Inference About Predictive Accuracy; Exchange Rate Volatility: Inventory Models

小行星9309927 数据挖掘或过度拟合，也许是由于在整个专业范围内重复使用有限的数据体，有时会导致样本拟合的结果令人难以置信。 一些经济学家认为，样本外预测的准确性是衡量模型质量的关键指标。 不幸的是，经济学中的大多数实证研究主要依赖于样本内证据。 许多预测和预测准确性的样本外测试都是非正式的和定性的，部分原因是应用研究人员发现迄今为止开发的程序不充分。 在样本中运行良好的程序在样本外似乎也不起作用。 该项目的一个贡献来自于开发新的程序，用于对样本预测和预测误差进行计量经济学推断，这些预测和预测误差一般足以在广泛的应用中有用，最终包括全球环境变化的社会经济影响模型。 早期的一项研究发现，具有良好样本内拟合的复杂汇率模型在样本外预测方面并不比随机游走模型更好。 本项目利用这些程序研究汇率波动性竞争模型的预测能力，并开发替代模型，包括将汇率与利率联系起来的模型。 该项目的最后一个研究领域是实证库存模型。 在研究的一部分，渐近理论和模拟方法将被用来评估估计库存模型的替代方法。 问题包括对标准化的敏感性，有效GMM估计的有限样本行为，以及在工具选择中所谓的“外生性”和“相关性”之间的权衡。 其结果不仅与库存估计有关，也与其他时间序列模型有关。 在第二部分，库存模型将估计两位数的美国，英国，加拿大和日本的数据，使用第一部分的结果。 要研究的问题包括成本函数的凸性，对冲击的动态反应，以及特质冲击与总体冲击的重要性。

9309927 West Data-mining or over-fitting, perhaps as a result of repeated profession-wide use of a limited body of data, leads at times to spuriously good in sample fits. Some economists have argued that out of sample predictive accuracy is the critical measure of model quality. Unfortunately most empirical work in economics relies mainly on in-sample evidence. Many of the out of sample tests of predictions and predictive accuracy are informal and qualitative in part because applied researchers find procedures developed to date inadequate. Procedures that have worked well in sample also do not appear to work well out of sample. One contribution of this project comes from developing new procedures for econometric inference about out of sample predictions and prediction errors general enough to be useful in a wide range of applications, including eventually models of the socioeconomic impacts of global environmental change. An earlier study found that complicated models of exchange rates with good in-sample fit do no better than a random walk model in out of sample predictions. This project uses these procedures to study the predictive ability of competing models of exchange rate volatility and develops alternative models, including one that links exchange rates to interest rates. The final area of research for this project is on empirical inventory models. In one part of the research, asymptotic theory and simulation methods will be used to evaluate alternative methods for estimating inventory models. Issues include sensitivity to normalization, finite sample behavior of efficient GMM estimators, and the tradeoff between what has been called "exogeneity" and "relevance" in choice of instruments. The results will be relevant in estimation of not just inventory but other time series models as well. In a second part, inventory models will be estimated on two digit U.S., U.K., Canadian and Japanese data, using the results of the first part. Questi ons to be studied include convexity of cost functions, dynamic responses to shocks, and the importance of idiosyncratic versus aggregate shocks.