Latviešu valodas versija nav pieejama
Andreas Joseph
- 22 November 2021
- WORKING PAPER SERIES - No. 2614Details
- Abstract
- We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering nonlinear relationships between the predic-tors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.
- JEL Code
- C40 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
F30 : International Economics→International Finance→General
G01 : Financial Economics→General→Financial Crises
- 12 September 2016
- WORKING PAPER SERIES - No. 1958Details
- Abstract
- The local network structure of international trade relations offers a new dimension for understanding a country
- JEL Code
- F14 : International Economics→Trade→Empirical Studies of Trade
F63 : International Economics→Economic Impacts of Globalization→Economic Development
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory - Network
- Competitiveness Research Network
- 15 July 2015
- OCCASIONAL PAPER SERIES - No. 163Details
- Abstract
- This Compendium describes the contribution of CompNet to the improvement of the analytical framework and indicators of competitiveness. It does this by presenting a comprehensive database of novel competitiveness indicators. These are more than 80 novel indicators designed by CompNet members that capture macro, micro and cross-country dimensions, thus providing a comprehensive view of the competitive position of EU countries and their peers. A short description of each innovative indicator
- JEL Code
- F14 : International Economics→Trade→Empirical Studies of Trade
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
F60 : International Economics→Economic Impacts of Globalization→General
D24 : Microeconomics→Production and Organizations→Production, Cost, Capital, Capital, Total Factor, and Multifactor Productivity, Capacity
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation