This paper proposes a new class of HAC covariance matrix estimators. The standard HAC estimation method re-weights estimators of the autocovariances. Here we initially smooth the data observations themselves using kernel function based weights. The resultant HAC covariance matrix estimator is the normalised outer product of the smoothed random vectors and is therefore automatically positive sem...

This paper presents a novel characterization of continuous time processes that captures the primary idiosyncratic features of many financial time series. We extend the analysis of unit root behaviors to incorporate series that have continuous sampling, but do so in such a way that the overall series does not tend towards explosive paths, as is implied by many unit root setups. In doing so, we e...

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...

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...

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...

In specifications of tastes and technology, econometricians often impose curvature globally, but monotonicity only locally or not at all. In fact monotonicity rarely is even mentioned in that literature. But without satisfaction of both curvature and monotonicity, the second order conditions for optimizing behavior fail, and duality theory fails. The resulting first order conditions, demand fun...