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结构不稳定下的面板数据协整检验AI 解读

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article.The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components.We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach.This…

结构 FECM：大规模结构 FAVAR 模型中的协整AI 解读

Summary Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by…

The power of standard panel cointegration statistics may be affected by misspecification errors if structural breaks in the parameters generating the process are not considered. In addition, the presence of cross-section dependence among the panel units can distort the empirical size of the statistics. We therefore design a testing procedure that allows for both structural breaks and cross-section dependence when testing the null hypothesis of no cointegration. The paper…

库存与销售多项式协整下线性二次模型的重新解释AI 解读

Abstract Estimation of the linear quadratic model, the workhorse of the inventory literature, traditionally takes inventories and sales to be first‐difference stationary series, and the ratio of the two variables to be stationary. However, these assumptions do not always match the properties of the data for the last two decades in the United States. We propose a model that allows for the non‐stationary characteristics of the data, using polynomial cointegration. We show that…

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单位根与趋势突变假说的递归和序贯检验：理论与国际证据AI 解读

This paper investigates the possibility, raised by P. Perron (1989, 1990) and P. Rappoport and L. Reichlin (1989), that aggregate economic time series can be characterized as being stationary around broken trend lines. Unlike those authors, the authors treat the break date as unknown a priori. Asymptotic distributions are developed for recursive, rolling, and sequential tests for unit roots and/or changing coefficients in time series regressions. When applied to data on real…