The vector autoregressive model has long been used for portfolio analysis, while a recent extension (VARX) incorporates exogenous factors. Despite its increased forecasting precision, the ...

Since the vector autoregressive models are estimates from the Yule-Walker equations, not by maximum likelihood, the exact likelihood values are not available for computing the AIC. However, for the ...

The vector autoregressive model has long been used for portfolio analysis, while a recent extension (VARX) incorporates exogenous factors. Despite its increased forecasting precision, the ...

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is ...

An expression for the likelihood function of a stationary vector autoregressive-moving average process is developed. The expression is very efficient numerically and applies to any stationary but not ...

Abstract This paper employs a threshold vector autoregressive (TVAR) model to analyze a possible asymmetric behavior of exchange rate pass-through (ERPT) or pricing-to-market (PTM) in Japanese exports ...

You can model the three series Y1-Y3 as a vector autoregressive process in the variables instead of in the errors by using the TYPE=V option. If you want to model Y1-Y3 as a function of past values of ...

For more information on this research see: A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression. Risks, 2024,12 (1).

The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term used to describe an approach to estimate volatility in financial markets.