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...

In this paper, we build on Ryan and Wales (1998), Moschini (1999), and Serletis and Shahmoradi (2007) and impose curvature conditions locally on the quadratic Almost Ideal Demand System (AIDS) model of Banks et al. (1997), an extension of the simple AIDS model of Deaton and Muellbauer (1980)...

Estimation of the non-linear Constant Elasticity of Scale (CES) function is generally considered problematic due to convergence problems and unstable and/or meaningless results. These problems often arise from a non-smooth objective function with large flat areas, the discontinuity of the CES...

In this note, we argue that tests of overidentifying restrictions give little information on the validity of the moment conditions implied by the underlying economic model, and therefore are mute about the possibility of identifying the parameters of interest.

We highlight a subtle identification problem that afflicts models for non-negative data in which the conditional expectation is specified as the product of a logit and an exponential function. The results are illustrated with an empirical model for medical expenditures.

We propose a test for the evaluation of statistical acceptability of a functional constraint which is imposed on parameters in the mixed data sampling regressions. The asymptotic behavior of the test statistic is characterized and a few other extensions are discussed.

In the aftermath of the financial crisis, this study investigates which underlying determinants cause bank rating transitions. We develop survival analysis models to explain credit transition hazards using macroeconomic factors and the rating history. We find that there exists a significant...

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.