Assessing forecast performance in a cointegrated system
Assessing forecast performance in a cointegrated system
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DOI：
10.1002/(sici)1099-1255(199609)11:5
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发表时间：
1996-09
影响因子：
2.1
通讯作者：
Dennis L. Hoffman;R. Rasche
中科院分区：
文献类型：
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作者：
Dennis L. Hoffman;R. Rasche
本文研究了预测性能的协整系统相对于预测性能的一个可比的VAR，未能认识到，该系统的特点是协整。我们研究的协整系统是由三个向量，货币需求的代表，费雪方程，并捕获的利率差的风险溢价。与此系统相关联的向量误差校正模型（VECM）产生的预测与从相应的差分向量自回归（DVAR）以及基于数据水平的向量自回归（LVAR）获得的预测进行比较。预测评估是使用Clements和Hendry（1993）提出的“全系统”标准和比较特定变量的预测性能进行的。总体而言，我们的研究结果表明，选择性的预测性能的改善（特别是在长期的预测视野），可以观察到的协整排名的知识。我们的总体结论是，当纳入协整的优势出现时，它通常是在较长的预测范围内。这与Engle和Yoo（1987）的预测一致。但我们也发现，与Clements和Hendry（1995）一致，预测性能的相对增益显然取决于所选择的数据转换。
This paper examines the forecast performance of a cointegrated system relative to the forecast performance of a comparable VAR that fails to recognize that the system is characterized by cointegration. The cointegrated system we examine is composed of three vectors, a money demand representation, a Fisher equation, and a risk premium captured by an interest rate differential. The forecasts produced by the vector error correction model (VECM) associated with this system are compared with those obtained from a corresponding differenced vector autoregression, (DVAR) as well as a vector autoregression based upon the levels of the data (LVAR). Forecast evaluation is conducted using both the ‘full-system’ criterion proposed by Clements and Hendry (1993) and by comparing forecast performance for specific variables. Overall our findings suggest that selective forecast performance improvement (especially at long forecast horizons) may be observed by incorporating knowledge of cointegration rank. Our general conclusion is that when the advantage of incorporating cointegration appears, it is generally at longer forecast horizons. This is consistent with the predictions of Engle and Yoo (1987). But we also find, consistent with Clements and Hendry (1995) that relative gain in forecast performance clearly depends upon the chosen data transformation.