The VAR/SVAR (Vector Autoregressive and Structural Vector Autoregressive) models are the cornerstone of contemporaneous empirical macroeconomic research, in particular for being able to measure impact fiscal policy shocks. They may be employed as atheoretical models, well a mean support estimation testing DSGE (Dynamic Stochastic General Equilibrium) – main theoretical tool modern macroeconomic...

Gaussian vector autoregressive (VAR) processes have been extensively studied in the literature. However, Gaussian assumptions are stringent for heavy-tailed time series that frequently arises in finance and economics. In this paper, we develop a unified framework for modeling and estimating heavy-tailed VAR processes. In particular, we generalize the Gaussian VAR model by an elliptical VAR mode...

The objective of this study is to compare alternative computerized model-selection strategies in the context of the vector autoregressive (VAR) modeling framework. The focus is on a comparison of subset modeling strategies with the general-to-specific reduction approach automated by PcGets. Different measures of the possible gains of model selection are considered: (i) the chances of finding th...

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonabl...

We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within the system that serve as good predictors improving the forecast accuracy and a cluster structure of the predictive tasks around these. The method is based on the classical linear vector autoregressive model (VAR) and...

In psychology, studying multivariate dynamical processes within a person is gaining ground. An increasingly often used method is vector autoregressive (VAR) modeling, in which each variable is regressed on all variables (including itself) at the previous time points. This approach reveals the temporal dynamics of a system of related variables across time. A follow-up question is how to analyze ...

This paper establishes vector autoregressive moving average (VARMA) models for Malaysian monetary policy analysis by efficiently identifying and simultaneously estimating the model parameters using full information maximum likelihood. The monetary literature is largely dominated by vector autoregressive (VAR) and structural vector autoregressive (SVAR) models, and to the best of our knowledge, ...