flexpaneldid: A Stata Command for Causal Analysis with Varying Treatment Time and Duration
Eva Dettmann, Alexander Giebler, Antje Weyh
Abstract
>>A completely revised version of this paper has been published as: Dettmann, Eva; Giebler, Alexander; Weyh, Antje: flexpaneldid. A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration. IWH Discussion Paper 3/2020. Halle (Saale) 2020.<<
The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) and its Stata implementation, the command flexpaneldid. The approach is particularly useful for causal analysis of treatments with varying start dates and varying treatment durations (like investment grants or other subsidy schemes). Introducing more flexibility enables the user to consider individual treatment and outcome periods for the treated observations. The flexpaneldid command for panel data implements the developed flexible difference-in-differences approach and commonly used alternatives like CEM Matching and difference-in-differences models. The novelty of this tool is an extensive data preprocessing to include time information into the matching approach and the treatment effect estimation. The core of the paper gives two comprehensive examples to explain the use of flexpaneldid and its options on the basis of a publicly accessible data set.
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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
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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Inference in Structural Vector Autoregressions when the Identifying Assumptions are not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations
Christiane Baumeister, James D. Hamilton
Journal of Monetary Economics,
2018
Abstract
Point estimates and error bands for SVARs that are set identified are only justified if the researcher is persuaded that some parameter values are a priori more plausible than others. When such prior information exists, traditional approaches can be generalized to allow for doubts about the identifying assumptions. We use information about both structural coefficients and impacts of shocks and propose a new asymmetric t-distribution for incorporating information about signs in a nondogmatic way. We apply these methods to a three-variable macroeconomic model and conclude that monetary policy shocks are not the major driver of output, inflation, or interest rates.
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Central Bank Transparency and the Volatility of Exchange Rates
Stefan Eichler, Helge Littke
Journal of International Money and Finance,
2018
Abstract
We analyze the effect of monetary policy transparency on bilateral exchange rate volatility. We test the theoretical predictions of a stylized model using panel data for 62 currencies from 1998 to 2010. We find strong evidence that an increase in the availability of information about monetary policy objectives decreases exchange rate volatility. Using interaction models, we find that this effect is more pronounced for countries with a lower flexibility of goods prices, a lower level of central bank conservatism, and a higher interest rate sensitivity of money demand.
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Do Digital Information Technologies Help Unemployed Job Seekers Find a Job? Evidence from the Broadband Internet Expansion in Germany
Nicole Gürtzgen, André Diegmann, Laura Pohlan, Gerard J. van den Berg
Abstract
This paper studies effects of the introduction of a new digital mass medium on reemployment of unemployed job seekers. We combine data on high-speed (broadband) internet availability at the local level with German individual register data. We address endogeneity by exploiting technological peculiarities that affected the roll-out of high-speed internet. The results show that high-speed internet improves reemployment rates after the first months in unemployment. This is confirmed by complementary analyses with individual survey data suggesting that internet access increases online job search and the number of job interviews after a few months in unemployment.
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IWH-Subventionsdatenbank: Mikrodaten zu Programmen direkter Unternehmenssubventionen in Deutschland. Datendokumentation
Matthias Brachert, Alexander Giebler, Gerhard Heimpold, Mirko Titze, Dana Urban-Thielicke
IWH Technical Reports,
Nr. 2,
2018
Abstract
Nahezu alle entwickelten Volkswirtschaften haben Programme zur Förderung von Projekten in Unternehmen im Rahmen von Industriepolitik eingeführt. Allerdings ist sehr wenig darüber bekannt, welche Programme eigentlich genau zur Anwendung kommen, welche finanziellen Mittel dafür aufgebracht werden und ob die Programme in der Art und Weise wirken, wie sie ursprünglich intendiert waren. Evaluationsstudien, die auf kausalen Untersuchungsdesigns basieren, können einen wertvollen Beitrag zur Beantwortung der Frage leisten, ob ein Programm tatsächlich Wirkungen entfaltet und welcher der verschiedenen Ansätze am erfolgversprechendsten ist. Dieser Datenreport stellt die vom Zentrum für evidenzbasierte Politikberatung am Leibniz-Institut für Wirtschaftsforschung Halle (IWH-CEP) entwickelte IWH-Subventionsdatenbank vor. Die Datenbank enthält (Stand November 2018) zehn Programme industriepolitischer Maßnahmen, die in Deutschland zur Anwendung kamen bzw. kommen. Der Report geht auf die Förderregeln dieser Programme ein und beschreibt die Prozeduren der Zusammenführung zu einer Masterdatei. Ferner diskutiert der Report Möglichkeiten der Verknüpfung der Förderdaten mit externen Unternehmensdatensätzen, die eine zwingende Voraussetzung für die Durchführung von Wirkungsanalysen darstellen, da die administrativen Förderdaten nicht alle Informationen enthalten, die für kausale Untersuchungsdesigns notwendig sind.
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The Privatisation Activities of the Treuhandanstalt and the Transformation of the East German Corporate Landscape: A New Dataset for First Explorations
Alexander Giebler, Michael Wyrwich
IWH Technical Reports,
Nr. 1,
2018
Abstract
Even nearly 30 years after the fall of the Berlin Wall, the privatisation and transformation of East Germany's business landscape is controversially discussed in the media and politics. The privatisation process led to enormous structural changes, which were associated with massive job losses. In particular, the stagnating regional development of East Germany is often blamed on the “long shadow” of the privatisation activities of the Treuhandanstalt (THA). From a scientific perspective, however, there are hardly any contributions dealing with the effects of privatisation activities. The IWH-Treuhand Privatisation Micro Database introduced in this technical report is novel as such that it provides comprehensive information on employment and turnover figures for formerly state-owned enterprises for the early 1990s.
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The Role of Auditors in Merger and Acquisition Completion Time
Salim Chahine, Iftekhar Hasan, Mohamad Mazboudi
International Journal of Auditing,
Nr. 3,
2018
Abstract
Using a sample of 664 merger and acquisition (M&A) transactions and office‐level audit data, this study investigates the role of auditors in M&A completion time. We find that having a common auditor for both acquirer and target firms in M&A transactions increases the completion time of such transactions because the exposure to higher litigation and reputational costs outweighs the information‐access advantage of common auditors. However, auditors' past experience in M&A transactions helps reduce completion time and costs. These results are robust to having Big N auditors at both ends as well as to various acquirer, target, and deal characteristics.
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Business Dynamics of Innovating Firms: Linking U.S. Patents with Administrative Data on Workers and Firms
Stuart Graham, Cheryl Grim, Tariqul Islam, Alan Marco, Javier Miranda
Journal of Economics and Management Strategy,
Nr. 3,
2018
Abstract
This paper discusses the construction of a new longitudinal database tracking inventors and patent-owning firms over time. We match granted patents between 2000 and 2011 to administrative databases of firms and workers housed at the U.S. Census Bureau. We use inventor information in addition to the patent assignee firm name to improve on previous efforts linking patents to firms. The triangulated database allows us to maximize match rates and provide validation for a large fraction of matches. In this paper, we describe the construction of the database and explore basic features of the data. We find patenting firms, particularly young patenting firms, disproportionally contribute jobs to the U.S. economy. We find that patenting is a relatively rare event among small firms but that most patenting firms are nevertheless small, and that patenting is not as rare an event for the youngest firms compared to the oldest firms. Although manufacturing firms are more likely to patent than firms in other sectors, we find that most patenting firms are in the services and wholesale sectors. These new data are a product of collaboration within the U.S. Department of Commerce, between the U.S. Census Bureau and the U.S. Patent and Trademark Office.
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Lame-Duck CEOs
Marc Gabarro, Sebastian Gryglewicz, Shuo Xia
SSRN Working Papers,
2018
Abstract
We examine the relationship between protracted CEO successions and stock returns. In protracted successions, an incumbent CEO announces his or her resignation without a known successor, so the incumbent CEO becomes a “lame duck.” We find that 31% of CEO successions from 2005 to 2014 in the S&P 1500 are protracted, during which the incumbent CEO is a lame duck for an average period of about 6 months. During the reign of lame duck CEOs, firms generate an annual four-factor alpha of 11% and exhibit significant positive earnings surprises. Investors’ under-reaction to no news on new CEO information and underestimation of the positive effects of the tournament among the CEO candidates drive our results.
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