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Data-driven local polynomial for the trend and its derivatives in economic time series

Yuanhua Feng () and Thomas Gries

No 102, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: The main purpose of this paper is the development of iterative plug-in algorithms for local polynomial estimation of the trend and its derivatives in macroeconomic time series. In particular, a data-driven lag-window estimator for the variance factor is proposed so that the bandwidth is selected without any parametric assumption on the stationary errors. Further analysis of the residuals using an ARMA model is discussed briefl y. Moreover, confidence bounds for the trend and its derivatives are conducted using some asymptotically unbiased estimates and applied to test possible linearity of the trend. These graphical tools also provide us further detailed features about the economic development. Practical performance of the proposals is illustrated by quarterly US and UK GDP data.

Keywords: Macroeconomic time series; semiparametric modelling; nonparametric regression with dependent errors; bandwidth selection; misspecification test (search for similar items in EconPapers)
Pages: 25 pages
Date: 2017-04
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Data-driven local polynomial for the trend and its derivatives in economic time series (2020) Downloads
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