02.10.2019 • 20/2019
Joint Economic Forecast Autumn 2019: Economy Cools Further – Industry in Recession
Berlin, October 2, 2019 – Germany’s leading economics research institutes have revised their economic forecast for Germany significantly downward. Whereas in the spring they still expected gross domestic product (GDP) to grow by 0.8% in 2019, they now expect GDP growth to be only 0.5%. Reasons for the poor performance are the falling worldwide demand for capital goods – in the exporting of which the Germany economy is specialised – as well as political uncertainty and structural changes in the automotive industry. By contrast, monetary policy is shoring up macroeconomic expansion. For the coming year, the economic researchers have also reduced their forecast of GDP growth to 1.1%, having predicted 1.8% in the spring.
Oliver Holtemöller
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Pricing Sin Stocks: Ethical Preference vs. Risk Aversion
Stefano Colonnello, Giuliano Curatola, Alessandro Gioffré
European Economic Review,
2019
Abstract
We develop an ethical preference-based model that reproduces the average return and volatility spread between sin and non-sin stocks. Our investors do not necessarily boycott sin companies. Rather, they are open to invest in any company while trading off dividends against ethicalness. When dividends and ethicalness are complementary goods and investors are sufficiently risk averse, the model predicts that the dividend share of sin companies exhibits a positive relation with the future return and volatility spreads. An empirical analysis supports the model’s predictions. Taken together, our results point to the importance of ethical preferences for investors’ portfolio choices and asset prices.
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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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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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Accounting Quality in Banking: The Role of Regulatory Interventions
Manthos D. Delis, Iftekhar Hasan, Maria Iosifidi, Lingxiang Li
Journal of Banking and Finance,
2018
Abstract
Using the full sample of U.S. banks and hand-collected data on enforcement actions over 2000–2014, we analyze the role of these interventions in promoting several aspects of accounting quality. We find that enforcement actions issued for both risk-related and accounting-related reasons lead to significant improvements in accounting quality. This improvement is consistently found for earnings smoothing, big-bath accounting, timely recognition of future loan losses, the association of loan loss provisions with future loan charge offs, loss avoidance, and cash flow predictability and earnings persistence. Most of the effects are somewhat more potent in the crisis period and survive in several sensitivity tests. Our findings highlight the imperative role of regulatory interventions in promoting bank accounting quality.
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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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Connecting to Power: Political Connections, Innovation, and Firm Dynamics
Ufuk Akcigit, Salomé Baslandze, Francesca Lotti
NBER Working Paper,
No. 25136,
2018
Abstract
How do political connections affect firm dynamics, innovation, and creative destruction? To answer this question, we build a firm dynamics model, where we allow firms to invest in innovation and/or political connection to advance their productivity and to overcome certain market frictions. Our model generates a number of theoretical testable predictions and highlights a new interaction between static gains and dynamic losses from rent-seeking in aggregate productivity. We test the predictions of our model using a brand-new dataset on Italian firms and their workers, spanning the period from 1993 to 2014, where we merge: (i) firm-level balance sheet data; (ii) social security data on the universe of workers; (iii) patent data from the European Patent Office; (iv) the national registry of local politicians; and (v) detailed data on local elections in Italy. We find that firm-level political connections are widespread, especially among large firms, and that industries with a larger share of politically connected firms feature worse firm dynamics. We identify a leadership paradox: when compared to their competitors, market leaders are much more likely to be politically connected, but much less likely to innovate. In addition, political connections relate to a higher rate of survival, as well as growth in employment and revenue, but not in productivity – a result that we also confirm using a regression discontinuity design.
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Information Feedback in Temporal Networks as a Predictor of Market Crashes
Stjepan Begušić, Zvonko Kostanjčar, Dejan Kovač, Boris Podobnik, H. Eugene Stanley
Complexity,
September
2018
Abstract
In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.
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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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Do Employers Have More Monopsony Power in Slack Labor Markets?
Boris Hirsch, Elke J. Jahn, Claus Schnabel
ILR Review,
No. 3,
2018
Abstract
This article confronts monopsony theory’s predictions regarding workers’ wages with observed wage patterns over the business cycle. Using German administrative data for the years 1985 to 2010 and an estimation framework based on duration models, the authors construct a time series of the labor supply elasticity to the firm and estimate its relationship to the unemployment rate. They find that firms possess more monopsony power during economic downturns. Half of this cyclicality stems from workers’ job separations being less wage driven when unemployment rises, and the other half mirrors that firms find it relatively easier to poach workers. Results show that the cyclicality is more pronounced in tight labor markets with low unemployment, and that the findings are robust to controlling for time-invariant unobserved worker or plant heterogeneity. The authors further document that cyclical changes in workers’ entry wages are of similar magnitude as those predicted under pure monopsonistic wage setting.
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