Banking and Debt Crisis in Europe: The Dangerous Liaisons?

The potential mutation of the Sub-Prime banking crisis into a sovereign debt one in Euro area countries is investigated. After reviewing the criteria used to measure the debt vulnerability, the balance sheet approach (BSA) is presented in order to illustrate the potential connections between these two types of crises. A graphical analysis yields evidence that at the end 2009 the probability of observing a Euro area country defaulting is less likely than six month before. Nevertheless, the serious threats, which concern Greece and Ireland, do not permit us to exclude the occurrence of a contagious, or self-fulfilling, sovereign debt or currency crises in Euro area in the future.banking crisis, sovereign debt crisis, European Union

Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects

We develop a two step estimation procedure to estimate nonlinear panel data models. Our approach combines the “correlated random effect” and the “control function” approach to handel endogeneity of regressors that are correlated with both the unobserved heterogeneity as well as the idiosyncratic component. The novelty here lies in integrating out the unobserved heterogeneity on which the structural equations are conditioned. The integration is performed with respect to the posterior distribution of the individual effects obtained from the first stage reduced form estimation. Our framework suggests separate tests for correlation between unobserved heterogeneity and the covariates, and correlation between idiosyncratic component and the covariates. Average partial effects (APEs) of covariates are also easily obtained.

On the information value of (un)embedded network ties

A firm sets up a network of information generating alliances to reduce technological uncertainty. This alliance group creates both advantages associated with similarity of existing partners and limitations due to restricted choice of new partners. Our model analyses the conditions (technological uncertainty, information overlap, alliance search costs, and the number of previous alliances) under which a firm opts for an embedded tie within an existing network or an unembedded tie with a new partner.Strategy;

Stock Markets, Banks and Long Run Economic Growth: A Panel Cointegration-Based Analysis

The aim of this paper is to investigate the long run relationship between the development of banks and stock markets and economic growth. We make use of the Groen and Kleibergen (2003) panel cointegration methodology to test the number of cointegrating vectors among these three variables for 5 developing countries. In addition, we test the direction of potential causality between financial and economic development. Our results conclude to the existence of a single cointegrating vector between financial development and growth and of causality going from financial development to economic growth. We find little evidence of reverse causation as well as bi-directional causality.

On the Univariate Representation of Multivariate Volatility Models with Common Factors

First, we investigate the minimal univariate representation of some well known n dimensional conditional volatility models. Simple systems (e.g. a VEC(0,1)) for the joint behaviour of several variables imply individual processes with a lot of persistence in the form of long order lags. We show that in the presence of factors, parsimonious univariate representations (e.g. GARCH(1,1)) can result from large multivariate models generating the conditional variances and conditional correlations. Second, we propose an approach to use empirical results for these univariate processes in the analysis of the underlying multivariate, possibly high-dimensional, GARCH process. We use reduced rank procedures to discriminate between a system with seemingly unrelated assets (e.g. a diagonal model) from a set of series with few common sources of volatility. Among the analyzed procedures, the cannonical correlation test statistics on logs of squared returns proposed by Engle and Marcucci (2006) has quite good properties even in the case of falsely omitted cross-moments. Out of 30 returns from the NYSE, six returns are shown to display a parsimonious GARCH(1,1) model for their conditional variance. We do not reject the hypothesis that a single common volatility factor drives these six series.financial economics and financial management ;

Common intraday periodicity

Using a reduced rank regression framework as well as information criteria we investigate the presence of commonalities in the intraday periodicity, a dominant feature in the return volatility of most intraday financial time series. We find that the test has little size distortion and reasonable power even in the presence of jumps. We also find that only three factors are needed to describe the intraday periodicity of thirty US asset returns sampled at the 5-minute frequency. Interestingly, we find that for most series the models imposing these commonalities deliver better forecasts of the conditional intraday variance than those where the intraday periodicity is estimated for each asset separately.financial economics and financial management ;

Testing for Common Cyclical Features in Nonstationary Panel Data Models

In this paper we extend the concept of serial correlation common features to panel data models. This analysis is motivated both by the need to develop a methodology to systematically stu dy and test for common structures and comovements in panel data with autocorrelation present and by an increase in efficiency coming from pooling procedures. We propose sequential testing procedures and study their properties in a small scale Monte Carlo analysis. Finally, we apply the framework to the well known permanent income hypothesis for 22 OECD countries, 1950-1992.Panel data, serial correlation common features, permanent income

Macro-panels and Reality

This note argues that large VAR models with common cyclical feature restrictions provide an attractive framework for parsimonious implied univariate final equations, justifying on the one hand the estimation of homogenous panels with dynamic heterogeneity and a common factor structure, and on the other hand the aggregation of time series. However, starting with a too restrictive DGP might preclude from looking at interesting empirical issues.Economics (Jel: A)

Panel Unit Root Tests in the Presence of Cross-Sectional Dependencies: Comparison and Implications for Modelling

Several panel unit root tests that account for cross section dependence using a common factor structure have been proposed in the literature recently, notably Pesaran (2003), Moon and Perron (2004) and Bai and Ng (2004). This paper is aimed at comparing these three proposed unit root tests for panels with dynamic factors. It makes fourcontributions: (1) it compares the three testing procedures in terms of similarities and difference in the data generation process, tests, null and alternative hypotheses considered,(2) it compares the small sample properties of the tests usingMonte Carlo results in models with up to two common factors, (3) it provides an application which illustrates the use of the tests, and (4) finally it discusses the use of the tests in modelling in general. The main conclusions are: Pesaran’s (2003) cross-sectionally augmented (CA)DF tests are designed for cases where cross-sectional dependence is due to a single factor. The Moon and Perron (2004) tests which use defactored data is similar in spirit but can account for mutiple common factors. The Bai and Ng (2004) tests allow to tests for unit roots in the common factors and/or the idiosyncratic factors. It would therefore be natural to use the Pesaran (2003) or Moon and Perron tests in a first step to find out whether there are unit roots in the data. Then in a second step of modelling, the Bai and Ng (2004) tests could be used to determine whether the unit roots arise in the common factors or in the idiosyncratic components. It is also found that the latter behave well when the observed nonstationarity in the data series comes exclusively from nonstationary common factors, e.g. when the series cointegrate along the cross sectional dimension of the panel.econometrics;