In this paper we investigate the effects of new railway stations on house prices using an extensive repeated sales dataset over a period of 13 years. We employ semiparametric panel data techniques allowing for anticipation effects of station openings. We show that a kilometre reduction in...

Even though economic models have been relatively successful in explaining the long run patterns of house prices, they have more difficulties in explaining short run developments of the housing markets. However, the fact that during such ‘bubbles’ the spatial pattern of house prices, which...

Since the beginnings of the eighties house prices in the Netherlands have increased steadily and considerably. In this paper we study the effect of this development on the demand for second mortgages and on the savings of Dutch households. We use the data of the Dutch socio-economic panel for...

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this article

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this article it is argued that one...

We extend the class of dynamic factor yield curve models for the inclusion of macro-economic factors. We benefit from recent developments in the dynamic factor literature for extracting the common factors from a large panel of macroeconomic series and for estimating the parameters in the model....

This paper conducts an empirical analysis of the heterogeneity of recessions in monthly U.S. coincident and leading indicator variables. Univariate Markovswitching models indicate that it is appropriate to allow for two distinct recession regimes, corresponding with ‘mild’ and ‘severe’...

We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the...

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...

We solve for the optimal portfolio allocation in a setting where both conditional correlation and the