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This article introduces a panel threshold model with covariate-dependent and time-varying thresholds and unobserved individual-specific threshold effects (PTCDI). We develop methods for estimation and inference for threshold parameters in the proposed PTCDI model by employing the correlated random effects (CRE) device. We also suggest test statistics for linearity, threshold constancy, unobserved individual-specific threshold effects, and for determining the number of thresholds. We derive the asymptotic properties of the proposed estimator in the small-threshold-effect framework and establish the limiting distributions of the suggested test statistics. We also investigate the extension to dynamic panels and show that both the static and dynamic models can be handled uniformly in the CRE framework. Monte Carlo simulation results indicate that the estimation, inference, and testing procedures have the desired performance in finite samples. The model is illustrated with two empirical applications to the relationship between cash flow and investment and the nexus between inflation and economic growth. [ABSTRACT FROM AUTHOR]

This research investigates four tail relationships between the U.S. dollar and S&P 500 returns by a dynamic mixture copula model with a link between the dependence structure and dependence intensity. The empirical results find that four tail relationships are evident and time-varying. Regarding the negative dependence, the tail relationship is prominently stronger in the situation where the U.S. dollar depreciates and the stock index increases than in the situation where the U.S. dollar appreciates and the stock index declines. Regarding the positive dependence, the situation of two returns increase occurs slightly more frequently than the situation of two returns decrease. [ABSTRACT FROM AUTHOR]

This article extends the kink threshold regression model with a constant threshold to a panel data framework with a covariate-dependent threshold, where the threshold is modeled as a function of informative covariates. We suggest an estimator based on the within-group transformation and propose test statistics for kink threshold effect and threshold constancy. We establish the asymptotic joint normality of the slope and threshold estimators and derive the limiting distributions of the test statistics. Our asymptotic results show that the inclusion of a covariate-dependent threshold does not affect the asymptotic joint normality of the slope and threshold estimates in the kink threshold regression model. Monte Carlo simulations show that the finite-sample proprieties of the proposed estimator and test statistics are generally satisfactory. [ABSTRACT FROM AUTHOR]

Classical threshold models assume that threshold values are constant and stable, which appears overly restrictive and unrealistic. In this article, we extend Hansen's (2000) constant threshold regression model by allowing for a time‐varying threshold which is approximated by a Fourier function. Least‐square estimation of regression slopes and the time‐varying threshold is proposed, and test statistics for the existence of threshold effect and threshold constancy are constructed. We also develop the asymptotic distribution theory for the time‐varying threshold estimator. Through Monte Carlo simulations, we show that the proposed estimation and testing procedures work reasonably well in finite samples, and there is little efficiency loss by the allowance for Fourier approximation in the estimation procedure even when there is no time‐varying feature in the threshold. On the contrary, the slope estimates are seriously biased when the threshold is time‐varying but being treated as a constant. The model is illustrated with an empirical application to a nonlinear Taylor rule for the United States. [ABSTRACT FROM AUTHOR]

This paper extends the cross-sectionally augmented panel unit-root test ( CIPS) developed by Pesaran et al. (2013, Journal of Econometrics, Vol. 175, pp. 94-115) to allow for smoothing structural changes in deterministic terms modelled by a Fourier function. The proposed statistic is called the break augmented CIPS ( BCIPS) statistic. We show that the non-standard limiting distribution of the (truncated) BCIPS statistic exists and tabulate its critical values. Monte-Carlo experiments point out that the sizes and powers of the BCIPS statistic are generally satisfactory as long as the number of time periods, T, is not less than fifty. The BCIPS test is then applied to examine the validity of long-run purchasing power parity. [ABSTRACT FROM AUTHOR]