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来源类型Working Paper
规范类型报告
DOI10.3386/w28568
来源IDWorking Paper 28568
Consistent Inference for Predictive Regressions in Persistent Economic Systems
Torben G. Andersen; Rasmus T. Varneskov
发表日期2021-03-22
出版年2021
语种英语
摘要We study standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all – or a subset – of the variables may be fractionally integrated, which induces a spurious regression problem. We propose a new inference and testing procedure – the Local speCtruM (LCM) approach – for joint significance of the regressors, that is robust against the variables having different integration orders and remains valid regardless of whether predictors are significant and if they induce cointegration. Specifically, the LCM procedure is based on fractional filtering and band-spectrum regression using a suitably selected set of frequency ordinates. Contrary to existing procedures, we establish a uniform Gaussian limit theory and a standard χ2-distributed test statistic. Using LCM inference and testing techniques, we explore predictive regressions for the realized return variation. Standard least squares inference indicates that popular financial and macroeconomic variables convey valuable information about future return volatility. In contrast, we find no significant evidence using our robust LCM procedure. If anything, our tests support a reverse chain of causality: rising financial volatility predates adverse innovations to macroeconomic variables. Simulations illustrate the relevance of the theoretical arguments for finite-sample inference.
主题Financial Economics ; Portfolio Selection and Asset Pricing ; Financial Markets
URLhttps://www.nber.org/papers/w28568
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/586242
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GB/T 7714
Torben G. Andersen,Rasmus T. Varneskov. Consistent Inference for Predictive Regressions in Persistent Economic Systems. 2021.
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