Forecasting Accuracy and Estimation Uncertainty Using VAR Models
with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study.
Osmani Teixeira de Carvalho Guillén, João Victor Issler and George
Athanasopoulos
Using vector autoregressive (VAR) models and Monte-Carlo simulation
methods we investigate the potential gains for forecasting accuracy and estimation
uncertainty of two commonly used restrictions arising from economic relationships.
The first reduces parameter space by imposing long-term restrictions on the
behavior of economic variables as discussed by the literature on cointegration,
and the second reduces parameter space by imposing short-term restrictions
as discussed by the literature on serial-correlation common features (SCCF).
Our simulations cover three important issues on model building, estimation,
and forecasting. First, we examine the performance of standard and modified
information criteria in choosing lag length for cointegrated VARs with SCCF
restrictions. Second, we provide a comparison of forecasting accuracy of .fitted
VARs when only cointegration restrictions are imposed and when cointegration
and SCCF restrictions are jointly imposed. Third, we propose a new estimation
algorithm where short- and long-term restrictions interact to estimate the
cointegrating and the cofeature spaces respectively. We have three basic results.
First, ignoring SCCF restrictions has a high cost in terms of model selection,
because standard information criteria chooses too frequently inconsistent
models, with too small a lag length. Criteria selecting lag and rank simultaneously
have a superior performance in this case. Second, this translates into a superior
forecasting performance of the restricted VECM over the VECM, with important
improvements in forecasting accuracy .reaching more than 100% in extreme cases.
Third, the new algorithm proposed here fares very well in terms of parameter
estimation, even when we consider the estimation of long-term parameters,
opening up the discussion of joint estimation of short- and long-term parameters
in VAR models.
Keywords: Reduced rank models, Model selection criteria, Forecasting
accuracy.
