Adding cycles into the neoclassical growth model

We propose a stochastic Solow growth model where a cyclical component is added to the total factor productivity process. Theoretically, an important feature of the model is that its main equation takes a state space representation where key parameters can be estimated via an unobserved component approach without involving capital stock measures. In addition, the dynamic properties of the model are mostly unaffected by the newly introduced cyclical component. Empirically, our novel framework is consistent with secular U.S. empirical evidence.     

增加农民收入的若干建议

农民的收入水平不仅关系农民生活的改善,还关系着整个国民经济的持续、健康、快速发展。农民收入问题是“三农”问题的核心。加入WTO后,农业又面临冲击。在这种背景下,要解决农民增收问题,必须从解决制约农业、农村经济发展的深层次问题入手。 一、通过发展农业产业化经营,完善利益联结机制,带动农民收入增长 改革开放后,农村实行家庭联产承包责任制,重塑了农村经济组织的微观基础,农民成为独立的生产经营者,形成     

Adding space to the international business cycle

Growth fluctuations exhibit substantial synchronization across countries, which has been viewed as reflecting a global business cycle driven by shocks with worldwide reach, or spillovers resulting from local real and/or financial linkages between countries. This paper brings these two perspectives together by analyzing international growth fluctuations in a setting that allows for both global shocks and spatial dependence. Using annual data for 117 countries over 1970–2016, the paper finds that the cross-country dependence of aggregate growth is the combined result of global shocks summarized by a latent common factor and spatial effects accruing through the growth of nearby countries – with proximity measured by bilateral trade linkages or geographic distance. The latent global factor shows a strong positive correlation with worldwide TFP growth. Countries’ exposure to global shocks is positively related to their openness to trade and the degree of commodity specialization of their economies, and negatively to their financial depth. Despite its simplicity, the empirical model fits the data well. Ignoring the cross-country dependence of growth, by omitting spatial effects or common shocks (or both) from the analysis, leads to a marked deterioration of the empirical model’s in-sample explanatory power and out-of-sample forecasting performance.