论文标题
建模长周期
Modeling Long Cycles
论文作者
论文摘要
经常性的繁荣和障碍周期是经济和财务历史的显着特征。数据中发现的周期是随机的，通常是高度持久的，并且涵盖了样本量的大量部分。我们将这样的周期称为“长”。在本文中，我们开发了一种新颖的方法来建模专门为捕获长周期而设计的周期性行为。我们表明，现有的推论程序可能会在存在长周期的情况下产生误导性结果，并提出了一种新的计量经济学程序来推断周期长度。无论循环长度如何，我们的程序在渐近上都是有效的。我们将方法应用于美国的一组宏观经济和财务变量，我们发现标准商业周期变量以及信贷和房价中长期随机周期的证据。但是，我们排除资产市场数据中随机周期的存在。此外，根据我们的结果，以信用和房价为特征的财务周期往往是商业周期的两倍。
Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent, and span substantial fractions of the sample size. We refer to such cycles as "long". In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in the presence of long cycles, and propose a new econometric procedure for the inference on the cycle length. Our procedure is asymptotically valid regardless of the cycle length. We apply our methodology to a set of macroeconomic and financial variables for the U.S. We find evidence of long stochastic cycles in the standard business cycle variables, as well as in credit and house prices. However, we rule out the presence of stochastic cycles in asset market data. Moreover, according to our result, financial cycles as characterized by credit and house prices tend to be twice as long as business cycles.
