Tolga Omay

A famous puzzle holds that real exchange rates carry unit roots and that their deviations take three to five years to die, far too long for sticky prices to explain. We show the puzzle is not about persistence but about the equilibrium: the fair value itself drifts smoothly, and imposed as a constant it is read as a unit root. A microeconomic buyer-seller difference equation and a macroeconomic Taylor loss-minimisation problem both deliver this moving fair value, and both map into one estimable smooth-transition (arctangent) trend, so the economic model and the econometric test share a single object. Around it, sixteen of twenty OECD real exchange rates are stationary, six of ten independent currency areas after euro consolidation; nonlinear ESTAR adjustment adds no rejection; a formal rule separates permanent from temporary shifts in parity; and the median half-life of deviations falls from years to five months.

This study examines Brent crude oil price dynamics using an integrated framework of bootstrap sequential break detection and Asymmetric Exponential Smooth Transition Autoregressive (AESTAR) modeling. We demonstrate that oil prices follow an AESTAR process where structural breaks emerge endogenously through dual transition functions, reconciling previously competing explanations in the literature. Analysis of monthly data (1985–2023) identifies major structural shifts coinciding with critical economic events, while revealing these breaks emerge automatically through regime-dependent means. Enhanced testing confirms embedded LSTAR-dominant dynamics with ESTAR components, while skeleton analysis validates the dual equilibrium framework with balanced regime distribution. Generalized Impulse Response Function analysis reveals distinct shock transmission patterns: Tier 1 extreme events ( δ _max > 1.8) exhibit persistent deviations requiring sustained policy intervention, while Tier 2 events demonstrate mean reversion properties suitable for conventional responses. The framework provides observable threshold levels ($53.62, $37.39) enabling real-time policy intervention, supporting regime-contingent monetary policy and strategic petroleum reserve management protocols. This approach offers policymakers actionable tools for managing oil price volatility through empirically validated intervention strategies.

In this study, we test the empirical validity of the real interest rate parity hypothesis for 15 Latin American countries over the period 2005–2023. To this end, we employ a battery of panel unit root tests to examine stochastic properties of the real interest rate differentials (RIDs) of the countries under consideration. The panel unit root tests that allow for both the cross‐sectional dependence and the nonlinearities in the adjustment process do not reject the null of unit root for the most of these countries, suggesting that the real interest rate parity hypothesis does not hold for these countries. On the other hand, the panel unit root test that allows for smooth structural changes produces results consistent with the real interest rate parity hypothesis for 12 out of 15 Latin American countries. These findings imply that various shocks, including political, economic, and financial upheavals, can cause significant structural shifts in the RIDs of Latin American countries.

The 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.

Structural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

This study aims to provide an understanding of the concept of omitted variable bias through the total derivative method. This novel approach that is often overlooked could bring a new perspective to statisticians, econometricians, or researchers in neighboring disciplines such as social sciences, management, or economics. In order to complement this mathematical method, the study also employs graphical representations. By doing so, we provide a detailed walkthrough of the total derivative method, its visual depiction, and its application to the omitted variable bias. We believe that this approach can enhance the understanding of regression analysis and foster a deeper connection between mathematics and econometrics. Overall, this study can contribute to the development of new theoretical foundations using the total differential method in this context.