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

• Autor(a) principal: Athanasopoulos, George
• Data de Publicação: 2010
• Outros Autores: Guillen, Osmani Teixeira Carvalho, Issler, João Victor, Vahid, Farshid
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: http://hdl.handle.net/10438/4279
• topic: Reduced rank models; Model selection criteria; Forecasting accuracy; Economia; Análise de regressão; Modelos macroeconômicos; Previsão econômica; Monte Carlo, Método de; Métodos de simulação; Economia

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.