Bayesian Model Evaluation and Prediction of Economic Time Series

经济时间序列的贝叶斯模型评估与预测

• 批准号: 9422922
• 负责人: Peter Phillips
• 金额: $ 23.46万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-01 至 1999-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9422922&HistoricalAwards=false
• 关键词: Bayesian; Model; Evaluation; Prediction; Economic

9422922 Peter Phillips Model choice, model simplification and the determination of good models for prediction are all important elements in empirical econometric research. When times series are nonstationary, an aspect of model choice is how to model the nonstationarity of the data (e.g., stochastic trends versus deterministic trends versus trend breaks). Although difficult this choice has a substantial impact on the performance of out-of-sample forecasts and forecast confidence sets. Furthermore, in practical business and financial research we are sometimes faced with the need to model or predict large number of series simultaneously. In such situations we need automated procedures for model selection that can take account such critical aspects of a series as its stationarity or lack thereof. The first of the three projects in this research is concerned with the development and justification of such procedures for use in the analysis of economic time series. The methods employed are Bayesian and build on ideas on model determination, hypothesis testing, and forecasting in the presence of non-stationarity that the PI has put forward in recent research. The second project by the PI looks at kernel regression when the errors are nonstationary. Although kernel regression theory is quite well developed in the stationary time series case, there appears to be no theory for case of unit roots or integrated processes. One problem with kernel regression in this case is that estimators are often inconsistent. The research develops modified kernel estimators that are consistent in the presence of nonstationarity both with and without long memory. The third project is an extension of the PIs work on "fully modified" VAR (FM-VAR) estimators. These estimators can take advantage of potential cointegrating links between series without having to be explicit about their form or dimension and without preliminary testing. This research extends the previous work so as to allow for I(0), I(1) , and I(2) regressors simultaneously in the same VAR. It proceeds without making any specific assumptions about the degree of cointegration or the order of cointegration of any of the regressors, and without pretesting of the cointegrating rank.

小行星9422922 模型的选择、模型的简化和预测模型的确定都是实证计量经济学研究的重要内容。 当时间序列是非平稳的时，模型选择的一个方面是如何对数据的非平稳性建模（例如，随机趋势对确定性趋势对趋势突变）。 虽然困难，这一选择有很大的影响，样本外的预测和预测信心集的表现。 此外，在实际的商业和金融研究中，我们有时需要同时对大量的序列进行建模或预测。 在这种情况下，我们需要模型选择的自动化程序，可以考虑到一个系列的平稳性或缺乏平稳性等关键方面。 本研究的三个项目中的第一个项目涉及经济时间序列分析中使用的这种程序的开发和合理性。 所采用的方法是贝叶斯和建立在模型确定，假设检验和预测的非平稳性，PI在最近的研究中提出了存在的想法。 PI的第二个项目是在误差是非平稳的情况下研究核回归。 虽然核回归理论在平稳时间序列的情况下是相当发达的，似乎没有单位根或集成过程的情况下的理论。 在这种情况下，核回归的一个问题是估计量往往不一致。 研究开发了修改后的核估计是一致的，在存在非平稳性和不长记忆。 第三个项目是对PI关于“完全修改”VAR（FM-VAR）估计量的工作的扩展。 这些估计量可以利用系列之间潜在的协整联系，而不必明确其形式或维度，也无需进行初步测试。 该研究扩展了以前的工作，允许I（0），I（1）和I（2）回归同时在同一个VAR。 它的收益没有作出任何具体的假设的程度的协整或协整的顺序的任何回归，也没有预先检验的协整秩。