We study semiparametric efficiency bounds and efficient estimation of parameters defined through general nonlinear, possibly non-smooth and over-identified moment restrictions, where the sampling information consists of a primary sample and an auxiliary sample. The variables of interest in the...

This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run...

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias...

A standard approach to estimating structural parameters in life‐cycle models imposes sufficient assumptions on the data to identify the “age profile” of outcomes, then chooses model parameters so that the model's age profile matches this empirical age profile. I show that this approach is...

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the framework of...

This paper is concerned with the estimation of nonlinear SUR models with additive AR(1) disturbances using panel data. We propose a transformation which eliminates auto-correlation for the whole system and yields a classical SUR-EC model. We present a general class of minimum distance estimators...

The problem of multicollinearity in the assessments of coefficients is well established. However, it is rarely researched in the estimations of macroeconomic variables and economic performance of developing countries. Predicatively, it has impacts on the estimations of coefficients that should...

We introduce an estimation method that applies to a class of multivariate regression problems. The method can estimate parameters that are subject to multiple reduced-rank conditions and other parameter restrictions and the method allows for a general specifications of the covariance matrix. We...

While 2SLS is the most widely used estimator for simultaneous equation models, OLS may do better in finite samples. Here we demonstrate analytically that for the widely used simultaneous equation model with one jointly endogenous variable and valid instruments, 2SLS has smaller MSE error, up to...

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov Chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the...