Two-Step Estimation of the Nonlinear Autoregressive Distributed Lag Model

Jin Seo Cho (), Matthew Greenwood-Nimmo and Yongcheol Shin
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Yongcheol Shin: Univ of York

No 2019rwp-154, Working papers from Yonsei University, Yonsei Economics Research Institute

Abstract: We consider estimation of and inference on the nonlinear autoregressive distributed lag (NARDL) model, which is a single-equation error correction model that allows for asymmetry with respect to positive and negative changes in the explanatory variable(s). We show that the NARDL model exhibits an asymptotic singularity issue that frustrates efforts to derive the asymptotic properties of the single-step estimator. Consequently, we propose a two-step estimation framework, in which the parameters of the long-run relationship are estimated first using the fully-modified least squares estimator before the dynamic parameters are estimated by OLS in the second step. We show that our two-step estimators are consistent for the parameters of the NARDL model and we derive their limit distributions. We also develop Wald test statistics for the hypotheses of short-run and long-run parameter asymmetry. We demonstrate the utility of our framework with an application to postwar dividend-smoothing in the U.S.

Keywords: Nonlinear Autoregressive Distributed Lag (NARDL) Model; Fully-Modified Least Squares Estimator; Two-Step Estimation; Wald Test Statistic; Dividend-Smoothing. (search for similar items in EconPapers)
JEL-codes: C22 G35 (search for similar items in EconPapers)
Pages: 64pages
Date: 2019-12
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (3)

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