Using constrained optimization, we develop a simple, efficient approach (applicable in both unconstrained and constrained maximum-likelihood estimation problems) to computing profile-likelihood confidence intervals. In contrast to Wald-type or score-based inference, the likelihood ratio...

The distributional specifications for the composite regression error term most often used in Stochastic Frontier Analysis (SFA) are inherently bounded as regards their skewness and excess kurtosis coefficients. These bounds provide simple diagnostic tools and model selection/rejection criteria...

In this paper it is proposed to use a non-parametric bootstrap based Bartlett correction factor for the LR test for linear restrictions on the cointegrating vectors to reduce the finite sample size distortion problem of the test statistic

We present a portable model of distorted learning which embodies Tversky and Kahneman's (1971) “belief in the law of small numbers.” When adjusting beliefs in response to new information the decision maker overweights the sample, updating as if the sample size were inflated. The degree of...

We characterize time-varying disaster risk using interbank rates and their options. The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We make an identification assumption that macroeconomic disasters coincide with banking...

This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for high dimensional nonlinear factor models, with slightly stronger conditions on the relative magnitude of N(number of subjects) and T(number of time periods). Factors and loadings are estimated by maximum...

In this paper, we study the statistical properties of heterogeneous agent models with incomplete markets. Using a Bewley-Hugget-Aiyagari model we compute the equilibrium density function of wealth and show how it can be used for likelihood inference. We investigate the identifiability of the...