This note shows that two ways of simulation based bias correction–indirect inference and bootstrap bias correction–are equivalent for two-stage-least-squares, as well as k-class estimators for the standard linear model with endogenous regressors.

Ng (2008) shows how the cross-sectional variance of the observed panel data can be used to construct a simple test for the proportion of non-stationary units. However, in the case with incidental trends the test is distorted. The present note shows how the distortions can be substantially...

In this note we extend the method proposed in Bun and Carree (2006) to the more general PVARX(1) model and show that the iterative procedure is not consistent for fixed T. Subsequently we provide corrected version of the bias correction procedure which is fixed T consistent and robust to both...

This paper considers a factor-augmented regression model in the presence of structural change. We propose a two-step procedure to estimate the coefficients of explanatory variables. We show that when the number of units (N) and the number of periods (T) are large and comparable, the proposed...

We estimate the long rate and its volatility within the Svensson framework. The procedure that best extrapolates the longest observable rate and its volatility is a 2-dimensional grid search conditioned on the ridge regression suggested by Annaert et al. (2013).

We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.

This paper extends the classical work of bipower variation by allowing the return process to be autocorrelated. We propose a method of estimating the return volatility when the price process is described by a fractal Brownian motion with jumps.

This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. The quantile coupling allows one to apply the standard Gaussian-based estimation and inference to the transformed data set. The resulting estimator is asymptotically normal with a...

In this note, I extend the optimal asymptotic least squares estimation framework to deal with singularities in the asymptotic covariance of the distance function. Further, the relationship between the asymptotic least squares and maximum likelihood estimation frameworks in such a singular set-up...

This paper investigates the quasi-maximum likelihood (QML) estimation of spatial panel data models where spatial weights matrices can be time varying. We show that QML estimate is consistent and asymptotically normal. We also derive the asymptotic distribution of average impact coefficients...