Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks
Christiane Baumeister, James D. Hamilton
American Economic Review,
No. 5,
2019
Abstract
Traditional approaches to structural vector autoregressions (VARs) can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not.
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01.04.2019 • 8/2019
Bank profitability increases after eliminating consolidation barriers
When two banks merge because political consolidation barriers are abolished, the combined entity is considerably more profitable and useful to the real economy. This is the headline result of an analysis of compulsory savings banks mergers carried out by the Halle Institute for Economic Research (IWH). The study yields important insights for the German and the European banking market.
Michael Koetter
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On DSGE Models
Lawrence J. Christiano, Martin S. Eichenbaum, Mathias Trabandt
Journal of Economic Perspectives,
No. 3,
2018
Abstract
The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.
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11.10.2016 • 44/2016
New look and feel for IWH website
The relaunched website of the Halle Institute for Economic Research (IWH) – Member of the Leibniz Association goes live today. After the successful launch of a new corporate design at the beginning of the year, IWH now presents itself also digitally in a new and adjusted way. The redesigned website focuses on IWH’s core issues and provides information tailored to each target group. Due to the responsive design, the new website can be perfectly read and navigated on smartphones and tablets as well.
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05.10.2016 • 42/2016
International young researchers at IWH
Scientific insights do not stop at national borders and have to be equally accessible for women and men. “Whoever wants to do world-class research has to look beyond his own nose”, says Reint E. Gropp, president of the Halle Institute for Economic Research (IWH) – Member of the Leibniz Association. He himself received his PhD and did research in the US for several years.
Reint E. Gropp
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09.06.2016 • 22/2016
The German Economy Benefits from Strong Domestic Demand
In 2016, the moderate upswing of the German economy continues. Incomes grow due to the steady expansion in employment, and the fall in energy prices has propped up the purchasing power of private households. As a consequence, private consumption expands healthily; investment in housing is additionally stimulated by very low interest rates. Exports, however, expand only moderately, as the world economy is rather weak. All in all, the IWH forecasts the German GDP to expand by 1.8% in this year and by 1.6% in 2017.
Oliver Holtemöller
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On the Low-frequency Relationship Between Public Deficits and Inflation
Martin Kliem, Alexander Kriwoluzky, Samad Sarferaz
Journal of Applied Econometrics,
No. 3,
2016
Abstract
We estimate the low-frequency relationship between fiscal deficits and inflation and pay special attention to its potential time variation by estimating a time-varying vector autoregression model for US data from 1900 to 2011. We find the strongest relationship neither in times of crisis nor in times of high public deficits, but from the mid 1960s up to 1980. Employing a structural decomposition of the low-frequency relationship and further narrative evidence, we interpret our results such that the low-frequency relationship between fiscal deficits and inflation is strongly related to the conduct of monetary policy and its interaction with fiscal policy after World War II.
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Nested Models and Model Uncertainty
Alexander Kriwoluzky, Christian A. Stoltenberg
Scandinavian Journal of Economics,
No. 2,
2016
Abstract
Uncertainty about the appropriate choice among nested models is a concern for optimal policy when policy prescriptions from those models differ. The standard procedure is to specify a prior over the parameter space, ignoring the special status of submodels (e.g., those resulting from zero restrictions). Following Sims (2008, Journal of Economic Dynamics and Control 32, 2460–2475), we treat nested submodels as probability models, and we formalize a procedure that ensures that submodels are not discarded too easily and do matter for optimal policy. For the United States, we find that optimal policy based on our procedure leads to substantial welfare gains compared to the standard procedure.
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RLPC: Record Linkage Pre-Cleaning – Technical Documentation of Routines
Wilfried Ehrenfeld
IWH Technical Reports,
No. 2,
2015
Abstract
The primary objective of record linkage is the merger of different data sets on the basis of an unique identifier. The cases at hand are mostly company data sets from databanks with company characteristics (e.g. BvD Amadeus/Dafne), patent data sets (e.g. Patstat or DPMA) and funding data sets (e.g. BMBF funding catalog). These data sets shall be merged on the basis of the company names. Due to the fact that company names have varying notations in different databases - for example the corporate structure – a harmonization and standardization is necessary.
The routines described here implement the record linkage pre-cleaning (RLPC). They are used to create record linkage compatible names (RLName) from given (actor) names (Name). This includes converting special characters to ASCII characters, identifying corporate structures, isolating and separating bracketed expressions. The result is an expression which allows for a comparison with other names. Following this pre-cleaning, record linkage systems can be used to merge several data sets that have been pretreated in the same way.
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Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
Christiane Baumeister, James D. Hamilton
Econometrica,
No. 5,
2015
Abstract
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
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