A BVAR model for the connecticut economy
A BVAR model for the connecticut economy
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DOI：
10.1002/for.3980140303
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发表时间：
1995-05
影响因子：
3.4
通讯作者：
Pami Dua;S. Ray
中科院分区：
文献类型：
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作者：
Pami Dua;S. Ray
为康涅狄格州经济开发了一个贝叶斯向量自回归(BVAR)模型，以预测失业率、非农就业、实际个人收入和批准的住房许可。该模型既包括国家变量，也包括州变量。贝叶斯先验是根据样本外预测的准确性来选择的。我们发现，宽松的先验数据通常会产生更准确的预测。BVAR预测的样本外精度还与无限制VAR模型的预测和单变量ARIMA模型生成的基准预测的预测精度进行了比较。BVAR模型通常产生最准确的1988年至1992年的短期和长期样本外预测。它还正确地预测了变化的方向。
A Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out-of-sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out-of-sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short- and long-term out-of-sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.