The labor-supply elasticity is a central element in many macroeconomic models. We argue that assumptions underlying previous econometric estimates of the intertemporal labor supply elasticity are inconsistent with incomplete markets economies. In particular, if the econometrician ignores...

The labor-supply elasticity is a central element in many macroeconomic models. We argue that assumptions underlying previous econometric estimates of the intertemporal labor supply elasticity are inconsistent with incomplete markets economies. In particular, if the econometrician ignores...

In this paper we derive conditions for the conditional covariance matrix to be positive definite in a general vector ARCH model. The conditions can be easily extended to the diagonal vector GARCH model. For the general vector GARCH model, analytical expressions for the conditions in terms of the...

We propose a seasonal cointegration model [SECM] for quarterly data which includes variables with different numbers of unit roots and thus needs to be transformed in different ways in order to yield stationarity. A Monte Carlo simulation is carried out to investigate the consequences of...

This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes semiparametric and nonparametric GARCH models. Existing specification and misspecification tests are discussed. Finally, there is an empirical...

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The approach adopted here is based on the decomposition of the covariances into correlations and standard deviations. The time-varying conditional correlations change smoothly between two...

In this paper we propose a Lagrange multiplier test for volatility interactions among markets or assets. The null hypothesis is the Constant Conditional Correlation GARCH model in which volatility of an asset is described only through lagged squared innovations and volatility of its own. The...

Bivariate VAR models are Monte Carlo simulated and OLS estimated, The resulting biases are used to compare two alternative approximations to the bias. They are found to be equivalent for first-order models, whereas for second-order models Nicholls and Pope's approximation outperforms Tjostheim...

Using a microbased superpopulation approach some aspects of optimal prediction of aggregated AR(1) processes are studied.

The concept of common factors has in the econometrics literature been applied to conditional means or in some cases to conditional variances. In this paper we generalize this concept to bivariate distributions. This is done using the conditional bivariate copula as the statistical tool. The...