Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Private Debt, Public Debt, and Capital Misallocation
Behzod Alimov
IWH-CompNet Discussion Papers,
No. 7,
2019
Abstract
Does finance facilitate efficient allocation of resources? Our aim in this paper is to find out whether increases in private and public indebtedness affect capital misallocation, which is measured as the dispersion in the return to capital across firms in different industries. For this, we use a novel dataset containing industrylevel data for 18 European countries and control for different macroeconomic indicators as potential determinants of capital misallocation. We exploit the within-country variation across industries in such indicators as external finance dependence, technological intensity, credit constraints and competitive structure, and find that private debt accumulation disproportionately increases capital misallocation in industries with higher financial dependence, higher R&D intensity, a larger share of credit-constrained firms and a lower level of competition. On the other hand, we fail to find any significant and robust effect of public debt on capital misallocation within our country-sector pairs. We believe the distortionary effects of private debt found in our analysis needs a deeper theoretical investigation.
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Total Factor Productivity and the Terms of Trade
Jan Teresinski
IWH-CompNet Discussion Papers,
No. 6,
2019
Abstract
In this paper we analyse how the terms of trade (TOT) – the ratio of export prices to import prices – affect total factor productivity (TFP). We provide empirical macroeconomic evidence for the European Union countries based on the times series SVAR analysis and microeconomic evidence based on industry level data from the Competitiveness Research Network (CompNet) database which shows that the terms of trade improvements are associated with a slowdown in the total factor productivity growth. Next, we build a theoretical model which combines open economy framework with the endogenous growth theory. In the model the terms of trade improvements increase demand for labour employed in exportable goods production at the expense of technology production (research and development – R&D) which leads to a shift of resources from knowledge development towards physical exportable goods. This reallocation has a negative impact on the TFP growth. Under a plausible calibration the model is able to replicate the observed empirical pattern.
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Resolving the Missing Deflation Puzzle
Jesper Lindé, Mathias Trabandt
Abstract
We propose a resolution of the missing deflation puzzle. Our resolution stresses the importance of nonlinearities in price- and wage-setting when the economy is exposed to large shocks. We show that a nonlinear macroeconomic model with real rigidities resolves the missing deflation puzzle, while a linearized version of the same underlying nonlinear model fails to do so. In addition, our nonlinear model reproduces the skewness of inflation and other macroeconomic variables observed in post-war U.S. data. All told, our results caution against the common practice of using linearized models to study inflation and output dynamics.
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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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The Research Data Centre of the Halle Institute for Economic Research – Member of the Leibniz Association FDZ-IWH
Tim Kuttig, Cornelia Lang
Jahrbücher für Nationalökonomie und Statistik,
No. 2,
2017
Abstract
The Halle Institute for Economic Research (IWH) was founded in 1992 and operates three research departments: Macroeconomics, Financial Markets, and Structural Change and Productivity. The IWH’s research structure is designed to foster close interplay between micro and macroeconomic research, however it has its roots in the empirical research conducted on the transition from a planned to a market economy, with a particular focus on East Germany.
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17th IWH-CIREQ-GW Macroeconometric Workshop: „Inequality, Micro Data and Macroeconomics”
Christoph Schult
Wirtschaft im Wandel,
No. 1,
2017
Abstract
Am 5. und 6. Dezember 2016 fand am Leibniz-Institut für Wirtschaftsforschung Halle (IWH) zum 17. Mal der IWH-CIREQ Macroeconometric Workshop statt. Die erfolgreiche Kooperation mit dem Centre inter- universitaire de recherche en economie quantitative (CIREQ) wurde in diesem Jahr um die George Washington University (GW) verstärkt. Als neuer Kooperationspartner wurde die seit Februar 2016 am IWH tätige Forschungsprofessorin Tara Sinclair, Ph.D., in diesem Jahr mit in die Auswahlkommission berufen. Der diesjährige Workshop befasste sich mit dem Thema „Inequality, Micro Data and Macroeconomics“.
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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
Abstract
In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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