JinSeo.Cho - Estimation and Inference of Nonlinear Autoregressive Distributed Lag Models by Ordinary Least Squares

<Abstract> This study examines the large-sample behavior of an ordinary least squares (OLS) estimator within a correctly specified nonlinear autoregressive distributed lag (NARDL) model for nonstationary data. Although the OLS estimator suffers from an asymptotically singular matrix problem, it remains consistent for unknown model parameters and asymptotically follows a mixed normal distribution. Additionally, we examine the large-sample behavior of the standardWald test, as defined by the OLS estimator, for asymmetries in long- and short-run NARDL parameters, and enhance this analysis with a super-consistent long-run parameter estimator so that parameters targeted by the OLS and the two-step NARDL estimators can be estimated at the same convergence rate. We then confirm the theory on the Wald test using Monte Carlo simulations. Finally, using U.S. GDP and exogenous fiscal shock data, we demonstrate use of the OLS estimator and show statistical evidence of long- and short-run symmetries between the effects of tax increases and decreases on U.S. GDP