Signaling NBER turning points: a sequential approach

Control charts for measurement error models

We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter is of essential importance for these linear MEMs, it is of much relevance to provide instruments for online monitoring in order to detect its possible changes. In this paper we develop control charts for online detection of such changes, i.e., from AR(1) to ARMA(1,1) and vice versa, as soon as they occur. For this purpose, we elaborate on both cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts and investigate their performance in a Monte Carlo simulation study. The empirical illustration of our approach is conducted based on time series of daily realized volatilities.

Regime-switching empirical similarity model: a comparison with baseline models

In this paper, I extend the standard specification of the empirical similarity (ES) model of Gilboa et al. (Rev Econ Stat 88:433-444, 2006) to account for changes in parameters. I implement this by allowing for a combination of component ES models in the spirit of Gaussian mixture models. The predictive power of the modified model, along with that of the standard specification, will be assessed and compared to the baseline models consisting of autoregressions and Markov-switching autoregressions within a simulation exercise. Finally, we also compare the predictive ability of models using data on quarterly US real GDP growth. The results indicate that in situations of a more complex regime-switching behavior and a moderate to high autocorrelation in series, modified ES model demonstrates a better empirical fit. In addition, results of the empirical example show that modified ES models can better predict more extreme observations.