Efficient Econometric Shrinkage and Forecasting

高效的计量经济学收缩和预测

• 批准号: 1258858
• 负责人: Bruce Hansen
• 金额: $ 26.89万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1258858&HistoricalAwards=false
• 关键词: Efficient; Econometric; Shrinkage; Forecasting

In the course of empirical research or policy analysis, economists typically estimate sophisticated high-dimensional models. There are a set of standard estimation methods developed for these purposes, and these estimators share the property that they have approximate normal distributions. However, it is well known that normally-distributed estimators can be improved (have reduced risk) if they are shrunk towards a pre-specified point in the parameter space (a restriction or simpler model of interest). This suggests that common econometric estimators can be improved by shrinkage towards restricted estimators.This proposal suggests that this insight can be made rigorous. The PI develops general shrinkage estimators whose risk (the statistical expected loss) is smaller than conventional estimators. These new estimators are efficient, meaning that their risk is the lowest possible among all feasible estimators. This project proposes efficient methods for both parametric models (those defined by a finite set of parameters) and semiparametric models (when some features of the model are treated as nonparametric or high dimensional).Closely related to the question of efficient estimation is the technique of model selection and combination. These issues are particularly relevant for economic forecasting, where forecast combination is routinely applied, yet little theoretical guidance exists for selection of the combination weights. This proposal focuses on developing rigorous criteria for selection of combination weights in the context of multi-step forecasts. Multi-step forecasts are critical for policy analysis, yet have particular technical challenges. This project investigates methods for direct forecasts, iterated forecasts, and forecast intervals. The econometric methods developed in this proposal are expected to have broad application in applied economic analysis, policy analysis, and economic forecasting. It is expected that the theory and methods uncovered by this research will find productive use by applied economists, statisticians, and other social scientists both in academics and the public sector.

在实证研究或政策分析过程中，经济学家通常会估计复杂的高维模型。有一套为这些目的开发的标准估计方法，这些估计器共享它们具有近似正态分布的属性。然而，众所周知，如果正态分布估计量缩小到参数空间中的一个预先指定的点(一个约束或更简单的感兴趣模型)，则它们可以得到改进(降低风险)。这表明，通常的计量经济学估计量可以通过向受限估计收缩来改进。这一建议表明，这一观点可以变得严格。PI发展了一般的收缩估计器，其风险(统计预期损失)比传统估计器小。这些新的估计器是有效的，这意味着它们的风险是所有可行估计器中可能最低的。该项目提出了参数模型(由有限参数集定义的模型)和半参数模型(当模型的某些特征被视为非参数或高维)的有效方法。与有效估计问题密切相关的是模型选择和组合技术。这些问题与经济预测特别相关，在经济预测中，预测组合是常规应用，但组合权重的选择几乎没有理论指导。这项建议侧重于在多步预测的背景下制定选择组合权重的严格标准。多步预测对于政策分析至关重要，但也面临着特殊的技术挑战。本项目研究直接预报、迭代预报和预报间隔的方法。本建议中发展的计量经济学方法有望在应用经济分析、政策分析和经济预测中得到广泛应用。预计这项研究发现的理论和方法将在学术界和公共部门得到应用经济学家、统计学家和其他社会科学家的富有成效的使用。