Alain Hecq

Permanent‐transitory Decomposition in Var Models With Cointegration and Common Cycles

In this paper we derive permanent‐transitory decompositions of non‐stationary multiple times series generated by (r)nite order Gaussian VAR(p) models with both cointegration and serial correlation common features. We extend existing analyses to the two classes of reduced rank structures discussed in Hecq, Palm and Urbain (1998). Using the corresponding state space representation of cointegrated VAR models in vector error correction form we show how decomposition can be obtained even in the case where the number of common feature and cointegration vectors are not equal to the number of variables. As empirical analysis of US business fluctuations shows the practical relevance of the approach we propose.

On non-contemporaneous short-run co-movements

Abstract This paper proposes the notion of polynomial serial correlation common features as a measure of non-contemporaneous cyclical co-movements in multiple time series. Statistical inference within this modeling is easily performed by reduced rank regression. We show the implications of the PSCCF in terms of the Beveridge–Nelson decomposition and we illustrate their relevance for empirical analyses.

Separation, Weak Exogeneity and P-T Decomposition in Cointegrated VAR Systems with Common Features

ABSTRACT The aim of this paper is to study the concept of separability in multiple nonstationary time series displaying both common stochastic trends and common stochastic cycles. When modeling the dynamics of multiple time series for a panel of several entities such as countries, sectors, firms, imposing some form of separability and commonalities is often required to restrict the dimension of the parameter space. For this purpose we introduce the concept of common feature separation and investigate the relationships between separation in cointegration and separation in serial correlation common features. Loosely speaking we investigate whether a set of time series can be partitioned into subsets such that there are serial correlation common features within the sub-groups only. The paper investigates three issues. First, it provides conditions for separating joint cointegrating vectors into marginal cointegrating vectors as well as separating joint short-term dynamics into marginal short-term dynamics. Second, conditions for making permanent-transitory decompositions based on marginal systems are given. Third, issues of weak exogeneity are considered. Likelihood ratio type tests for the different hypotheses under study are proposed. An empirical analysis of the link between economic fluctuations in the United States and Canada shows the practical relevance of the approach proposed in this paper.

Does seasonal adjustment induce common cycles

Abstract In this note we analyze via Monte Carlo simulations how serial correlation common features test statistics behave when X-11 seasonal adjusted data are encountered. We emphasize both size and power distortions. We illustrate the analysis on Japanese consumption/income relationship.

Assessing a Perfect European Optimum Currency Area: A Common Cycles Approach

In this paper we introduce a new definition for an optimum currency area (OCA) which is more restrictive than the previous ones. Indeed, using both a cointegration and a common cyclical feature analysis in a VAR(p)framework, a set of countries is said to constitute a perfect OCA if theshort-run dynamics is perfectly correlated while long-run relationships arenot constrained. Using seasonally unadjusted industrial production indicesfor the period 75:M1 to 97:M4, we show that European countries are notsufficiently related to fit our definition.

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.

Inference in codependence: some Monte Carlo results and applications

In this paper, we investigate through Monte Carlo simulations the behavior of the codependence testing procedure (Gourieroux et Peaucelle [1989]) in small samples and in various usual statistical situations. Our results suggest that, except for the pure MA(q) case, important power losses may occur. The simulation results are illustrated by an analysis of Okuns law conducted for the main OECD countries.

Codependence and convergence in the EC economies

Abstract The objective of this paper is to get some insight into the process of real convergence between European countries. Emphasizing the stationary context of the data and the importance of synchronization of the real performances with Germany, we propose a new concept of convergence based on the notion of codependence introduced by Gourieroux and Peaucelle (1989) . Our empirical analysis produces some interesting results, which do not reject the hypothesis that some degree of the erosion of the credibility for some currencies during the period of the new EMS may be due to the emergence of real divergences with Germany.

IGARCH effect on autoregressive lag length selection and causality tests

Using Monte Carlo experiments, we show how information criteria determine, in the presence of GARCH errors, an optimal lag length in univariate time series and causality tests. We illustrate the simulations by testing the presence of serial correlation in exchange rates as well as Granger-causality between interest rates.

On the Univariate Representation of BEKK Models with Common Factors

Abstract Simple low order multivariate GARCH models imply marginal processes with a lot of persistence in the form of high order lags. This is not what we find in many situations however, where parsimonious univariate GARCH(1,1) models for instance describe quite well the conditional volatility of some asset returns. In order to explain this paradox, we show that in the presence of common GARCH factors, parsimonious univariate representations can result from large multivariate models generating the conditional variances and conditional covariances/correlations. The diagonal model without any contagion effects in conditional volatilities gives rise to similar conclusions though. Consequently, after having extracted a block of assets representing some form of parsimony, remains the task of determining if we have a set of independent assets or instead a highly dependent system generated with a few factors. To investigate this issue, we first evaluate a reduced rank regressions approach for squared returns that we extend to cross-returns. Second we investigate a likelihood ratio approach, where under the null the matrix parameters have a reduced rank structure. It emerged that the latter approach has quite good properties enabling us to discriminate between a system with seemingly unrelated assets (e.g. a diagonal model) and a model with few common sources of volatility.