Topics in Econometrics and Macroeconomics

研究者研究了四个不同的主题，第一个比第二个和第三个范围更大，而第二个和第三个范围又比第四个范围更大。第一个主题是方法论，涉及某些通常研究的经济关系中的统计偏差。“偏见”是一个精确的统计术语，指的是系统地估计一个平均错误的值的倾向。在经济数据中，有偏差的估计导致预测和经济建议具有误导性，如果偏差很大，可能会产生很大的误导性。相关经济关系的例子包括股票收益与股息收益率、汇率变化与远期溢价、利率与通货膨胀之间的关系。对这些重要关系的估计用于预测经济活动、制定金融决策和制定经济政策。先前的不同证据表明，使用一种称为回归的技术对这些关系的估计存在偏差。研究者开发了一个通用的、统一的框架，允许系统的研究、解释，并在必要时纠正这种回归中的偏差。一般框架的优点是，它让研究人员了解在给定的情况下，偏见是可能大还是可能小。结果可用于解释相关回归中的估计和推理，改进估计和推理，并理解经济模型的模拟结果。重要的是，这种方法考虑到了经济数据中常见的复杂性；这些并发症的计量术语是序列相关和条件异方差。研究者将结果应用于汇率回报对远期溢价的回归。研究者研究的第二个主题与货币政策如何影响通胀率大致相似的国家之间的浮动汇率有关。这项研究是经验性的，依赖于一种叫做向量自回归的技术。标准的宏观经济模型关注的是货币政策（以美国和外国的利率之差衡量）与美元价值之间的直接联系。这些模型将影响汇率的任何其他因素视为货币政策的外生性演变。研究者提出并发展了一种方法，使研究者能够评估货币政策的意外变动是否实际上仅通过利率或通过对超额回报或风险溢价的影响来影响汇率。这项研究提高了我们对货币政策的理解。本课题研究的第三个课题是，当被评估的预测模型之间存在复杂的关系时，如何比较预测的准确性。这项研究是方法论的。在经济研究中，一些预测竞赛比较简单模型和复杂模型，简单模型是更复杂模型的一个特例（称为“嵌套”模型比较）。其他预测竞赛评估复杂的模型，其中没有模型是其他模型的特殊情况（“非嵌套”）。最后，有一些预测竞争同时涉及两种类型的比较（嵌套和非嵌套）。项目的这一部分开发并评估了一种技术，该技术可用于在同时涉及嵌套模型和非嵌套模型的比较时评估准确性。这项研究可以帮助经济学家和政策制定者更好地确定能够持续改进预测的模型。该方法技术适用于美国的通货膨胀和产出增长。本研究的第四个主题涉及对一系列商品价格的预测。本研究是实证研究。对石油、锡或橡胶等大宗商品价格的大多数预测都集中在单一大宗商品上。研究者考虑一种商品的价格变动如何帮助预测另一种商品的价格变动。

The investigator studies four distinct topics, with the first somewhat larger in scope than the second and third, which in turn are somewhat larger in scope than the fourth. The first topic is methodological, concerning statistical bias in certain commonly studied economic relations. "Bias" is a precise statistical term that refers to a tendency to systematically estimate a value that is wrong, on average. In economic data, biased estimation leads to forecasts and economic advice that are misleading, and possibly very misleading if the bias is large. Examples of the relevant economic relations include those between stock returns and dividend yields, exchange rate changes and forward premiums, and interest rates and inflation. Estimates of these important relations are used in forecasting economic activity, making financial decisions and deciding economic policy. Disparate pieces of previous evidence have suggested bias in estimates of these relations produced using a technique called regression. The investigator develops a general, unifying framework that allows systematic study, interpretation and, if necessary, correction for bias in such regressions. The advantage of the general framework is that it lets researchers understand whether biases are likely to be large or small under a given scenario. The results can be used to interpret estimation and inference in the relevant regressions, to improve estimation and inference, and to understand simulation results from economic models. Importantly, the approach allows for complications that are common in economic data; econometric terms for these complications are serial correlation and conditional heteroskedasticity. The investigator applies the results to regressions of exchange rate returns on the forward premium. The second topic that the investigator studies, is related to how monetary policy affects floating exchange rates between countries with roughly similar inflation rates. This research is empirical, and relies on a technique called vector autoregressions. Standard macroeconomic models focus on a direct link from monetary policy, measured as the difference between U.S. and foreign interest rates, to the value of the U.S. dollar. These models treat any other factors that influence exchange rates as evolving exogenously to monetary policy. The investigator proposes and develops an approach that allows the researcher to evaluate whether surprise movements in monetary policy in fact affect exchange rates solely through interest rates or also via an impact on excess returns or risk premia. This research improves our understanding of monetary policy. The third topic that this project investigates is how to compare the accuracy of forecasts when there are complicated relationships among the forecasting models being evaluated. This research is methodological. In economic research, some forecast competitions compare a simple model and complicated models, with the simple model a special case of the more complicated models (called "nested" model comparisons). Other forecast competitions evaluate complicated models where no model is a special case of other models ("nonnested"). Finally, there are some forecast competitions that simultaneously involve both types of comparisons (nested and nonnested). That part of the project develops and evaluates a technique that can be used to evaluate accuracy when the comparison simultaneously involves nested and nonnested models. This research helps economists and policy makers better identify models that lead to lasting improvements in prediction. The methodological technique is applied to U.S. inflation and output growth. The fourth topic of this study concerns the production of forecasts of a set of commodity prices. This research is empirical. Most forecasts of prices of commodities such as oil, tin or rubber focus on a single commodity. The investigator considers how movements in the price of one commodity help forecast movements in the price of another commodity.