On business cycle fluctuations in USA macroeconomic time series

被引:6
|
作者
Kiani, Khurshid M. [1 ]
机构
[1] Gulf Univ Sci & Technol, Coll Business Adm, N1-128,Block 5,Bldg 1,Masjid Al Aqsa St, Mubarak Al Abdullah Area, West Mishref, Kuwait
关键词
Asymmetries; Nonlinearities; Neural networks; Jackknife out-of-sample forecasts; Stable distributions; Conditional heteroskedasticity; Long memory; Business cycle fluctuations; Monetary policy; MODEL-SELECTION APPROACH; ASYMMETRIC BEHAVIOR; LINEAR-MODELS; RECESSIONS; PARAMETER; RATES;
D O I
10.1016/j.econmod.2015.11.022
中图分类号
F [经济];
学科分类号
02 ;
摘要
This study employs eighteen USA macroeconomic time series variables to investigate possible existence of asymmetries in business cycle fluctuations in the series. Detection of asymmetric fluctuations in economic activity is important for policymakers since effective monetary policy relies on asymmetric business cycle fluctuations in all the series. The asymmetric deviations from the long-term growth trend in each of the series are modeled using regime switching models and artificial neural networks. The results based on nonlinear switching time series models reveal strong evidence of business cycle asymmetries in most of the series. The results based on in-sample approximations from artificial neural networks show statistically significant evidence of asymmetries in all the series. Similar results are obtained when jackknife out-of-sample approximations from artificial neural networks are used. Thus, the study results show statistically significant evidence of asymmetries in all the series which indicates that business cycle fluctuations in the series are asymmetric, thus alike. Therefore, the impact of monetary policy shocks on the output and the other macroeconomic variables can be anticipated using nonlinear models only. The results on asymmetric business cycle fluctuations in real GDP are in line with recent studies but in sharp contrast with Balke and Fomby (1994). (C) 2015 Elsevier B.V. All rights reserved.
引用
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页码:179 / 186
页数:8
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