This paper evaluates complementarities of labor market institutions and the business cycle in the context of a stochastic dynamic general equililbriurn model econorny. Matching between workers and vacancies with endogenous search intensity, Nash-bargained wages, payroll taxation, and...

This paper evaluates complementarities of labor market institutions and the business cycle in the context of a stochastic dynamic general equilibrium model economy. Matching between workers and vacancies with endogenous time spent in search, Nash{bargained wages, payroll taxation, and...

We emphasize the importance of properly identifying the long-run relations underlying the monetary model of the exchange rate. The separate estimation of long-run money demands leads to a "structural" error correction equation which allows an interpretation of the various channels affecting the...

We establish a relation between stochastic volatility models and the class of generalized hyperbolic distributions. These distributions have been found to fit exceptionally well to the empirical distribution of stock returns. We review the background of hyperbolic distributions and prove...

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In this paper, we develop such a test based on the linear state space representation. We provide a...

We consider two multivariate long-memory ARCH models, which extend the univariate long-memory ARCH models, we first consider a long-memory extension of the restricted constant conditional correlations (CCC) model introduced by Bollerslev (1990), and we propose a new unrestricted conditional...

To assess the predictive content of the interest rate term spread for future economic growth, we distinguish short-run from long-run predictability by using two different approaches. First, following Dufour and Renault (1998) a test procedure is proposed to test for causality at different...