| 概要 |
本発表では、発表者の近年の研究を3件紹介する。
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Identification of Regression Models with a Misclassified and Endogenous Binary Regressor
We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error.
We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate are present.
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Inference in Predictive Quantile Regressions
This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root.
We propose a switching-fully modified predictive test for quantile predictability with persistent regressors.
We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors —the dividend yield, earnings price ratio, and book to market ratio— to predict the median, shoulders, and tails of the stock return distribution.
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Testing the Number of Regimes in Markov Regime Switching Models
Markov regime switching models have been used in numerous empirical studies in economics and finance.
However, the asymptotic distribution of the likelihood ratio test statistic for testing the number of regimes in Markov regime switching models has been an unresolved problem.
This paper derives the asymptotic distribution of the likelihood ratio test statistic for testing the null hypothesis of M regimes against the alternative hypothesis of M+1 regimes for any M ≥ 1 both under the null hypothesis and under local alternatives.
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