Lorena Saiz
- 28 January 2016
- OCCASIONAL PAPER SERIES - No. 167Details
- Abstract
- Although monetary union created the conditions for improving economic and financial integration in the euro area, in the context of the financial and sovereign crises, it has also been accompanied by the emergence of severe imbalances in savings and investment, credit and housing booms in some countries and the allocation of resources towards less productive sectors. The global financial crisis and the euro area sovereign debt crisis then led to major and abrupt adjustments as the risks posed by the large imbalances materialised. Although the institutional shortcomings in the EU that permitted the emergence of imbalances have been largely addressed since 2008, the adjustment process is not yet complete. From a macroeconomic perspective, the imbalances in the external accounts have led to the accumulation of high levels of external liabilities that need to be reduced, which, in turn, is weakening investment and therefore weighing on growth prospects and growth potential. From a macroprudential perspective, the lingering imbalances have added to systemic risk and rendered the euro area more vulnerable to risks. This Occasional Paper analyses the dynamic patterns in macroeconomic imbalances primarily from the former perspective, addressing in particular the connections between macroeconomic and sectoral adjustments of imbalances and the challenges for economic growth and performance over a longer horizon.
- JEL Code
- E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
F32 : International Economics→International Finance→Current Account Adjustment, Short-Term Capital Movements
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
- 6 February 2018
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 1, 2018Details
- Abstract
- Growth in the consumption of durable goods has been very strong in recent years. During the financial crisis, durable goods consumption contracted sharply, although the car scrappage schemes in several euro area countries provided some relief by encouraging purchases of new cars (e.g. in 2009). Since 2013, durable goods consumption has again grown vigorously, pushing up growth in overall consumption. The recovery observed in real disposable incomes and the easing of financing conditions have both boosted households’ appetite for durable goods, particularly in those euro area countries that were more affected by the financial crisis.
- JEL Code
- E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 9 August 2018
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 5, 2018Details
- Abstract
- The degree of business cycle synchronisation, both across the euro area countries as well as between the euro area and the rest of the world, is a pertinent research question. Regarding the euro area, the endogenous optimal currency area (OCA) hypothesis suggests that the degree of business cycle synchronisation among the participating countries should increase over time as a result of deepening financial and trade integration. Individual countries should thus become less exposed to idiosyncratic shocks, facilitating the effectiveness of the single monetary policy. Against this background, this box presents and analyses several measures of business cycle synchronisation both within the euro area as well as from a global perspective.
- JEL Code
- E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 8 November 2018
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 7, 2018Details
- Abstract
- This box reviews the characteristics of intangible assets and looks at a number of implications of their increasing importance. It finds that investment in intangible assets has increased in importance in the euro area, both in absolute terms and relative to tangible assets. Investment in intangibles enables productivity gains and can explain part of the gap between firms' investment in tangible assets and Tobin's Q. At the same time, the specific nature of intangible assets poses challenges as regards the measurement of activity, profits and capital stock, as well as making it less easy to use those assets as collateral.
- JEL Code
- D25 : Microeconomics→Production and Organizations
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
- 13 June 2019
- OCCASIONAL PAPER SERIES - No. 224Details
- Abstract
- Well-functioning economic structures are key for resilient and prospering euro area economies. The global financial and sovereign debt crises exposed the limited resilience of the euro area’s economic structures. Economic growth was masking underlying weaknesses in several euro area countries. With the inception of the crises, significant efforts have been undertaken by Member States individually and collectively to strengthen resilience of economic structures and the smooth functioning of the euro area. National fiscal policies were consolidated to keep the increase in government debt contained and structural reform momentum increased notably in the second decade, particularly in those countries most hit by the crisis. The strengthened national economic structures were supported by a reformed EU crisis and economic governance framework. However, overall economic structures in euro area countries are still not fully commensurate with the requirements of a monetary union. Moreover, remaining challenges, such as population ageing, low productivity and the implications of digitalisation, will need to be addressed to increase economic resilience and long-term growth.
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E60 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
F10 : International Economics→Trade→General
J11 : Labor and Demographic Economics→Demographic Economics→Demographic Trends, Macroeconomic Effects, and Forecasts
O43 : Economic Development, Technological Change, and Growth→Economic Growth and Aggregate Productivity→Institutions and Growth
- 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
- 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
- 26 March 2020
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 2, 2020Details
- Abstract
- This article first stresses the importance for a central bank of having a reliable quantitative framework for obtaining a real-time assessment of developments in economic activity in the near term and discusses associated challenges. Second, it presents the framework for short-term forecasting of euro area real GDP growth used at the ECB. The article evaluates the forecast performance of the framework, also comparing it with the Eurosystem/ECB staff macroeconomic projections. It also illustrates how the framework is used to i) analyse the role that data surprises play in the revisions to the outlook and ii) assess risks to the projections. It concludes by pointing out directions for future work.
- JEL Code
- C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 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 January 2021
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 8, 2020Details
- Abstract
- The exceptional contraction in economic activity induced by the outbreak of the coronavirus (COVID-19) has warranted an update of the standard toolkit used to forecast euro area real GDP in real time. This box describes the adjustments and the additions to the standard toolkit developed by ECB staff to account for the dramatic change in statistical and economic relationships due to COVID-19. The use of each individual tool is subject to a considerable degree of judgment as to the type of adjustment needed to best capture the sharp movements in economic activity. These tools have provided helpful insights into forecasting euro area real GDP in real time, even if they imply some shortcomings.
- JEL Code
- C18 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Methodological Issues: General
E27 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Forecasting and Simulation: Models and Applications