An Integrated Panel Data Approach to Modelling Economic Growth

Jiti Gao (), Guangming Pan (), Yanrong Yang () and Bo Zhang ()

No 9/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Accurate estimation for extent of cross-sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data with weak relations (cross-sectional dependence) together often results in less efficient dimension reduction and worse forecasting. This paper describes cross-sectional dependence among a large number of objects (time series) via a factor model and parametrizes its extent in terms of strength of factor loadings. A new joint estimation method, benefiting from unique feature of dimension reduction for high dimensional time series, is proposed for the parameter representing the extent and some other parameters involved in the estimation procedure. Moreover, a joint asymptotic distribution for a pair of estimators is established. Simulations illustrate the effectiveness of the proposed estimation method in the finite sample performance. Applications in cross-country macro-variables and stock returns from S&P 500 are studied.

Keywords: cross-sectional dependence; factor model; joint estimation; large panel data analysis; marginal estimation. (search for similar items in EconPapers)
JEL-codes: C21 C32 (search for similar items in EconPapers)
Pages: 47
Date: 2019
New Economics Papers: this item is included in nep-bec and nep-ore
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