Nie ma wersji polskiej
Dominik Hirschbühl
- 4 August 2021
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 5, 2021Details
- Abstract
- This article reviews how policy institutions – international organisations and central banks – use big data and machine learning methods to analyse the business cycle. It provides different examples to show how big data and machine learning methods are particularly suitable for capturing large shocks and non-linearities in real time. The coronavirus crisis is a case in point, where big data have provided invaluable timely signals on the state of the economy, thus helping to track and assess economic activity, domestic demand and labour market developments in real time. Finally, the article discusses the main challenges faced by central banks when using non-standard data and methods and areas of further application to the work of central banks.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 12 April 2021
- WORKING PAPER SERIES - No. 2536Details
- Abstract
- Foreign driven medium-term oscillations that originate from fluctuations in technological frontier countries gained widespread attention among policymakers. To study this phenomenon in the context of domestic and other foreign drivers of the euro area business cycle, we develop a medium-scale, two-economy dynamic stochastic general equilibrium model with endogenous growth and estimate it with Bayesian methods for the United States and the euro area for the period from 1984:Q1 to 2017:Q4. The framework suggests that foreign shocks can be a substantial source of medium-term oscillations that contribute to pro-cyclicality of real GDP across countries. Notably, US shocks to liquidity preference and trade demand explain more than a third of the euro area downturn during the Great Recession.
- JEL Code
- E2 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
F1 : International Economics→Trade
F4 : International Economics→Macroeconomic Aspects of International Trade and Finance
O4 : Economic Development, Technological Change, and Growth→Economic Growth and Aggregate Productivity
- 25 November 2020
- WORKING PAPER SERIES - No. 2494Details
- Abstract
- We propose a granular framework that makes use of advanced statistical methods to approximate developments in economy-wide expected corporate earnings. In particular, we evaluate the dynamic network structure of stock returns in the United States as a proxy for the transmission of shocks through the economy and identify node positions (firms) whose connectedness provides a signal for economic growth. The nowcasting exercise, with both the in-sample and the out-of-sample consistent feature selection, highlights which firms are contemporaneously exposed to aggregate downturns and provides a more complete narrative than is usually provided by more aggregate data. The two-state model for predicting periods of negative growth can remarkably well predict future states by using information derived from the node-positions of manufacturing, transportation and financial (particularly insurance) firms. The three-states model, which identifies high, low and negative growth, successfully predicts economic regimes by making use of information from the financial, insurance, and retail sectors.
- JEL Code
- C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
N1 : Economic History→Macroeconomics and Monetary Economics, Industrial Structure, Growth, Fluctuations
- 5 October 2020
- WORKING PAPER SERIES - No. 2475Details
- Abstract
- We estimate a modified version of the “Financial Business Cycles” model originally developed by Iacoviello (2015) in order to investigate the role played by financial factors in driving the business cycle in the euro area. In the model, financial shocks such as borrower defaults, collateral shocks and credit supply effects amplify economic downturns by reducing the flow of credit from banks to the real sector. In this novel application to the euro area, we introduce capital reallocation inefficiency, an innovation to the original set-up which allows for more realistic effects of entrepreneur defaults on economic activity. Our results suggest that financial factors, as captured by this model, played a smaller role in the euro area throughout the double-dip recession than in the United States during the 2008-09 global financial crisis. In a scenario on second-round effects implied by potential NFC loan losses due to the COVID-19 pandemic, we find large financial amplification risks to real economic activity.
- JEL Code
- E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
- 9 January 2020
- WORKING PAPER SERIES - No. 2359Details
- Abstract
- We model economic policy uncertainty (EPU) in the four largest euro area countries by applying machine learning techniques to news articles. The unsupervised machine learning algorithm used makes it possible to retrieve the individual components of overall EPU endogenously for a wide range of languages. The uncertainty indices computed from January 2000 to May 2019 capture episodes of regulatory change, trade tensions and financial stress. In an evaluation exercise, we use a structural vector autoregression model to study the relationship between different sources of uncertainty and investment in machinery and equipment as a proxy for business investment. We document strong heterogeneity and asymmetries in the relationship between investment and uncertainty across and within countries. For example, while investment in France, Italy and Spain reacts strongly to political uncertainty shocks, in Germany investment is more sensitive to trade uncertainty shocks.
- JEL Code
- C80 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→General
D80 : Microeconomics→Information, Knowledge, and Uncertainty→General
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
E66 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General Outlook and Conditions
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation
G31 : Financial Economics→Corporate Finance and Governance→Capital Budgeting, Fixed Investment and Inventory Studies, Capacity
- 6 August 2019
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 5, 2019Details
- Abstract
- This box presents a model-based economic policy uncertainty (EPU) index for the euro area by applying machine learning techniques to news articles from January 2000 to May 2019. The machine learning algorithm retrieves components of overall EPU, such as trade, fiscal, monetary or domestic regulations, for a wide range of languages. Recently, a steady and pronounced increase in the euro area EPU index has been observed, driven mainly by trade, domestic regulation and fiscal policy uncertainties.
- JEL Code
- C1 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General
C8 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs
E65 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Studies of Particular Policy Episodes