Likelihood-ratio-based Confidence Intervals for Multiple Threshold Parameters

19 Pages Posted: 19 Apr 2023

See all articles by Luiggi Donayre

Luiggi Donayre

University of Minnesota - Duluth - Department of Economics and Health Care Management

Date Written: April 5, 2023

Abstract

This paper proposes the inversion of likelihood ratio tests for the construction of confidence intervals for multiple threshold parameters. Using Monte Carlo simulations, conservative likelihood-ratio-based confidence intervals are shown to exhibit empirical coverage rates at least as high as nominal levels for all threshold parameters, while still being informative in the sense of only including relatively few observations in each confidence interval. These findings are robust to the magnitude of the threshold effect, the sample size and the presence of serial correlation. Applications to existing models with multiple thresholds for U.S. real GDP growth and for the wage Phillips curve demonstrate how the proposed approach is empirically relevant to make inferences about the uncertainty of threshold estimates.

Keywords: Confidence Intervals, Multiple-regime threshold regression, Likelihood Ratio, Monte Carlo Simulations.

JEL Classification: C15, C24, E32

Suggested Citation

Donayre, Luiggi, Likelihood-ratio-based Confidence Intervals for Multiple Threshold Parameters (April 5, 2023). Available at SSRN: https://ssrn.com/abstract=4399691 or http://dx.doi.org/10.2139/ssrn.4399691

Luiggi Donayre (Contact Author)

University of Minnesota - Duluth - Department of Economics and Health Care Management ( email )

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