Trending Economic Time Series and Panels

经济数据的一个主要特点是它们随时间推移而呈现趋势。这种行为包括利率、汇率和通货膨胀的游荡性及其长期和不稳定的周期，以及GDP等序列的长期漂移。 重要的社会经济问题，如国家经济增长、增长的决定因素以及各国之间的增长差距，都与趋势问题有关。许多现代时间序列计量经济学，以及最近的面板数据研究，都关注这种趋势行为的统计分析，包括不同序列趋势之间可能的相互联系。经济理论提供了关于趋势和联系的潜在来源的见解，但它几乎没有提供关于经验研究的适当公式的实际指导。 这项研究将模拟在这种情况下的经济数据的趋势，并制定一个统计推断的理论，允许不正确的规范的趋势，在应用工作。 该项目将开发一种正式的仪器，用于研究常见的拟合趋势函数，并开发方法，用于在一个坐标系的背景下有效地解释这些拟合趋势，该坐标系旨在捕获数据中的趋势和长周期行为，并在一个更一般的假设下保持。使用坐标函数，可以分析单个系列中的趋势和多个系列中的共同运动，同时仅对生成机制的类别施加弱限制。这项研究将为这种方法开发新的计量经济学方法，并探索它们与为真实生成过程已知的情况开发的传统方法的关系。该研究将涉及一个渐近理论的推理和趋势预测，模拟实验，以评估性能的方法，并与宏观经济和金融数据集的实证应用。这些方法将有助于我们理解与经济增长有关的更广泛的社会经济问题，并将用于评估关于世界经济在开始趋同之前的过渡时期出现分歧这一假设的经验证据。 因此，这项研究的结果将使社会科学家和统计学家能够对迄今为止尚不清楚趋势的数据进行建模和正确分析。

A dominant characteristic of economic data is their tendency to trend over time. This behavior includes the wandering nature of interest rates, exchange rates, and inflation with their long and erratic cycles, as well as the secular drift of series like GDP. Important socioeconomic issues such as national economic growth, the determinants of growth, and disparities in growth across nations all relate to matters of trend. Much of modern time series econometrics, as well as recent work on panel data, is concerned with the statistical analysis of such trend behavior, including the possible interconnectedness of the trends across different series. Economic theory provides insights about potential sources of trend and linkages but it gives little practical guidance concerning suitable formulations for empirical research. This research will model trends in economic data in such situations and develop a theory of statistical inference that allows for incorrect specification of the trends in applied work. The project will develop a formal apparatus for studying commonly fitted trend functions and develop methods for validly interpreting these fitted trends within the context of a coordinate system that is designed to capture trends and long cyclical behavior in the data under a much more general maintained hypothesis. Using coordinate functions, it is possible to analyze trends in a single series and co-movement in several series while placing only weak restrictions on the class of generating mechanisms. The research will develop new econometric methods for this approach and explore their relationship to conventional methods developed for cases where the true generating process is known. The research will involve an asymptotic theory for inference and for trend forecasting, simulation experiments to evaluate performance characteristics of the methods, and empirical applications with both macro-economic and financial data sets. The methods will contribute to our understanding of wider socioeconomic issues related to economic growth and they will be used to assess empirical evidence on the hypothesis that the world economy diverges during a period of transition before it starts to converge. Thus, the results of this research will allow social scientists and statistician to model and correctly analyze data whose trends had hitherto been poorly understood.