Recent Developments in Macro-Econometric Modeling: Theory and Applications

Dear Colleagues,

Developments in macro-econometrics have been evolving since the aftermath of the Second World War. Essentially, macro-econometrics benefited from the development of mathematical, statistical, and econometric tools. Such a research program attained a meaningful success; as the methods of macro-econometrics have rapidly been used to check the implications of economic theories, forecast business cycles, and to provide advice to policymakers. More recently, as macro-econometrics has been at the center of the debate between economists and statisticians, several extensions to this research program have been proposed, with different interesting challenges. Accordingly, macro-econometricians have applied novel methods (panel data, instrumental variables, generalized method of moments (GMM), time series analysis, various simulations and computational algorithms, frequentist and Bayesian estimation of dynamic stochastic general equilibrium (DSGE) models, etc.) to appropriate versions of macro-economic models.

However, despite this progress, important methodological and interpretative questions in macro-econometrics remain. Additionally, the recent global financial crisis and the subsequent economic recession (2007–2008) that highlighted the end of the Great Moderation suggested at least two limitations. On the one hand, it seems that current macroeconomics models were not able to forecast this economic down-turn, perhaps because they did not take into account an indicator of systemic risk, nor a measure for the financial cycle. On the other hand, it seems that something was going wrong. Indeed, several new lines of research pointed to the importance of measurement errors in macroeconomic and financial variables (Barnett, 2012). Additionally, it appears that monetary rules and macroeconomic models were mis-specified, and the pre-crisis generation of DSGE models had ignored many features of the real world, which should have been incorporated in the structure (e.g., banks and financial sector, heterogeneities, imperfect information, etc.) and behavioral assumptions (e.g., expectation formation and learning, rational inattention, etc.) of these models. Consequently, a growing recent research agenda has been defined to tackle these questions.

This Special Issue aims to focus on the recent developments in macroeconomic modeling and macro-econometric techniques that have been introduced to extend the original framework of macro-econometrics. Accordingly, this Special Issue will present theoretical, methodological, as well as empirical, research in macro-econometrics. It will, thus, provide a concise but authoritative update on the recent developments of macro-econometric models.

Prof. Dr. Gilles Dufrénot
Dr. Fredj Jawadi
Dr. Alexander Mihailov
Guest Editors

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• Macroeconomics
• Macro-econometrics
• Monetary Policy
• Macro-financial dynamics

References

Barnett, W., A. (2012), Getting It Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy. Cambridge, MA: MIT Press, 322 pp. ISBN: 978-0-262-51688-4.

Del Negro, Marco and Frank Schorfheide (2011), Bayesian Macroeconometrics, in John Geweke, Gary Koop and Herman van Dijk (eds.), The Oxford Handbook of Bayesian Econometrics, Oxford: Oxford University Press (Ch. 7).

Schorfheide, Frank (2013), Estimation and Evaluation of DSGE Models: Progress and Challenges, Advances in Economics and Econometrics, Volume 3, Number 51, p. 184–230

Stock, J., H. (2001), Essays Macro-econometrics, Journal of Econometrics, 100, 29-32.