A VAR Model for the Analysis of the Effects of Monetary Policy in the Euro Area

This paper, by the estimation of a structural VAR model on aggregate data from 1980 to 2002, examines the macroeconomic effects of an unexpected change in monetary policy on the euro area. The results are in line with the economic theory and they are close to the one estimated by other authors. These results, considering the formation of the European Monetary Union, give rise to some doubts and require some considerations. Thus, this paper discusses the limits of both the econometric technique used, and the data compilation methodology usually applied in these works.

Monetary Policy, the Housing Market, and the 2008 Recession: A Structural Factor Analysis

DThis paper estimates a Structural Dynamic Factor Model on a panel of 102 US quarterly series. We model economic comovements by means of 5 underlying structural shocks (oil price, productivity, aggregate demand, monetary policy, and housing demand). The results of the benchmark model (impulse responses and variance decompositions) are in line with those predicted by economic theory and usually estimated by the empirical literature. We show that while over the whole sample the contribution of the housing demand shock is negligible, after the early eighties' liberalizations in housing finance, the housing demand shock has become a substantial source of business cycle fluctuations. The model is then used to analyze the causes of the 2008 recession: results indicate that we cannot exclude that monetary policy played a non negligible role in leading the way for the downturn in residential investment and the ensuing recession.\\ JEL Classification: C32, E32, E52, R2Structural Factor Model, Business Cycle, Monetary Policy, Housing.Structural Factor Model, Business Cycle, Monetary Policy, Housing

Measuring Euro Area Monetary Policy Transmission in a Structural Dynamic Factor Model

We study the effects of euro area common monetary policy by means of a structural dynamic factor model estimated on a large panel of euro area quarterly series. While we estimate a flat response of prices to a monetary policy shock, which we explain as aggregation of heterogeneous country-specific responses, we find no relevant asymmetries between countries in terms of output reaction. However, for both Spain and Italy, we find asymmetries in consumption, investment and unemployment. The introduction of the single currency in 1999 has helped reducing asymmetries in price responses but not in consumption and investment.

A model for vast panels of volatilities

Realized volatilities, when observed over time, share the following stylised facts: comovements, clustering, long-memory, dynamic volatility, skewness and heavy-tails. We propose a dynamic factor model that captures these stylised facts and that can be applied to vast panels of volatilities as it does not suffer from the curse of dimensionality. It is an enhanced version of Bai and Ng (2004) in the following respects: i) we allow for longmemory in both the idiosyncratic and the common components, ii) the common shocks are conditionally heteroskedastic, and iii) the idiosyncratic and common shocks are skewed and heavy-tailed. Estimation of the factors, the idiosyncratic components and the parameters is simple: principal components and low dimension maximum likelihood estimations. A Monte Carlo study shows the usefulness of the approach and an application to 90 daily realized volatilities, pertaining to S&P100, from January 2001 to December 2008, evinces, among others, the following fi ndings: i) All the volatilities have long-memory, more than half in the nonstationary range, that increases during fi nancial turmoils. ii) Tests and criteria point towards one dynamic common factor driving the co-movements. iii) The factor has larger long-memory than the assets volatilities, suggesting that long–memory is a market characteristic. iv) The volatility of the realized volatility is not constant and common to all. v) A forecasting horse race against 8 competing models shows that our model outperforms, in particular in periods of stressCuando se observan a través del tiempo, las volatilidades realizadas comparten una serie de características comunes: comovimiento, comportamiento de racimo (clustering), memoria larga, volatilidad de la volatilidad dinámica, asimetría y colas gruesas. En este artículo proponemos un modelo dinámico factorial que captura estas características y que puede ser aplicado a paneles de volatilidad de grandes dimensiones dado que no sufre la maldición de la dimensionalidad. El modelo es una adaptación del de Bai y Ng (2004) en los siguientes aspectos: i) permitimos memoria larga en los componentes comunes e idiosincráticos, ii) las sacudidas (shocks) comunes son condicionalmente heterocedásticas, y iii) las sacudidas comunes e idiosincráticas son asimétricas y con colas gruesas. La estimación de los factores, los componentes idiosincráticos y los parámetros es simple: componentes principales y estimaciones de máxima verosimilitud de baja dimensión. Un profundo estudio de Monte Carlo muestra la utilidad de la estrategia de estimación y una aplicación a un panel de 90 volatilidades realizadas correspondiente a compañías pertenecientes al índice S&P100, desde enero de 2001 a diciembre de 2008, muestra, entre otros resultados, que i) todas las volatilidades tienen memoria larga, más de la mitad en el rango no estacionario, que se incrementa durante períodos de estrésii) contrastes y criterios indican la presencia de un factor común dinámicoiii) el factor tiene memoria más larga que las volatilidades de las compañías, lo que sugiere que la memoria larga es una característica del mercadoiv) la volatilidad de la volatilidad realizada es dinámica y común para todas las compañíasv) una comparación entre 8 modelos en términos de predicción muestra que nuestro modelo es superior, sobre todo en períodos de estré

The Determinants of Investment in Information and Communication Technologies. Bruges European Economic Research (BEER) Papers 16/March 2010

In this paper we investigate the determinants of ICT investment at the macro level for a panel of ten countries over the period 1992-2005. We argue that, since ICT is a General Purpose Technology, its diffusion can be understood only considering the interaction with institutional and structural factors. The empirical results are in line with this view: facilitating factors such as changes in regulation, human capital and the sectoral composition of the economy are relevant determinants for increasing ICT investment

Googling SIFIs

To measure the systemic risk in financial markets, and rank systemically important financial institutions (SIFIs), we propose a methodology based on the Google PageRank algorithm. We understand the economic system as interconnected risk shocks of firms in both the financial sector and the real economy. By taking into account both sectors, we demonstrate the efficacy of intervention programs, such as the TARP, as circuit breakers in the propagation of crises – something not evident in applications which address only financial firms

Systemic risk in the US: Interconnectedness as a circuit breaker

We measure systemic risk via the interconnections between the risks facing both financial and real economy firms. SIFIs are ranked by building on the Google PageRank algorithm for finding closest connections. For a panel of over 500 US firms over 2003-2011 we find evidence that intervention programs (such as TARP) act as circuit breakers in crisis propagation. The curve formed by the plot of firm average systemic risk against its variability clearly separates financial firms into three groups: (i) the consistently systemically risky (ii) those displaying the potential to become risky and (iii) those of little concern for macro-prudential regulators.The authors acknowledge the support provided for this research by the Centre for International Finance and Regulation under Grant E102: Detecting Systemically Important Risk. A large part of this paper was written while Matteo Luciani was charge de recherches F.R.S.- F.N.R.S., and he gratefully acknowledges their financial support