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


Autoria(s): Athanasopoulos, George; Guillen, Osmani Teixeira Carvalho; Issler, João Victor; Vahid, Farshid
Data(s)

29/03/2010

23/09/2010

29/03/2010

23/09/2010

29/03/2010

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.

Identificador

0104-8910

http://hdl.handle.net/10438/4279

Idioma(s)

en_US

Publicador

Fundação Getulio Vargas. Escola de Pós-graduação em Economia

Relação

Ensaios Econômicos;704

Palavras-Chave #Reduced rank models #Model selection criteria #Forecasting accuracy #Análise de regressão #Modelos macroeconômicos #Previsão econômica #Método de Monte Carlo #Modelos de simulação #Economia
Tipo

Working Paper