Inference on a Semiparametric Model with Global Power Law and Local Nonparametric Trends

Jiti Gao, Oliver Linton and Bin Peng (bin.peng@monash.edu)

No 10/17, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper studies a model with both a parametric global trend and a nonparametric local trend. This model may be of interest in a number of applications in economics, finance, ecology, and geology. The model nests the parametric global trend model considered in Phillips (2007) and Robinson (2012), and the nonparametric local trend model. We first propose two hypothesis tests to detect whether either of the special cases are appropriate. For the case where both null hypotheses are rejected, we propose an estimation method to capture both aspects of the time trend. We establish consistency and some distribution theory in the presence of a large sample. Moreover, we examine the proposed hypothesis tests and estimation methods through both simulated and real data examples. Finally, we discuss some potential extensions and issues when modelling time effects.

Keywords: global mean sea level; nonparametric kernel estimation; nonstationarity. (search for similar items in EconPapers)
JEL-codes: C14 C22 Q54 (search for similar items in EconPapers)
Pages: 55
Date: 2017
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: INFERENCE ON A SEMIPARAMETRIC MODEL WITH GLOBAL POWER LAW AND LOCAL NONPARAMETRIC TRENDS (2020)
Working Paper: Inference on a semiparametric model with global power law and local nonparametric trends (2018)
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