Approximation formulae are developed for the bias of ordinary andgeneralized Least Squares Dummy Variable (LSDV) estimators in dynamicpanel data models. Results from Kiviet (1995, 1999) are extended tohigher-order dynamic panel data models with general covariancestructure. The focus is on...

With the aim to mitigate the possibleproblem of negativity in the estimation of the conditionaldensity function, we introduce a so-called re-weightedNadaraya-Watson (RNW) estimator. The proposed RNWestimator is constructed by a slight modificationof the well-known Nadaraya-Watson...

We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e. the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic (...)

The paper considers the K-statistic, Kleibergen’s (2000) adaptation ofthe Anderson-Rubin (AR) statistic in instrumental variables regression.Compared to the AR-statistic this K-statistic shows improvedasymptotic efficiency in terms of degrees of freedom in overidentifiedmodels and yet it...

The finite sample behaviour is analysed of particular least squares (LS) andmethod of moments (MM) estimators in panel data models with individual effectsand both a lagged dependent variabIe regressor and another explanatory variabIewhich may be affected by lagged feedbacks from the dependent...

Asymptotic expansions are employed in a dynamic regression model with a unit root inorder to find approximations for the bias, the variance and for the mean squared error of theleast-squares estimator of all coefficients. It is found that in this particular context suchexpansions exist only when...

The literature that tests for U-shaped relationships using panel data, such as those between pollution and income or inequality and growth, reports widely divergent (parametric and non-parametric) empirical findings. We explain why lack of identification lies at the root of these differences. To...

The aim of the paper is to obtain confidence intervals for the tail index and high quantiles taking into account the optimal rate of convergence of the estimator. The common approach to obtaining confidence intervals presented in the literature is to use the normal distribution approximation at...

Recent models for credit risk management make use of Hidden Markov Models (HMMs). The HMMs are used to forecast quantiles of corporate default rates. Little research has been done on the quality of such forecasts if the underlying HMM is potentially mis-specified. In this paper, we focus on...

Under the condition that the observations, which come from a high-dimensional population (X,Y), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric M-estimator for the unknown...