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Inference of the Trend in a Partially Linear Model with Locally Stationary Regressors

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  • Kun Ho Kim

Abstract

In this article, we construct the uniform confidence band (UCB) of nonparametric trend in a partially linear model with locally stationary regressors. A two-stage semiparametric regression is employed to estimate the trend function. Based on this estimate, we develop an invariance principle to construct the UCB of the trend function. The proposed methodology is used to estimate the Non-Accelerating Inflation Rate of Unemployment (NAIRU) in the Phillips Curve and to perform inference of the parameter based on its UCB. The empirical results strongly suggest that the U.S. NAIRU is time-varying.

Suggested Citation

  • Kun Ho Kim, 2016. "Inference of the Trend in a Partially Linear Model with Locally Stationary Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1194-1220, August.
    • Handle: RePEc:taf:emetrv:v:35:y:2016:i:7:p:1194-1220
    DOI: 10.1080/07474938.2014.976530
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    References listed on IDEAS

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    1. Andrés González & Kirstin Hubrich & Timo Teräsvirta, 2009. "Forecasting inflation with gradual regime shifts and exogenous information," CREATES Research Papers 2009-03, Department of Economics and Business Economics, Aarhus University.
    2. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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