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

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.

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

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 inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

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

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.

Improved combinations of parametric and nonparametric Regression estimators

This paper provides a systematic and unified treatment of the developments in the area of kernel estimation in econometrics and statistics. Both the estimation and hypothesis testing issues are discussed for the nonparametric and semiparametric regression models. A discussion on the choice of windowwidth is also presented.

Do brazilian states engage in the fiscal war of ports? An empiric study of tax competition and an analysis of a natural experiment: the brazilian senate resolution 13/2012

O fim do ano de 2014 marcou o segundo aniversário da Resolução 13/2012 (R13) do Senado brasileiro. Grosso modo, R13 constituiu-se de um normativo do Senado cujo objetivo era o de por um fim na Guerra Fiscal dos Portos (FWP), uma competição fiscal entre os estados que se dá através da concessão de benefícios fiscais sobre operações interestaduais com mercadorias importadas de modo a atrair empresas importadoras para o território do estado concedente. R13 diminuiu o nível da tributação sobre tais operações, esperando com isso diminuir os lucros auferidos e a propensão das firmas de aceitarem tais regimes especiais de incentivação fiscal. Nada obstante, R13 gerou uma grande discussão sobre se os benefícios da atração de investimentos para um estado em particular superariam ou não os custos que esse estado incorreria em renunciar receitas tributárias em razão concessão desses benefícios fiscais. O objetivo do presente trabalho é o de dar uma contribuição a essa discussão, testando se um comportamento de interação estratégica entre estados, tal como aquele que supostamente ocorre no contexto da FWP, de fato emerge dos dados de importação coletados de janeiro de 2010 a maio de 2015, e, também, testando se a R13 de fato afetou tal comportamento de interação estratégica. Utiliza-se aqui um modelo de econometria espacial, no qual se especifica uma matriz de pesos que agrega o nível de importação das jurisdições concorrentes, organizando os dados em um painel de efeitos fixos. Os resultados sugerem que existe um comportamento de interação estratégica entre os estados e que a R13 de fato impactou tal comportamento.