This paper examines the performance of prediction intervals based on bootstrap for threshold autoregressive models. We consider four bootstrap methods to account for the variability of estimates, correct the small-sample bias of autoregressive coefficients and allow for heterogeneous errors....

In this paper we highlight the necessity of new criteria for evaluation of performance of unit root tests. We suggest focusing directly on the reasons that create ambiguity in unit root test’s results. Two reasons for unsatisfactory properties of unit root tests can be found in the literature...

In this paper, we propose a new approach to constructing confidence sets for the timing of structural breaks. This approach involves using Markov-chain Monte Carlo methods to simulate marginal “fiducial” distributions of break dates from the likelihood function. We compare our proposed...

This paper studies the sequential sampling scheme as a solution to the problem of aliasing, where the sampling interval is restricted to a minimum allowable value. Sequential sampling is analyzed and it is proved that when the sampling ratio is an integral number, the associated spectral...

This paper contributes to the literature by comparing predictive accuracy of one-period real-time simple seasonal ARIMA forecasts of Latvia's Gross Domestic Product (GDP) as well as by comparing a direct forecast of Latvia's GDP versus three kinds of indirect forecasts. Four main results are as...

This paper contributes to the literature by comparing predictive accuracy of one-period real-time simple seasonal ARIMA forecasts of Latvia's Gross Domestic Product (GDP) as well as by comparing a direct forecast of Latvia's GDP versus three kinds of indirect forecasts. Four main results are as...

This paper contributes to the literature by comparing predictive accuracy of one-period real-time simple seasonal ARIMA forecasts of Latvia's Gross Domestic Product (GDP) as well as by comparing a direct forecast of Latvia's GDP versus three kinds of indirect forecasts. Four main results are as...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...

The coronavirus has deleteriously affected a great majority of countries in the world. Developed societies such as the United States and the majority of Western countries have had the highest rates of mortality because of the pandemic. Sub-Saharan Africa, on the other hand, has been the...