Understanding adjustment processes has become central in economics. Empirical analysis is fraught with the problem that the target is usually unobserved. This paper develops, simulates and applies GMM methods for estimating dynamic adjustment models in a panel data context with partially...

This paper sets out to help explain why estimates of asset correlations based on equity prices tend to be considerably higher than estimates based on default rates. Resolving this empirical puzzle is highly important because, firstly, asset correlations are a key driver of credit risk and,...

What does the saving-investment (SI) relation really measure and how should the (SI) relation be measured? These are two of the most discussed issues triggered by the so called Feldstein-Horioka puzzle. Based on panel data we introduce a new variant of functional coefficient models that allow to...

We introduce a new, factor based bootstrap approach which is robust under heteroskedastic error terms for inference in functional coefficient models. Modeling the functional coefficient parametrically, the bootstrap approximation of an F statistic is shown to hold asymptotically. In simulation...

Recent empirical evidence suggests that reversing current account balances imply costly adjustment processes leading to reduced economic growth. Using large panel data sets to analyze determinants and costs of reversals asks for controls of heterogeneity among countries. This paper contributes a...

In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models. It is shown in particular that the standard autoregressive specification used in the literature can be interpreted as a latent common factor model. However, this specification...

We study the modelling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalising the volatility in high...

Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy tailed distributions. We show that the recently proposed MAVE and OPG methods by Xia et al. (2002) allow us to make them robust in a relatively straightforward way...

A definition of selfinformative Bayes carriers or limits is given as a description of an approach to noninformative Bayes estimation in non- and semiparametric models. It takes the posterior w.r.t. a prior as a new prior and repeats this procedure again and again. A main objective of the paper...

In this paper we study time-varying coefficient models with time trend function and serially correlated errors to characterize nonlinear, nonstationary and trending phenomenon in time series. Compared with the Nadaraya-Watson method, the local linear approach is developed to estimate the time...