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

• Autor(a) principal: Athanasopoulos, George
• Data de Publicação: 2009
• Outros Autores: Guillen, Osmani Teixeira Carvalho, Issler, João Victor
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: http://hdl.handle.net/10438/2192
• topic: Reduced rank models; Model selection criteria; Forecasting accuracy; Economia; Análise de regressão; Previsão econômica; Modelos econométricos; Expectativas racionais (Teoria econômica); 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 for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.