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Article Citation - WoS: 1Citation - Scopus: 1A Unit Root Test With Markov Switching Deterministic Components: A Special Emphasis on Nonlinear Optimization Algorithms(Springer, 2023) Omay, Tolga; Corakci, AysegulIn this study, we investigate the performance of different optimization algorithms in estimating the Markov switching (MS) deterministic components of the traditional ADF test. For this purpose, we consider Broyden, Fletcher, Goldfarb, and Shanno (BFGS), Berndt, Hall, Hall, Hausman (BHHH), Simplex, Genetic, and Expectation-Maximization (EM) algorithms. The simulation studies show that the Simplex method has significant advantages over the other commonly used hill-climbing methods and EM. It gives unbiased estimates of the MS deterministic components of the ADF unit root test and delivers good size and power properties. When Hamilton's (Econometrica 57:357-384, 1989) MS model is re-evaluated in conjunction with the alternative algorithms, we furthermore show that Simplex converges to the global optima in stationary MS models with remarkably high precision and even when convergence criterion is raised, or initial values are altered. These advantages of the Simplex routine in MS models allow us to contribute to the current literature. First, we produce the exact critical values of the generalized ADF unit root test with MS breaks in trends. Second, we derive the asymptotic distribution of this test and provide its invariance feature.Article Citation - WoS: 6Citation - Scopus: 4Is real per capita state personal income stationary? New nonlinear, asymmetric panel-data evidence(Wiley, 2020) Emirmahmutoglu, Furkan; Gupta, Rangan; Miller, Stephen M.; Omay, TolgaThis paper re-examines the stochastic properties of U.S. state real per capita personal income, using new panel unit-root procedures. The new developments incorporate non-linearity, asymmetry, and cross-sectional correlation within panel-data estimation. Including nonlinearity and asymmetry finds that 43 states exhibit stationary real per capita personal income whereas including only nonlinearity produces 42 states that exhibit stationarity. Stated differently, we find that two states exhibit nonstationary real per capita personal income when considering nonlinearity, asymmetry, and cross-sectional dependence.Article Citation - WoS: 1Citation - Scopus: 1Re-Examining the Real Interest Rate Parity Hypothesis Under Temporary Gradual Breaks and Nonlinear Convergence(Springer Heidelberg, 2023) Hasanov, Mubariz; Omay, Tolga; Abioglu, VasifThis paper investigates the real interest parity hypothesis by testing stationarity of real interest rate differentials for 52 countries with respect to the USA. Taking account of the fact that both asymmetric adjustment and gradual temporary breaks may better characterize the dynamics of real interest rate differentials, we propose a new test that allows for two temporary shifts together with asymmetric adjustment towards the equilibrium. We employ the newly proposed test procedure along with the conventional ADF test as well as nonlinear KSS and OSH tests to examine stationarity of real interest rate differentials. Among the main results, we find that the newly proposed unit root test procedure highly outperforms the existing unit root tests in terms of rejecting the null hypothesis of unit root. Our results suggest that real interest rate differentials can be characterized by a stationary process with asymmetric adjustment around gradual and temporary shifts of mean.Article Citation - Scopus: 2The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: an Extensive Simulation Study(Mdpi, 2024) Omay, Tolga; Akdi, Yilmaz; Emirmahmutoglu, Furkan; Eryilmaz, MeltemThe Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.
