In this paper we argue that authorities aid cooperation by means ofdirect coordination or the enforcement of re-commitment devices suchas contract laws.Credible threats of violence allow this role. In alocal interaction model, an authority forms if mutually connected individuals with sufficient...

In this paper, I consider the identification of lagged durationdependence in multiple spells without using the assumtion that there are additionalregressors orthogonal to the individual effects. The non-parametricidentification strategy is applied to the multiple non-employment spells of 2066...

In this paper we attempt to explain individual, ordinally comparable,satisfaction levels. We postulate a simultaneous equation model where general satisfaction isexplained by exogenous shock and level variables, and by the values of the satisfactionswith respect to six distinct endogenous...

We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of...

We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

We propose a multiplicative dynamic factor structure for the conditional modelling of the variances of an N-dimensional vector of financial returns. We identify common and idiosyncratic conditional volatility factors. The econometric framework is based on an observation-driven time series model...