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Laurent Maurin

9 June 2005
OCCASIONAL PAPER SERIES - No. 30
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Abstract
Chapter 1 provides an overview and assessment of the price competitiveness and export performance of the euro area and the larger euro area countries, as well as an evaluation of how standard equations have been able to explain actual export developments. Chapter 2 carries out a constant market share analysis for the euro area and thereby sheds light on the reasons for movements in aggregate export market shares by looking at the sectoral and geographical composition of euro area exports. Chapter 3 looks at the evolution of the technological competitiveness of the euro area and major competitors
JEL Code
E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
16 May 2008
WORKING PAPER SERIES - No. 894
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Abstract
Several factor-based models are estimated to investigate the role of country-specific trade and survey data in forecasting euro area manufacturing production. Following Boivin and Ng (2006), the emphasis is put on the role of dataset selection on the empirical performance of factor models. First, spectral analysis is used to assess the information content for euro area manufacturing production of external trade and surveys data of the three largest economies as well as two medium-sized highly opened economies. Second, common factors are estimated on four datasets, following two methodologies, Stock and Watson (2002a, 2002b) and Forni et al. (2005). Third, a rolling out of sample forecast comparison exercise is carried out on nine models. Compared to univariate benchmarks, our results are supportive of factor-based models up to two quarters. They show that incorporating survey and external trade information improves the forecast of manufacturing production. They also confirm the findings of Marcellino, Stock and Watson (2003) that, using country information, it is possible to improve forecasts for the euro area. Interesting, the medium-sized highly opened economies provide valuable information to monitor area wide developments, beyond their weight in the aggregate. Conversely, the large countries do not add much to the monitoring of the aggregate, when considered separately.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C3 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
14 August 2008
WORKING PAPER SERIES - No. 925
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Abstract
Euro area GDP and components are now-cast and forecast one quarter ahead. Based on a dataset of 163 series comprising the relevant monthly indicators, simple bridge equations with one explanatory variable are estimated for each. The individual forecasts generated by each equation are then pooled, using six weighting schemes including Bayesian ones. To take into consideration the release calendar of each indicator, six forecasts are compiled independently during the quarter, each based on different information sets: different indicators, different individual equations and finally different weights to aggregate information. The information content of the various blocks of information at different points in time for each GDP component is then discussed. It appears that taking into account the information flow results in significant changes in the weight allocated to each block of information, especially when the first month of hard data becomes available. This conclusion, reached for all the components and most of the weighting scheme, supports and extends the findings of Giannone, Reichlin and Small (2006) and Banbura and R
JEL Code
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E17 : Macroeconomics and Monetary Economics→General Aggregative Models→Forecasting and Simulation: Models and Applications
4 October 2011
WORKING PAPER SERIES - No. 1384
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Abstract
The paper focuses on the estimation of the euro area output gap. We construct model-averaged measures of the output gap in order to cope with both model uncertainty and parameter instability that are inherent to trend-cycle decomposition models of GDP. We first estimate nine models of trend-cycle decomposition of euro area GDP, both univariate and multivariate, some of them allowing for changes in the slope of trend GDP and/or its error variance using Markov-switching specifications, or including a Phillips curve. We then pool the estimates using three weighting schemes. We compute both ex-post and real-time estimates to check the stability of the estimates to GDP revisions. We finally run a forecasting experiment to evaluate the predictive power of the output gap for inflation in the euro area. We find evidence of changes in trend growth around the recessions. We also find support for model averaging techniques in order to improve the reliability of the potential output estimates in real time. Our measures help forecasting inflation over most of our evaluation sample (2001-2010) but fail dramatically over the last recession.
JEL Code
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
20 December 2011
WORKING PAPER SERIES - No. 1410
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Abstract
This paper explores the behavior of profits in the four largest euro area countries (Germany, France, Italy and Spain) and the euro area as a whole, while at the same time considering three main sectors (manufacturing, construction and services) in each economy over the period 1988–2010. The paper presents stylized facts about profit developments and, applying a vector autoregressive modeling framework, discusses the sensitivity of profits to four distinctive structural shocks (a demand shock, an employment shock, a wage and price mark-up shocks). In addition, it provides the shock decomposition of historical developments in profits across countries and sectors.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E23 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Production
E25 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Aggregate Factor Income Distribution
28 November 2012
WORKING PAPER SERIES - No. 1499
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Abstract
We develop a partial adjustment model in order to estimate the factors contributing to banks
JEL Code
G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
6 March 2014
WORKING PAPER SERIES - No. 1644
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Abstract
We implement a two-step approach to construct a financing conditions index (FCI) for the euro area and its four larger member states (Germany, France, Italy and Spain). The method, which follows Hatzius et al. (2010), is based on factor analysis and enables to summarise information on financing conditions from a large set of financial indicators, controlling for the level of policy interest rates, changes in output and inflation. We find that the FCI tracks successfully both worldwide and euro area specific financial events. Moreover, while the national FCIs are constructed independently, they display a similar pattern across the larger euro area economies over most of the sample period and varied more widely since the start of the sovereign debt crisis in 2010. Focusing on the euro area, we then incorporate the FCI in a VAR model comprising output, inflation, the monetary policy rate, bank loans and bank lending spreads. The credit supply shock extracted with sign restrictions is estimated to have caused around one fifth of the decline in euro area manufacturing production at the trough of the financial crisis and a rise in bank lending spreads of around 30 basis points. We also find that adding the FCI to the VAR enables an earlier detection of credit supply shocks.
JEL Code
E17 : Macroeconomics and Monetary Economics→General Aggregative Models→Forecasting and Simulation: Models and Applications
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
18 September 2015
WORKING PAPER SERIES - No. 1849
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Abstract
Based on a sample of EU listed banks, we estimate the sensitivity of banks
JEL Code
G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
19 June 2017
WORKING PAPER SERIES - No. 2077
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Abstract
We contribute to the empirical literature on the impact of shocks to bank capital in the euro area by estimating a Bayesian VAR model identied with sign restrictions. The variables included in the VAR are those typically used in monetary policy analysis, extended to include aggregate banking sector variables. We estimate two shocks affecting the euro area economy, namely a demand shock and a shock to bank capital. The main findings of the paper are as follows: i) Impulse-response analysis shows that in response to a shock to bank capital, banks boost capital ratios by reducing their relative exposure to riskier assets and by adjusting lending to a larger extent than they increase the level of capital and reserves per se; ii) Historical shock decomposition analysis shows that bank capital shocks have contributed to increasing capital ratios since the crisis, impairing bank lending growth and contributing to widen bank lending spreads; and iii) counterfactual analysis shows that higher capital ratios pre-crisis would have helped dampening the euro area credit and business cycle. This suggests that going forward the use of capital-based macroprudential policy instruments may be helpful to avoid a repetition of the events seen since the start of the global financial crisis.
JEL Code
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
16 September 2019
MACROPRUDENTIAL BULLETIN - ARTICLE - No. 8
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Abstract
How do changes in bank capital requirements affect bank lending, lending spreads and the broader macroeconomy? The answer to this question is important for calibrating and assessing macroprudential policies. There is, however, relatively little empirical evidence to answer this question in the case of the euro area countries. This article contributes to filling this gap by studying the effects of changes in economic bank capital buffers in the four largest euro area countries. We use bank-level data and macroeconomic information to estimate a bank-internal, target level of economic capital ratio, i.e. the capital ratio that a bank would like to hold considering its own characteristics (size, profitability, risk aversion of its creditors, risk exposure, etc.) and macroeconomic conditions (expected GDP growth, etc.). Economic bank capital buffers are then computed as the difference between the current and the target economic capital ratio. However, due to adjustment costs, banks cannot adjust the actual capital ratio to the target level instantaneously. As a result, a change in the target capital ratio will result in an instantaneous change in the economic capital buffer. These buffers are aggregated at the country level and included in a panel Bayesian vector auto regressive (VAR) model. With the VAR, it is then possible to compute the response of macroeconomic and banking variables to a change in the buffer. The idea is that changes in economic capital buffers mimic the effects a change in regulatory capital requirements would have on the economy. We find that a negative economic capital buffer shock, i.e. a decline in actual capital ratios below the target level, leads to a modest decline in output and prices and to a larger decline in bank lending growth. By affecting the difference between actual and target economic capital ratios, these findings suggest that countercyclical capital-based macroprudential policy measures can be useful to dampen the financial cycle.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages