How Forecast Accuracy Depends on Conditioning Assumptions
Carola Engelke, Katja Heinisch, Christoph Schult
IWH Discussion Papers,
No. 18,
2019
Abstract
This paper examines the extent to which errors in economic forecasts are driven by initial assumptions that prove to be incorrect ex post. Therefore, we construct a new data set comprising an unbalanced panel of annual forecasts from different institutions forecasting German GDP and the underlying assumptions. We explicitly control for different forecast horizons to proxy the information available at the release date. Over 75% of squared errors of the GDP forecast comove with the squared errors in their underlying assumptions. The root mean squared forecast error for GDP in our regression sample of 1.52% could be reduced to 1.13% by setting all assumption errors to zero. This implies that the accuracy of the assumptions is of great importance and that forecasters should reveal the framework of their assumptions in order to obtain useful policy recommendations based on economic forecasts.
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History, Microdata, and Endogenous Growth
Ufuk Akcigit, Tom Nicholas
Annual Review of Economics,
2019
Abstract
The study of economic growth is concerned with long-run changes, and therefore, historical data should be especially influential in informing the development of new theories. In this review, we draw on the recent literature to highlight areas in which study of history has played a particularly prominent role in improving our understanding of growth dynamics. Research at the intersection of historical data, theory, and empirics has the potential to reframe how we think about economic growth in much the same way that historical perspectives helped to shape the first generation of endogenous growth theories.
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Financial Literacy and Self-employment
Aida Ćumurović, Walter Hyll
Journal of Consumer Affairs,
No. 2,
2019
Abstract
In this paper, we study the relationship between financial literacy and self‐employment. We use established financial literacy questions to measure literacy levels. The analysis shows a highly significant and positive correlation between the index and self‐employment. We address the direction of causality by applying instrumental variable techniques based on information about maternal education. We also exploit information on financial support and family background to account for concerns about the exclusion restriction. The results provide support for a positive effect of financial literacy on the probability of being self‐employed. As financial literacy is acquirable, the findings suggest that entrepreneurial activities might be increased by enhancing financial literacy.
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Mission, Motivation, and the Active Decision to Work for a Social Cause
Sabrina Jeworrek, Vanessa Mertins
Abstract
The mission of a job does not only affect the type of worker attracted to an organisation, but may also provide incentives to an existing workforce. We conducted a natural field experiment with 267 short-time workers and randomly allocated them to either a prosocial or a commercial job. Our data suggest that the mission of a job itself has a performance enhancing motivational impact on particular individuals only, i.e., workers with a prosocial attitude. However, the mission is very important if it has been actively selected. Those workers who have chosen to contribute to a social cause outperform the ones randomly assigned to the same job by about 15 percent. This effect seems to be a universal phenomenon which is not driven by information about the alternative job, the choice itself or a particular subgroup.
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Predicting Free-riding in a Public Goods Game – Analysis of Content and Dynamic Facial Expressions in Face-to-Face Communication
Dmitri Bershadskyy, Ehsan Othman, Frerk Saxen
IWH Discussion Papers,
No. 9,
2019
Abstract
This paper illustrates how audio-visual data from pre-play face-to-face communication can be used to identify groups which contain free-riders in a public goods experiment. It focuses on two channels over which face-to-face communication influences contributions to a public good. Firstly, the contents of the face-to-face communication are investigated by categorising specific strategic information and using simple meta-data. Secondly, a machine-learning approach to analyse facial expressions of the subjects during their communications is implemented. These approaches constitute the first of their kind, analysing content and facial expressions in face-to-face communication aiming to predict the behaviour of the subjects in a public goods game. The analysis shows that verbally mentioning to fully contribute to the public good until the very end and communicating through facial clues reduce the commonly observed end-game behaviour. The length of the face-to-face communication quantified in number of words is further a good measure to predict cooperation behaviour towards the end of the game. The obtained findings provide first insights how a priori available information can be utilised to predict free-riding behaviour in public goods games.
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Lock‐in Effects in Relationship Lending: Evidence from DIP Loans
Iftekhar Hasan, Gabriel G. Ramírez, Gaiyan Zhang
Journal of Money, Credit and Banking,
No. 4,
2019
Abstract
Do prior lending relationships result in pass‐through savings (lower interest rates) for borrowers, or do they lock in higher costs for borrowers? Theoretical models suggest that when borrowers experience greater information asymmetry, higher switching costs, and limited access to capital markets, they become locked into higher costs from their existing lenders. Firms in Chapter 11 seeking debtor‐in‐possession (DIP) financing often fit this profile. We investigate the presence of lock‐in effects using a sample of 348 DIP loans. We account for endogeneity using the instrument variable (IV) approach and the Heckman selection model and find consistent evidence that prior lending relationship is associated with higher interest costs and the effect is more severe for stronger existing relationships. Our study provides direct evidence that prior lending relationships do create a lock‐in effect under certain circumstances, such as DIP financing.
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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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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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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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