A Brief History of Macro-Economic Modeling, Forecasting, and Policy Analysis
From A History of Macroeconomics from Keynes to Lucas and Beyond
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
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
From Macroeconomic Modeling in the Policy Process: A Review of Tools Used at the Federal Reserve Board and Their Relation to Ongoing Research
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
Macroeconomic Models, Forecasting, and Policymaking
Andrea Pescatori and Saeed Zaman
Click to access 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
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
The FRB/US Model: A Tool for Macroeconomic Policy Analysis
Flint Brayton, Thomas Laubach, and David Reifschneider
2014
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
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
Can Disequilibrium Macroeconomic Models Be Used to Anticipate Financial Instability?
A Case Study
Dirk J. Bezemer
Click to access a6d8daa2716892ed0984f8aa0882c6dccefc.pdf
Central Bank Models:Lessons from the Past and Ideas for the Future
John B. Taylor
November 2016
Macro-Finance Models of Interest Rates and the Economy
Glenn D. Rudebusch∗
Federal Reserve Bank of San Francisco
Panel Discussion on Uses of Models at Central Banks
ECB Workshop on DSGE Models and Forecasting September 23, 2016
John Roberts
Click to access 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
Click to access 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
Click to access frbus_equation_documentation.pdf
Challenges for Central Banks’ Macro Models
Jesper Lindé, Frank Smets and Rafael Wouters
2016
Click to access rap_wp323_160512.pdf
Central Bank Models: Lessons from the Past and Ideas for the Future
John B. Taylor
2016
Click to access central-bank-models-lessons-past.pdf
Lawrence R. Klein: Macroeconomics, econometrics and economic policy
Ignazio Visco
2014
Click to access Visco_Klein_2014.pdf
Macro-Econometric System Modelling @75
Tony Hall Jan Jacobs Adrian Pagan
The Econometrics of Macroeconomic Modelling
Gunnar Ba ̊rdsenØyvind Eitrheim Eilev S. Jansen Ragnar Nymoen
Click to access 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
Click to access Valadkhani_131.pdf
ECONOMETRICS: AN HISTORICAL GUIDE FOR THE UNINITIATED
by D.S.G. Pollock
University of Leicester
RBI-MSE Joint Initiative on Modeling the Indian Economy for Forecasting and Policy Simulations
N R Bhanumurthy NIPFP, New Delhi, India
ECONOMIC MODELS
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
Click to access 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
Click to access Apresentação%20Michael%20Kiley.pdf
Policy Analysis Using DSGE Models: An Introduction
Argia M. Sbordone, Andrea Tambalotti, Krishna Rao, and Kieran Walsh
2010
DSGE Model-Based Forecasting
Marco Del Negro Frank Schorfheide
Staff Report No. 554 March 2012
Click to access 2761a45dbc2a4b57990d250adb8ae846129f.pdf
The Use of (DSGE) Models in Central Bank Forecasting: The FRBNY Experience
Marco Del Negro
Click to access DelNegro_DSGE_forecasting_panel.pdf
Modern Macroeconomic Models as Tools for Economic Policy
Narayana Kocherlakota
Click to access 2009_mplsfed_annualreport_essay.pdf
STRUCTURAL ECONOMETRIC MODELLING: METHODOLOGY AND TOOLS WITH APPLICATIONS UNDER EVIEWS
Click to access 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
Click to access 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
Viewpoints which Matter 