學(xué)術(shù)講座：Balanced Predictive Regressions with Cointegrated Regressors

講座題目：Balanced Predictive Regressions with Cointegrated Regressors

主講人： 易艷萍

時(shí)間：5月28日中午12:20-13:20

地點(diǎn)：博學(xué)925

主講人簡(jiǎn)介：

易艷萍，美國(guó)紐約大學(xué)經(jīng)濟(jì)學(xué)博士，上海財(cái)經(jīng)大學(xué)經(jīng)濟(jì)學(xué)院常任副教授，博士生導(dǎo)師。研究興趣主要是金融計(jì)量理論，時(shí)間序列，以及半?yún)?shù)估計(jì)。在國(guó)際學(xué)術(shù)期刊如 Annals of Statistics、Journal of Econometrics （2篇已發(fā)表，1篇 forthcoming）、Journal of Business & Economic Statistics、Economics Letters 、Economic Modelling 上共發(fā)表文章數(shù)篇。此外，她還是 Journal of Econometrics, Review of Economics and Statistics,  Journal of Financial Econometrics,   Journal of Business & Economic Statistics,   Journal of Machine Learning Research,  Journal of the Royal Statistical Society 等國(guó)際頂尖學(xué)術(shù)期刊的匿名審稿人。

講座摘要：

In the predictive regression, a less persistent return series is regressed on the first lag of some highly persistent predictors. Therefore, predictability could often be missed due to the persistence imbalance. This paper aims to balance the predictive regression by augmenting the regression with an additional lag of the predictors. This second lag generally reduces the persistence level in the right-hand side of the equation to achieve balance. We then propose a simple test procedure for the univariate and the multivariate predictive regressions, based on least squares estimation. Empirically, we reexamine the popular predictors in the literature, and find quite different results.