Vector Autoregressive Analysis
Helmut Lütkepohl
Abstract
Open-access reader
Helmut Lütkepohl
Abstract
Open-access reader
An introduction to vector autoregressive (VAR) analysis is given with special emphasis on cointegration. The models, estimating their parameters and specifying the autoregressive order, the cointegrating rank and other restrictions are discussed. Possibilities for model validation are also considered, Causality tests, impulse responses and forecast error variance decompositions are presented as tools for analyzing VAR models.
OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
An introduction to vector autoregressive (VAR) analysis is given with special emphasis on cointegration. The models, estimating their parameters and specifying the autoregressive order, the cointegrating rank and other restrictions are discussed. Possibilities for model validation are also considered, Causality tests, impulse responses and forecast error variance decompositions are presented as tools for analyzing VAR models.
Key concepts: Autoregressive model, Cointegration, Econometrics, Vector autoregression, Impulse response, Rank (graph theory), Autoregressive integrated moving average, STAR model