Econometric Theory· 2026Q1
ESTIMATION AND INFERENCE OF NONLINEAR AUTOREGRESSIVE DISTRIBUTED LAG MODELS WITH TIME TREND BY ORDINARY LEAST SQUARES
- 0citations
- Q1SCImago
- 2026year
Short summary
Ordinary Least Squares (OLS) is shown to be a consistent estimator for nonlinear autoregressive distributed lag (NARDL) models with time trends, even with nonstationary data.
AI-generated from the title and abstract; the full text is not read.
Key points
- OLS estimator is consistent for correctly specified NARDL models with time trends, even with nonstationary data.
- The OLS estimator asymptotically follows a mixed normal distribution.
- Standard Wald test for asymmetries in NARDL parameters is analyzed, with a super-consistent estimator proposed for enhancement.
- Application to U.S. GDP and fiscal shock data shows statistical evidence of long- and short-run symmetries between tax increase/decrease effects.
AI-generated from the title and abstract; the full text is not read.
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 standard Wald 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. 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 the 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.
The authors' abstract, as published at the source. Econometric Theory, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: General Economics, Econometrics and Finance
General Economics, Econometrics and FinanceEconomics, Econometrics and Finance