylvania. In 1987 according to Bodkin (1988b) Project LINK consisted of 79 MEMs of individual countries or aggregations. In Project LINK the world is treated as a closed system of approximately 20,000 equations which “allow trade, capita flows, and possible exchange rate and other repercussions to influence systematically the individual national economies” (Bodkin, 1988b: 222).

 

From STRUCTURAL ECONOMETRIC MODELLING: METHODOLOGY AND TOOLS WITH APPLICATIONS UNDER EVIEWS

Since an early date in the twentieth century, economists have tried to produce mathematical tools which, applied to a given practical problem, formalized a given economic theory to produce a reliable numerical picture. The most natural application is of course to forecast the future, and indeed this goal was present from the first. But one can also consider learning the consequences of an unforeseen event, or measuring the efficiency of a change in the present policy, or even improving the understanding of a set of mechanisms too complex to be grasped by the human mind.

In the last decades, three kinds of tools of this type have emerged, which share the present modelling market.

  •   The “VAR” models. They try to give the most reliable image of the near future, using a complex estimated structure of lagged elements, based essentially on the statistical quality, although economic theory can be introduced, mostly through constraints on the specifications. The main use of this tool is to produce short term assessments.
  •   The Computable General Equilibrium models. They use a detailed structure with a priori formulations and calibrated coefficients to solve a generally local problem, through the application of one or several optimizing behaviors. The issues typically addressed are optimizing resource allocations, or describing the consequences of trade agreements. The mechanisms described contain generally little dynamics.

This is no longer true for the Dynamic Stochastic General Equilibrium models, which dominate the current field. They include dynamic behaviors and take into account the uncertainty in economic evolutions. Compared to the traditional models (see later) they formalize explicitly the optimizing equilibria, based on the aggregated behavior of individual agents. This means that they allow agents to adapt their behavior to changes is the rules governing the behaviors of others, including the State, in principle escaping the Lucas critique. As the model does not rely on traditional estimated equations, calibration is required for most parameters.

  •  The “structural” models. They start from a given economic framework, defining the behaviors of the individual agents according to some globally consistent economic theory. They use the available data to associate to these behaviors reliable formulas, which are linked by identities guaranteeing the consistency of the whole set. These models can be placed halfway between the two above categories: they do rely on statistics, and also on theory. To accept a formula, it must respect both types of criteria.

The use of this last kind of models, which occupied the whole field at the beginning, is now restricted to policy analysis and medium term forecasting. For the latter, they show huge advantages: the full theoretical formulations provide a clear and understandable picture, including the measurement of individual influences. They allow also to introduce stability constraints leading to identified long term equilibriums, and to separate this equilibrium from the dynamic fluctuations which lead to it.

Compared to CGEs and DSGEs, optimization behaviors are present (as we shall see later) and introduced in the estimated equations. But they are frozen there, in a state associated with a period, and the behavior of other agents at the time. If these conditions do not change, the statistical validation is an important advantage. But sensitivity to shocks is flawed, in a way which is difficult to measure.

 

From Macroeconomic Modeling in the Policy Process: A Review of Tools Used at the Federal Reserve Board and Their Relation to Ongoing Research

models-1models-2models-3

 

From Macroeconomic Modeling in the Policy Process: A Review of Tools Used at the Federal Reserve Board and Their Relation to Ongoing Research

models-4

 

USA Central Bank Models

A. FRB Models (Neo Classical)

  • MPS ( MIT-PENN-FRB)
  • FRB/US (since 1996)
  • FRB/MCM
  • FRB/WORLD
  • FRB/EDO
  • SIGMA
  • VAR Models
  • Accelerator Models

B.  FRB/NY DSGE Model

C.  FRB/Chicago DSGE Model

D. FRB/Philadelphia DSGE Model – PRISM

 

 

Newer Central Bank Models

From Macroeconomic Models for Monetary Policies: A Critical Review from a Finance Perspective

There has been a remarkable evolution of macroeconomic models used for monetary policy at major central banks around the world, in aspects such as model formulation, solution methods, estimation approaches, and importantly, communication of results between central banks. Central banks have developed many different classes and variants of macroeconomic models in the hopes of producing a reliable and comprehensive analysis of monetary policy. Early types of models included quantitative macroeconomic models1, reduced-form statistical models, structural vector autore- gressive models, and large-scale macroeconometric models, a hybrid form combining the long-run structural relationships implied by a partial equilibrium treatment of theory (e.g., the decision rule for aggregate consumption) and reduced-form short-run relationships employing error-correcting equations.

Over the past 20 years in particular, there have been significant advances in the specification and estimation for New Keynesian Dynamic Stochastic General Equilibrium (New Keynesian DSGE) models. Significant progress has been made to advance policymaking models from the older static and qualitative New Keynesian style of modeling to the New Keynesian DSGE paradigm. The New Keynesian DSGE model is designed to capture real world data within a tightly structured and self-consistent macroeconomic model. The New Keynesian DSGE model has explicitly theoretical foundations, allowing it to circumvent the Sims Critique (see Sims, 1980) and the Lucas Critique (see Lucas, 1976), and therefore it can provide more reliable monetary policy analysis than earlier models. A consensus baseline New Keynesian DSGE model has emerged, one that is heavily influenced by estimated impulse response functions based on Structural Vector Autoregression (SVAR) models. In particular, a baseline New Keynesian DSGE model has recently been shown by Christiano et al. (2005) to successfully account for the effects of a monetary policy shock with nominal and real rigidities. Similarly, Smets and Wouters (2003, 2007) show that a baseline New Keynesian DSGE model can track and forecast time series as well as, if not better than, a Bayesian vector autoregressive (BVAR) model. New Keynesian DSGE models have been developed at many central banks, becoming a crucial part of many of their core models.2 Sbordone et al. (2010) have emphasized that an advantage of New Keynesian DSGE models is that they share core assumptions about the behavior of agents, making them scalable to relevant details to address the policy question at hand. For example, Smets and Wouters (2007) introduced wage stickiness and investment frictions into their model, Gertler et al. (2008) incorporated labor market search and wage bargaining, and Bernanke et al. (1999), Chari et al. (1995) and Christiano et al. (2008) studied the interaction between the financial sector and macroeconomic activity.

The devastating aftermath of the financial crisis and the Great Recession has prompted a rethink of monetary policy and central banking. Central bank monetary policy models face new challenges. Many macroeconomists (and in fact, many of the world’s leading thinkers) have called for a new generation of DSGE models. The first and foremost critique of the current state of the art of New Keynesian DSGE models is that these models lack an appropriate financial sector with a realistic interbank market, and as a result, the models fail to fully account for an important source of aggregate fluctuations, such as systemic risk. Second, the linkage between the endogenous risk premium and macroeconomic activity is crucial for policymakers to understand the transmission mechanism of monetary policy, especially in financially stressed periods. In models that lack a coherent endogenous risk premium, policy experiments become unreliable in stressed periods, and the model cannot provide a consistent framework for conducting experimental stress tests regarding financial stability or macroprudential policy. Third, heterogeneity among the players in the economy is essential to our understanding of inefficient allocations and flows between agents. These inefficiencies have an extremely important effect on the equilibrium state of the economy. Without reasonable heterogeneity among agents in models, there is no way to infer the distributional effects of monetary policy.

Finally, and perhaps most importantly in terms of government policy, a new generation of models is in strong demand to provide policymakers a unified and coherent framework for both conventional and unconventional monetary policies. For example, at the onset of the financial crisis, the zero lower bound went from a remote possibility to reality with frightening speed. This led central banks to quickly develop unconventional measures to provide stimulus, including credit easing, quantitative easing and extraordinary forward guidance. These unconventional measures demanded a proper platform to be analyzed. Furthermore, these unconventional monetary policies have blurred the boundary between monetary policy and fiscal policy. Through these policies, central banks gave preference to some debtors over others (e.g. industrial companies, mortgage banks, governments), and some sectors over others (e.g. export versus domestic). In turn, the distributional effects of monetary policy were much stronger than in normal times. As a result, these measures are sometimes referred to as quasi-fiscal policy. As Sims emphasized, a reliable monetary policy experiment cannot ignore the effect of ongoing fiscal policy. In order to implement unconventional measures during the crisis, central banks put much more risk onto government balance sheets than ever before, which had the potential to lead to substantial losses. Thus the government balance sheets in these models should be forward-looking, and its risk characteristics are crucial to the success of the model. 

 

 

Other Central Banks Models

From Macro-Econometric System Modelling @75

A fourth generation of models has arisen in the early 2000s. Representatives are TOTEM (Bank of Canada, Murchinson and Rennison, 2006), MAS (the Modelling and Simulation model of the Bank of Chile, Medina and Soto, 2005), GEM (the Global Economic Model of the IMF, Laxton and Pesenti, 2003), BEQM (Bank of England Quarterly Model, Harrison et al, 2004), NEMO (Norwegian Economic Model at the Bank of Norway, Brubakk et al, 2006), The New Area Wide Model at the European Central Bank, Kai et al, 2008), the RAMSES model at the Riksbank (Adolfson et al, 2007), AINO at the Bank of Finland (Kuismanen et al, 2003), SIGMA (Erceg et al, 2006) at the U.S. Federal Reserve, and KITT (Kiwi Inflation Targeting Technology) at the Reserve Bank of New Zealand, Beneˇs et al, 2009.

From Macroeconomic Models for Monetary Policies: A Critical Review from a Finance Perspective

  • the Bank of Canada (QPM, ToTEM),
  • the Bank of England (MTMM, BEQM),
  • the Central Bank of Chile (MAS),
  • the Central Reserve Bank of Peru (MEGA-D),
  • the European Central Bank (NAWM, CMR),
  • the Norges Bank (NEMO),
  • the Sveriges Riksbank (RAMSES),
  • the US Federal Reserve (SIGMA, EDO),
  • the Central Bank of Brazil,
  • the Central Bank of Spain,
  • the Reserve Bank of New Zealand,
  • the Bank of Finland,
  • and IMF (GEM, GFM and GIMF).

In particular, the Bank of Canada, the Bank of England, the Central Bank of Chile, the Central European Bank, the Norges Bank, the Sveriges Rikbank, and the U.S. Federal Reserve have incorporated New Keynesian DSGE models into their core models.

 

 

Other Institutions Models

  • USA CBO (Congressional Budget Office)
  • USA OMB ( Office of Management and Budget)
  • USA Department of Energy – EIA Models
  • USA Bureau of Economic Analysis (BEA) Model
  • University of Michigan RSQE Model
  • World Bank
  • UN
  • IMF
  • OECD
  • FAIR US and MC Model at Yale University

 

Other Governmental Agencies Models

  • PITM Model
  • MATH Model
  • KGB Model
  • TRIM Model
  • Claremont Model

 

Private Sector Forecasting Models

  • The Conference Board
  • Wells Fargo
  • JP Morgan
  • Citi
  • Oxford Economics
  • Moody’s Analytics
  • IHS Inc./Global Insight

 

Old Non Governmental Models

  • DRI (Data Resources Inc.)
  • Chase Econometrics
  • Wharton Econometrics

They all merged into an entity IHS, Inc.

In 1987 Wharton Econometric Forecasting Associates (WEFA) merged with Chase Econometrics, a competitor to DRI and WEFA,[13] and in 2001 DRI merged with WEFA to form Global Insight.[14][15] In 2008 Global Insight was bought by IHS Inc., thus inheriting 50 years of experience and more than 200 full-time economists, country risk analysts, and consultants. [16]

 

The following book is a good resource for Lists of Models used in various countries.

  • Macroeconometric Models By Władysław Welfe

 

 

Heterodox Models

 

  • System Dynamics Models
  • Stock Flow Consistent Models
  • Flow of Funds Models
  • Agent based Computational Models
  • Network Economics Approaches

 

From Can Disequilibrium Macroeconomic Models Be Used to Anticipate Financial Instability? A Case Study

Two other approaches to modeling the macroeconomy are flow-of-funds models and stock-flow consistent models, and a fourth is agent-based models. All trace unfolding processes rather than equilibrium snapshots, and are so evolutionary. SFC models also differ from DSGE models in that they aim to be financially complete (but obviously stylized) representations of the economy.

 

Please see my other posts on Heterodox Modeling.

Increasing Returns, Path Dependence, Circular and Cumulative Causation in Economics

Jay W. Forrester and System Dynamics

Micro Motives, Macro Behavior: Agent Based Modeling in Economics

Stock-Flow Consistent Modeling

Foundations of Balance Sheet Economics

Contagion in Financial (Balance sheets) Networks

 

 

Key People:

  • Jan Tinbergen
  • Lawrance Klein
  • Wassily Leontief
  • Tjalling Koopmans
  • Franco Modigliani
  • Kenneth Arrow
  • Trygve Haavelmo
  • T.W. Anderson
  • G. Debreu
  • Leonid Hurwitz
  • Harry Markowitz
Key Sources of Research:

 

 

Macroeconomic Models, Forecasting, and Policymaking

Andrea Pescatori and Saeed Zaman

http://www.relooney.com/NS3040/0_New_14947.pdf

 

 

The Evolution of Macro Models at the Federal Reserve Board

􏰃Flint Brayton, Andrew Levin, Ralph Tryon, and John C. Williams

Revised: February 7, 1997

https://www.federalreserve.gov/pubs/feds/1997/199729/199729pap.pdf

 

 

A Guide to FRB/US

A Macroeconomic Model of the United States

Macroeconomic and Quantitative Studies 􏰂 Division of Research and Statistics Federal Reserve Board Washington, D.C. 20551

version 1.0, October 1996

https://www.federalreserve.gov/pubs/feds/1996/199642/199642pap.pdf

 

 

The FRB/US Model: A Tool for Macroeconomic Policy Analysis

Flint Brayton, Thomas Laubach, and David Reifschneider

2014

https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/a-tool-for-macroeconomic-policy-analysis.html

https://www.federalreserve.gov/econresdata/frbus/us-models-package.htm

https://www.federalreserve.gov/econresdata/frbus/us-documentation-papers.htm

https://www.federalreserve.gov/econresdata/frbus/us-technical-qas.htm

https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/november-2014-update-of-the-frbus-model-20141121.html

 

 

Estimated Dynamic Optimization (EDO) Model

https://www.federalreserve.gov/econresdata/edo/edo-models-about.htm

https://www.federalreserve.gov/econresdata/edo/edo-model-package.htm

https://www.federalreserve.gov/econresdata/edo/edo-documentation-papers.htm

 

 

The FRBNY DSGE Model

Marco Del Negro Stefano Eusepi Marc Giannoni Argia Sbordone Andrea Tambalotti Matthew Cocci Raiden Hasegawa M. Henry Linder

 

2013

https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr647.pdf

 

 

Can Disequilibrium Macroeconomic Models Be Used to Anticipate Financial Instability?

A Case Study

Dirk J. Bezemer

 

https://pdfs.semanticscholar.org/224f/a6d8daa2716892ed0984f8aa0882c6dccefc.pdf

 

 

Central Bank Models:Lessons from the Past and Ideas for the Future

John B. Taylor

November 2016

http://web.stanford.edu/~johntayl/2016_pdfs/Text_Keynote_BoC_Workshop_Taylor-2016.pdf

 

 

DSGE models and central banks

by Camilo E Tovar

2008

http://www.bis.org/publ/work258.pdf

 

 

Macro-Finance Models of Interest Rates and the Economy

Glenn D. Rudebusch∗
Federal Reserve Bank of San Francisco

https://pdfs.semanticscholar.org/6b60/3c8c75a3daa52749dd4ade71f9ae1642f9aa.pdf

 

 

Panel Discussion on Uses of Models at Central Banks

ECB Workshop on DSGE Models and Forecasting September 23, 2016

John Roberts

 

https://www.ecb.europa.eu/pub/conferences/shared/pdf/20160922_dsge/Roberts_Panel_discussion.pdf

 

 

The Chicago Fed DSGE Model

Scott A. Brave Jerey R. Campbell  Jonas D.M. Fisher  Alejandro Justiniano

August 16, 2012

https://www.chicagofed.org/publications/working-papers/2012/wp-02

 

 

Macroeconomics and consumption: Why central bank models failed and how to repair them

John Muellbauer

21 December 2016

http://voxeu.org/article/why-central-bank-models-failed-and-how-repair-them

 

 

Model Comparison and Robustness: A Proposal for Policy Analysis after the Financial Crisis

Volker Wieland

1st Version: November 28, 2010 This Version: March 21, 2011

 

http://www.macromodelbase.com/fileadmin/user_upload/documents/Wieland_CournotConf_110321.pdf

 

 

TOBIN LIVES: INTEGRATING EVOLVING CREDIT MARKET ARCHITECTURE INTO FLOW OF FUNDS BASED MACRO- MODELS

John Duca and John Muellbauer

September 2012

 

http://www.economics.ox.ac.uk/materials/papers/12225/paper622.pdf

 

 

FRB/US Equations Documentation

http://www.petertulip.com/frbus_equation_documentation.pdf

 

 

Challenges for Central Banks’ Macro Models

Jesper Lindé, Frank Smets and Rafael Wouters

2016

 

http://www.riksbank.se/Documents/Rapporter/Working_papers/2016/rap_wp323_160512.pdf

 

 

Central Bank Models: Lessons from the Past and Ideas for the Future

John B. Taylor

2016

 

http://www.bankofcanada.ca/wp-content/uploads/2016/12/central-bank-models-lessons-past.pdf

 

 

Lawrence R. Klein: Macroeconomics, econometrics and economic policy􏰑

Ignazio Visco

2014

 

http://economics.sas.upenn.edu/sites/economics.sas.upenn.edu/files/u4/Visco_Klein_2014.pdf

 

 

Macro-Econometric System Modelling @75

Tony Hall  Jan Jacobs Adrian Pagan

http://www.ncer.edu.au/papers/documents/WP95.pdf

 

 

The Econometrics of Macroeconomic Modelling

Gunnar Ba ̊rdsenØyvind Eitrheim Eilev S. Jansen Ragnar Nymoen

http://folk.uio.no/rnymoen/master210104.pdf

 

 

The Macroeconomist as Scientist and Engineer

N. Gregory Mankiw

May 2006

 

http://scholar.harvard.edu/files/mankiw/files/macroeconomist_as_scientist.pdf?m=1360042085

 

 

 Macroeconometric Models

By Władysław Welfe

 

 

HISTORY OF MACROECONOMETRIC MODELLING: LESSONS FROM PAST EXPERIENCE

Abbas Valadkhani

 

http://eprints.qut.edu.au/385/1/Valadkhani_131.pdf

 

 

ECONOMETRICS: AN HISTORICAL GUIDE FOR THE UNINITIATED

by D.S.G. Pollock

University of Leicester

http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp14-05.pdf

 

 

RBI-MSE Joint Initiative on Modeling the Indian Economy for Forecasting and Policy Simulations

N R Bhanumurthy NIPFP, New Delhi, India

 

http://www.mse.ac.in/wp-content/uploads/2016/09/Model-1.pdf

 

 

ECONOMIC MODELS

 

http://pages.hmc.edu/evans/chap1.pdf

 

 

MACROECONOMIC MODELLING OF MONETARY POLICY

BY MATT KLAEFFLING

SEPTEMBER 2003

https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp257.pdf?62a2706261fcf6fb02a681c780f3408f

 

 

Macroeconomic Modeling in India

N R Bhanumurthy NIPFP, New Delhi, India

 

http://www.unescap.org/sites/default/files/India-Macroeconomic%20modeling%20in%20India.pdf

 

 

Macroeconomic Modeling in the Policy Process: A Review of Tools Used at the Federal Reserve Board and Their Relation to Ongoing Research

 

Michael Kiley

 

http://www.bcb.gov.br/secre/apres/Apresentação%20Michael%20Kiley.pdf

 

 

Policy Analysis Using DSGE Models: An Introduction

Argia M. Sbordone, Andrea Tambalotti, Krishna Rao, and Kieran Walsh

2010

 

https://www.newyorkfed.org/medialibrary/media/research/epr/10v16n2/1010sbor.pdf

 

 

DSGE Model-Based Forecasting

Marco Del Negro Frank Schorfheide

Staff Report No. 554 March 2012

 

https://pdfs.semanticscholar.org/7597/2761a45dbc2a4b57990d250adb8ae846129f.pdf

 

 

The Use of (DSGE) Models in Central Bank Forecasting: The FRBNY Experience

Marco Del Negro

 

https://www.ecb.europa.eu/pub/conferences/shared/pdf/20160922_dsge/DelNegro_DSGE_forecasting_panel.pdf

 

 

Modern Macroeconomic Models as Tools for Economic Policy

Narayana Kocherlakota

 

https://www.minneapolisfed.org/~/media/files/pubs/region/10-05/2009_mplsfed_annualreport_essay.pdf

 

 

STRUCTURAL ECONOMETRIC MODELLING: METHODOLOGY AND TOOLS WITH APPLICATIONS UNDER EVIEWS

 

http://www.eviews.com/StructModel/structmodel.pdf

 

 

Macroeconomic Models for Monetary Policies: A Critical Review from a Finance Perspective∗

Winston W. Dou †, Andrew W. Lo‡, and Ameya Muley

This Draft: March 12, 2015

https://bfi.uchicago.edu/sites/default/files/research/MacroFinanceReview_v11_DLM.pdf

 

 

Lawrence R. Klein 1920-2013: Notes on the early years

Olav Bjerkholt, University of Oslo

 

 

A History of Macroeconomics from Keynes to Lucas and Beyond

 

By Michel De Vroey

2016

 

 

Economic Theory, Model Size, and Model Purpose

John B Taylor

Chapter in a Book Large Scale Macroe conomtric Models

1981