Model specification test with correlated but not cointegrated variables Academic Article

abstract

• Many macroeconomic and financial variables show highly persistent and correlated patterns but are not necessarily cointegrated. Recently, Sun et al. (2011) propose using a semiparametric varying coefficient approach to capture correlations between integrated but non cointegrated variables. Due to the complication arising from the integrated disturbance term and the semiparametric functional form, consistent estimation of such a semiparametric model requires stronger conditions than usually needed for consistent estimation for a linear (spurious) regression model, or a semiparametric varying coefficient model with a stationary disturbance. Therefore, it is important to develop a testing procedure to examine for a given data set, whether linear relationship holds or not, while allowing for the disturbance being an integrated process. In this paper we propose two test statistics for detecting linearity against semiparametric varying coefficient alternative specification. Monte Carlo simulations are used to examine the finite sample performances of the proposed tests.

authors

• Gan, Li

published proceedings

• JOURNAL OF ECONOMETRICS

author list (cited authors)

• Gan, L. i., Hsiao, C., & Xu, S.

citation count

• 3

complete list of authors

• Gan, Li||Hsiao, Cheng||Xu, Shu

publication date

• January 2014

publisher

• Elsevier  Publisher

published in

• Journal of Econometrics  Journal

keywords

• Kernel Estimation
• Specification Test
• Spurious Regression
• Varying Coefficient

Digital Object Identifier (DOI)

• 10.1016/j.jeconom.2013.08.008

start page

• 80

end page

• 85

volume

• 178

issue

• PART 1

URL

• http://dx.doi.org/10.1016/j.jeconom.2013.08.008