Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions

Christiane Baumeister James D. Hamilton

in: NBER Working Paper No. 26606, 2020

Abstract

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.

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Fiscal Policy and Fiscal Fragility: Empirical Evidence from the OECD

Makram El-Shagi Gregor von Schweinitz

in: IWH Discussion Papers, No. 13, 2019

Abstract

In this paper, we use local projections to investigate the impact of consolidation shocks on GDP growth, conditional on the fragility of government finances. Based on a database of fiscal plans in OECD countries, we show that spending shocks are less detrimental than tax-based consolidation. In times of fiscal fragility, our results indicate strongly that governments should consolidate through surprise policy changes rather than announcements of consolidation at a later horizon.

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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?

Johannes Beutel Sophia List Gregor von Schweinitz

in: IWH Discussion Papers, No. 2, 2019

Abstract

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

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Did the Swiss Exchange Rate Shock Shock the Market?

Manuel Buchholz Gregor von Schweinitz Lena Tonzer

in: IWH Discussion Papers, No. 9, 2018

Abstract

The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Based on a synthetic matching framework, we analyse the impact of this unexpected (and therefore exogenous) shock on the stock market. The results reveal a significant level shift (decline) in asset prices in Switzerland following the discontinuation of the minimum exchange rate. While adjustments in stock market returns were most pronounced directly after the news announcement, the variance was elevated for some weeks, indicating signs of increased uncertainty and potentially negative consequences for the real economy.

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Sovereign Stress, Banking Stress, and the Monetary Transmission Mechanism in the Euro Area

Oliver Holtemöller Jan-Christopher Scherer

in: IWH Discussion Papers, No. 3, 2018

Abstract

In this paper, we investigate to what extent sovereign stress and banking stress have contributed to the increase in the level and in the heterogeneity of non-financial firms’ financing costs in the Euro area during the European debt crisis and how both have affected the monetary transmission mechanism. Employing a large firm-level data set containing two million observations, we are able to identify the effect of government bond yield spreads (sovereign stress) and the share of non-performing loans (banking stress) on firms‘ financing costs in a panel model by assuming that idiosyncratic shocks to individual firms are uncorrelated with country-specific variables. We find that the two sources of stress have increased firms’ financing costs controlling for country and firm-specific factors. Moreover, we estimate both to have significantly impaired the monetary transmission mechanism.

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