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When no variables are observed for endogenous non-respondents of panel data, bias correction is available only for a limited class of instrumental variable estimators, which require strong conditions for consistency and often suffer from substantial efficiency loss. In this paper we examine a convenient alternative method of imputing the missing explanatory variables and then using standard bias-correction procedures for sample selection. Various bias-corrected estimators are derived and their performances are compared by Monte Carlo experiments. Results verify efficiency loss by the instrumental variable estimators and suggest that the imputation method is practically useful if it is applied to first-difference regression.

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Beginning with the seminal work of Manski (1990), there has been a growing literature on estimation and inference on partially identifiable parameters, including the distribution and/or quantile functions of the heterogeneous treatment effect. This article applies and extends the bounding approaches that Williamson and Downs (1990) and Fan and Park (2010, 2012) to partially identify distribution of treatment effects of class size reduction (CSR). Empirical data I used are from the Project STAR. Conducted by Tennessee State Department of Education in 1985-1988, it was a large-scale, randomized experiment designed to investigate the effect of CSR on student performance.

As an extension of the bounding approach that Fan and Park (2010) used, I proposed bounds for the conditional probability distribution function of treatment effects on pre-treatment outcomes. Although it was hard to find definitive properties of the conditional distribution due to the nature of bounding approach, I find the approach is insightful and has a potential.

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This paper studies the price elasticity of the peak electricity demand
of the residential sector in Korea using high frequency data collected by AMR (Automatic Meter Reading) system. The main purpose of this paper is to estimate the price elasticity by allowing the nonlinear relationship between price and temperature in the short-run residential electricity demand curve. Specifically, we consider a Logistic Smooth Transition Regression model with functional coefficients to capture the temperature-dependent price elasticity of residential peak demand in Korea. We show conclusive evidence that the non-economic variables influence the price elasticity of peak residential demand in Korea. Our estimation results show that the price elasticity is dependent upon temperature, and peak demand becomes more sensitive when the weather is very hot or cold.