Parameter regimes in partial functional panel regression

Dominik Liebl and Fabian Walders

Econometrics and Statistics, 2019, vol. 11, issue C, 105-115

Abstract: A new partial functional linear regression model for panel data with time varying parameters is introduced. The parameter vector of the multivariate model component is allowed to be completely time varying while the function-valued parameter of the functional model component is assumed to change over K unknown parameter regimes. Consistency is derived for the suggested estimators and for the classification procedure used to detect the K unknown parameter regimes. Additionally, the convergence rates of the estimators are derived under a double asymptotic differentiating between asymptotic scenarios depending on the relative order of the panel dimensions n and T. The statistical model is motivated by a real data application considering the so-called “idiosyncratic volatility puzzle” using high frequency data from the S&P500.

Keywords: Functional data analysis; Mixed data; Partial functional linear regression model; Classification; Idiosyncratic volatility puzzle (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:11:y:2019:i:c:p:105-115

DOI: 10.1016/j.ecosta.2018.05.003

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