This article tests the linearity assumption underlying the popular heterogeneous autoregressive model for realized volatility (HAR-RV). We implement a consistent model specification test that is robust to both distributional and model misspecification. We find that, using a nonparametric HAR-RV...

This paper shows the inconsistency of three forms of 2SLS estimators to illustrate the specialty of the endogeneity problem in threshold regression.

This paper shows a potential pitfall of the exponential tilting (ET) estimator under misspecification. We show that the pseudo-true value of the ET estimator is not identified if the true distribution is not absolutely continuous with respect to the probability measures implied by the moment...

A simple graphical approach to presenting results from nonlinear regression models is described. In the face of multiple covariates, ‘partial mean’ plots may be unattractive. The approach here is portable to a variety of settings and can be tailored to the specific application at hand. A...

Estimating binary choice models with endogeneity is of considerable importance in microeconometrics. The leading control function approach does not apply when the endogenous variable is binary. We propose a multi-stage estimation procedure for a heteroscedastic binary choice model with an...

This paper investigates the Estimating Function method in the context of ACD modelling and appraises the properties of these estimates. A simulation study is conducted to demonstrate that these estimates are more efficient than the corresponding ML and QML estimates.

This study investigates the identification of parameters in semiparametric binary response models of the form y=1(x′β+v+ε0) when there are nonignorable nonresponses. We propose an estimation procedure for the identified set, the set of parameters that are observationally indistinguishable...

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...

We develop a new way to incorporate prior information within an Information-Theoretic (IT) estimation framework. The estimator considers many potential priors and uses a simple statistic to choose the optimal solution. Our method outperforms its competitors for all finite data.