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This paper proposes parametric two-step procedures for assessing the stability of cross-sectional dependency measures in the presence of potential breaks in the marginal distributions. The procedures are based on formerly proposed sup-LR tests in which restricted and unrestricted likelihood functions are compared with each other. First, we show theoretically that standard asymptotics do not hold in this situation. We propose a suitable bootstrap scheme and derive test statistics in different commonly used settings. The properties of the test statistics and precision of the associated change-point estimator are analysed and compared with existing non-parametric methods in various Monte Carlo simulations. These studies reveal advantages in test power for higher-dimensional data and an almost uniform superiority of the sup-LR test in terms of precision of the change-point estimator. We then apply this method to equity returns of European banks during the financial crisis of 2008.

This paper proposes a Bayesian unit root test for testing a non-stationary random walk of nonlinear exponential smooth transition autoregressive process. It investigates the performance of Bayes estimators and Bayesian unit root test due to its superiority in estimation and power properties than reported in existing literature. The proposed approach is applied to the real effective exchange rates of 10 selected countries of the organization of economic co-operation and development (OECD) and the paper observe some interesting findings which demonstrate the usefulness of the model.

By applying an endogenous switching regression model to a sample of 64 countries, this article explores whether the effect of trade openness on inflation is influenced by the adoption of inflation targeting (IT). The outcome indicates that, while there exists a significant and negative impact of trade openness on inflation in the non-IT countries with flexible exchange rate system, the effect is negligible in the IT economies. In addition, the above differential inflation effect of trade openness across IT and non-IT regimes is only present in the developing subsample with flexible exchange rate system, but not the developed counterpart. Moreover, apart from trade openness, financial openness reinforces inflation in those developing countries not adopting IT, whereas no such significant effect is found in developing countries adopting IT. Instead of inflation, further results show that trade openness lowers inflation volatility both in developing and developed countries not adopting IT, yet the impact is smaller in developed country group. However, no such statistically significant link is found in developing and developed countries that adopt IT.

This paper presents a Bayesian sampling approach to bandwidth estimation for the local linear estimator of the regression function in a nonparametric regression model. In the Bayesian sampling approach, the error density is approximated by a location-mixture density of Gaussian densities with means the individual errors and variance a constant parameter. This mixture density has the form of a kernel density estimator of errors and is referred to as the kernel-form error density (c.f. Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34.). While (Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34) use the local constant (also known as the Nadaraya-Watson) estimator to estimate the regression function, we extend this to the local linear estimator, which produces more accurate estimation. The proposed investigation is motivated by the lack of data-driven methods for simultaneously choosing bandwidths in the local linear estimator of the regression function and kernel-form error density. Treating bandwidths as parameters, we derive an approximate (pseudo) likelihood and a posterior. A simulation study shows that the proposed bandwidth estimation outperforms the rule-of-thumb and cross-validation methods under the criterion of integrated squared errors. The proposed bandwidth estimation method is validated through a nonparametric regression model involving firm ownership concentration, and a model involving state-price density estimation.

We analyze Australian electricity price returns and find that they exhibit volatility clustering, long memory, structural breaks, and multifractality. Consequently, we let the return mean equation follow two alternative specifications, namely (i) a smooth transition autoregressive fractionally integrated moving average (STARFIMA) process, and (ii) a Markov-switching autoregressive fractionally integrated moving average (MSARFIMA) process. We specify volatility dynamics via a set of (i) short- and long-memory GARCH-type processes, (ii) Markov-switching (MS) GARCH-type processes, and (iii) a Markov-switching multifractal (MSM) process. Based on equal and superior predictive ability tests (using MSE and MAE loss functions), we compare the out-of-sample relative forecasting performance of the models. We find that the (multifractal) MSM volatility model keeps up with the conventional GARCH- and MSGARCH-type specifications. In particular, the MSM model outperforms the alternative specifications, when using the daily squared return as a proxy for latent volatility.

This paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.

It is widely recognized that aggregate employment dynamics is characterized by hysteresis. In the presence of hysteresis, the long run level of employment instead of being unique and history-independent, depends on the adjustment path that is taken, which includes the monetary and fiscal measures. It is thus important to study the presence of hysteresis in the macrodynamics of employment to understand whether the recession followed 2007s financial crisis will have permanent effects, and prospectively to conduct fiscal and monetary policies. The main contribution of this paper is to analyse the relative impact of the main sources hysteresis (non-convex adjustment costs, uncertainty and the flexibility of working time arrangements) to the width of the employment band of inaction. For that purpose, a switching employment equation was estimated from a computational implementation of the linear play model of hysteresis. From our results we found significant hysteresis effects in the aggregate employment dynamics caused by the presence of non-convex adjustment costs as uncertainty. We also found that the flexibility firms may have to adjust labour input by varying the number of hours of work per employee helps to mitigate the effect of uncertainty upon the band of inaction.

This paper examines how different uncertainty measures affect the unemployment level, inflow, and outflow in the U.S. across all states of the business cycle. We employ linear and nonlinear causality-in-quantile tests to capture a complete picture of the effect of uncertainty on U.S. unemployment. To verify whether there are any common effects across different uncertainty measures, we use monthly data on four uncertainty measures and on U.S. unemployment from January 1997 to August 2018. Our results corroborate the general predictions from a search and matching framework of how uncertainty affects unemployment and its flows. Fluctuations in uncertainty generate increases (upper-quantile changes) in the unemployment level and in the inflow. Conversely, shocks to uncertainty have a negative impact on U.S. unemployment outflow. Therefore, the effect of uncertainty is asymmetric depending on the states (quantiles) of U.S. unemployment and on the adopted unemployment measure. Our findings suggest state-contingent policies to stabilize the unemployment level when large uncertainty shocks occur.