Steady state adjusting trends using a data-driven local polynomial regression |
| |
| Institution: | 1. School of Economics and Management, Beihang University, Beijing, 100191, China;2. College of Business, University of Nevada, Reno, Reno, NV 89557, USA;1. Faculty of Economics, Chulalongkorn University, Bangkok, Thailand;2. Department of Econometrics and Business Statistics, Monash University, Victoria, Australia;1. School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, 310018, PR China;2. Department of Statistics, The University of British Columbia, Vancouver, BC, Canada;1. INCEIF, Kuala Lumpur, Malaysia;2. Taylor''s Business School, Taylor''s University, Malaysia;3. Nottingham University Business School, University of Nottingham Malaysia Campus, Semenyih, Malaysia;4. Suleman Dawood School of Business, Lahore University of Management Sciences (LUMS), Lahore, Pakistan;1. School of Economics and Finance, Xi’an Jiaotong University, Shaanxi, China;2. Business School, University of Jinan, China;3. Finance Research Institute, Shandong Collaborative Innovation Center for Capital Market Innovation and Development, University of Jinan, China;1. Accident Compensation Corporation, Wellington, New Zealand;2. School of Economics, Finance and Marketing, RMIT University, Building 80, Level 11, 445 Swanston Street, Melbourne, VIC 3000, Australia |
| |
| Abstract: | Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence. |
| |
| Keywords: | Nonparametric model Nonstationary process Time series models Empirical growth trends C14 C22 O47 |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
