아래와 같이 계량 세미나 일정을 알려드립니다.
We are going to have an econometric seminar as follows.
– Speaker: Prof. Peter M. Robinson, London School of Economics
– Time: 2017.9.8.(Fri) 5:30-6:30 PM
– Place: 고려대학교 정경관 509A (PSEB 509A)
– Title: Inference on Trending Panel Data (joint with Carlos Velasco)
Abstract: Semiparametric panel data modelling and statistical inference with fractional stochastic trends, nonparametrically time-trending individual effects, and general spatial or cross-sectional correlation and heteroscedasticity in innovations are developed. The fractional stochastic trends allow for a wide range of nonstationarity, indexed by a memory parameter, nesting the familiar I(1) case and allowing for parametric short-memory. The individual effects can nonparametrically vary simultaneously across time and across units. The spatial or cross-sectional covariance matrix is also nonparametric. The main focus is on estimation of the time series parameters. Two methods are considered, both of which entail an only approximate differencing out of the individual effects, leaving an error which has to be taken account of in our theory. In both cases we obtain standard asymptotics, with a central limit theorem, over a wide range of possible parameter values, unlike the nonstandard asymptotics for autoregressive parameter estimates at a unit root. For statistical inference, consistent estimation of the limiting covariance matrix of the parameter estimates requires consistent estimation of a functional of the cross-sectional covariance matrix. We examine efficiency loss due to spatial correlation. A Monte Carlo study of finite-sample performance is included.
아래와 같이 계량 세미나 일정을 알려드립니다.
We are going to have an econometric seminar as follows.
– Speaker: Prof. Peter M. Robinson, London School of Economics
– Time: 2017.9.8.(Fri) 5:30-6:30 PM
– Place: 고려대학교 정경관 509A (PSEB 509A)
– Title: Inference on Trending Panel Data (joint with Carlos Velasco)
Abstract: Semiparametric panel data modelling and statistical inference with fractional stochastic trends, nonparametrically time-trending individual effects, and general spatial or cross-sectional correlation and heteroscedasticity in innovations are developed. The fractional stochastic trends allow for a wide range of nonstationarity, indexed by a memory parameter, nesting the familiar I(1) case and allowing for parametric short-memory. The individual effects can nonparametrically vary simultaneously across time and across units. The spatial or cross-sectional covariance matrix is also nonparametric. The main focus is on estimation of the time series parameters. Two methods are considered, both of which entail an only approximate differencing out of the individual effects, leaving an error which has to be taken account of in our theory. In both cases we obtain standard asymptotics, with a central limit theorem, over a wide range of possible parameter values, unlike the nonstandard asymptotics for autoregressive parameter estimates at a unit root. For statistical inference, consistent estimation of the limiting covariance matrix of the parameter estimates requires consistent estimation of a functional of the cross-sectional covariance matrix. We examine efficiency loss due to spatial correlation. A Monte Carlo study of finite-sample performance is included.