In this paper we propose an inferential procedure for transformation models with conditional heteroskedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose a consistent estimator and show that it is asymptoticaly √ n normal. We conduct a simulation study that reve...

Consider a non-standard parametric estimation problem, such as the estimation of the AR(1) coefficient close to the unit root. We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We demonstrate the usefulness of our generic approach by also applying it ...

International organizations collect data from national authorities to create multivariate cross-sectional time series for their analyses. As data from countries with not yet wellestablished statistical systems may be incomplete, the bridging of data gaps is a crucial challenge. This paper investigates data structures and missing data patterns in the crosssectional time series framework, reviews...

We develop a stochastic frontier model with unknown locations and number of structural breaks for both efficiency and technology. The structural changes in technology and efficiency are different. It is possible to identify these two types of changes even under a short panel context. The algorithm to estimate the model is designed to avoid the potential pitfalls with panel data. Parallel comput...

This paper provides a comprehensive analysis of the short-term interest-rate dynamics based on three different data sets and two flexible parametric specifications. The significance of nonlinearity in the short-rate drift declines with increasing maturity for the interest-rate series used in the study. Using a flexible diffusion specification and incorporating GARCH volatility and non-normal in...

We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies, or have other irregularities that cause the econometrician to observe them with mildly nonstationary noise. Least squares estimation of the cointegrating vector is consistent. Existing prototypical variancebased ...

This article provides a selective review on the recent developments of some nonlinear nonparametric and semiparametric panel data models. In particular, we focus on two types of modelling frameworks: nonparametric and semiparametric panel data models with deterministic trends, and semiparametric single-index panel data models with individual effects. We also review various estimation methodolog...

The paper investigates asymptotically efficient inference in general time series likelihood models with time varying parameters. Inference procedures for general loss functions are evaluated by a weighted average risk criterion. The weight function focusses on persistent parameter paths of moderate magnitude, and is proportional to the distribution function of a Gaussian random walk. It is show...

We study the impact of nine categories of scheduled and unscheduled news announcements on the euro/ dollar return volatility. We highlight and analyze the pre-announcement, contemporaneous and postannouncement reactions. Using high-frequency intraday data and within the framework of ARCH-type models, we show that volatility increases in the pre-announcement periods, particularly before schedule...

A semiparametric extension of the GJR model (Glosten et al., 1993. Journal of Finance 48, 1779–1801) is proposed for the volatility of foreign exchange returns. Under reasonable assumptions, asymptotic normal distributions are established for the estimators of the model, corroborated by simulation results. When applied to the Deutsche Mark/US Dollar and the Deutsche Mark/British Pound daily ret...