01.02.2021 • 4/2021
During Corona, households are saving more – not for fear of unemployment but for lack of spending opportunities
During the Corona crisis, European households increased their savings dramatically. According to an analysis carried out by the Halle Institute for Economic Research (IWH), the increase in savings is largely due to the inability of households to consume in the face of government lockdown measures, rather than other factors such as economic uncertainty. IWH President Reint Gropp therefore sees potential for a significant catch-up effect in consumption as soon as the lockdown is lifted.
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Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions
Journal of International Money and Finance,
This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha’s (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.
Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks
American Economic Review,
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.
Does Machine Learning Help us Predict Banking Crises? ...
19.04.2018 • 7/2018
Joint Economic Forecast Spring 2018: Germany’s Economic Experts Raise Forecast Slightly
Berlin, 19 April – Germany’s leading economic experts raised their forecasts for 2018 and 2019 slightly in their Spring Joint Economic Forecast released on Thursday in Berlin. They now expect economic growth of 2.2 percent for this year and 2.0 percent for 2019, versus 2.0 percent and 1.8 percent respectively in their autumn forecast. “The German economy is still booming, but the air is getting thinner as unused capacities are shrinking“, notes Timo Wollmershaeuser, ifo Head of Economic Forecasting. Commenting on the new German government’s economic policy, he adds: “It is precisely when the government’s coffers are full that fiscal policy should reflect the implications of its actions for overall economic stability and the sustainability of public finances. The extension of statutory pension benefits outlined in the coalition agreement runs counter to the idea of sustainability.”
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Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
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.
The Quantity Theory Revisited: A New Structural Approach
We propose a unified identification scheme to identify monetary shocks and track their propagation through the economy. We combine three approaches dealing with the consequences of monetary shocks. First, we adjust a state space version of the P-star type model employing money overhang as the driving force of inflation. Second, we identify the contemporaneous impact of monetary policy shocks by applying a sign restriction identification scheme to the reduced form given by the state space signal equations. Third, to ensure that our results are not distorted by the measurement error exhibited by the official monetary data, we employ the Divisia M4 monetary aggregate provided by the Center for Financial Stability. Our approach overcomes one of the major difficulties of previous models by using a data-driven identification of equilibrium velocity. Thus, we are able to show that a P-star model can fit U.S. data and money did indeed matter in the United States.