Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Shan Dai, Ngai Hang Chan
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引用次数: 0

Abstract

Cointegration has been widely used in macroeconomics and financial time series analysis, but traditional linear cointegration relationship is often rejected in empirical applications. Many constant parameters testing methods in semi-parametric functional coefficient cointegrated framework have been developed accordingly. However, there are little studies on constant parameters testing problem for the case that the index variable is integrated of order one. From a practical point of view, there is also a need for a test that accommodates integrated index variable in functional coefficient cointegrated setting, for example, in the study of the purchasing power parity hypothesis. In this article, an orthogonal series approximation-based test statistic is proposed to tackle the problem. The asymptotic results are also studied. Monte Carlo experiments are conducted to evaluate the finite sample performance of the proposed test, and an empirical example about price and exchange rate data is provided.

含协变量的半参数泛函系数模型的常参数检验
协整在宏观经济和金融时间序列分析中得到了广泛的应用,但传统的线性协整关系在实证应用中往往被拒绝。相应的,在半参数泛函系数协整框架中发展了许多常参数检验方法。然而,对于指标变量为1阶积分情况下的常参数检验问题,研究较少。从实践的角度来看,也需要在功能系数协整设置中容纳综合指数变量的检验,例如在购买力平价假设的研究中。本文提出了一种基于正交序列近似的检验统计量来解决这一问题。并对渐近结果进行了研究。通过蒙特卡罗实验对所提方法的有限样本性能进行了评价,并给出了一个关于价格和汇率数据的实证例子。
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来源期刊
Journal of Time Series Analysis
Journal of Time Series Analysis 数学-数学跨学科应用
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.
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