Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

Resumo

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Ficha do documento

Tipo

Estudo

Ano

2010

Instituição

Fundação Getulio Vargas. Escola de Pós-graduação em Economia

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Não informado

Identificador

oai:repositorio.fgv.br:10438/6993

Temas

Economia

Palavras-chave

Reduced rank modelsModel selection criteriaForecasting accuracyEconomiaAnálise de regressãoModelos macroeconômicosPrevisão econômicaMonte Carlo, Método deMétodos de simulação