Investment in China by Hong Kong Companies
Publication Type
Journal Article
Publication Date
1991
Abstract
This paper develops a regression limit theory for discrete choice nonstationary panels with large cross section (N) and time series (T) dimensions. Some results emerging from this theory are directly applicable in the wider context of M-estimation. This includes an extension of work by Wooldridge [Wooldridge, J.M., 1994. Estimation and Inference for Dependent Processes. In: Engle, R.F., McFadden, D.L. (Eds.). Handbook of Econometrics, vol. 4, North-Holland, Amsterdam] on the limit theory of local extremum estimators to multi-indexed processes in nonlinear nonstationary panel data models. It is shown that the maximum likelihood (ML) estimator is consistent without an incidental parameters problem and has a limit theory with a fast rate of convergence N1/2T3/4 (in the stationary case, the rate is N1/2T1/2) for the regression coefficients and thresholds, and a normal limit distribution. In contrast, the limit distribution is known to be mixed normal in time series modeling, as shown in [Park, J.Y., Phillips, P.C.B., 2000, Nonstationary binary choice. Econometrica, 68, 1249-1280] (hereafter PP), and [Phillips, P.C.B., Jin, S., Hu, L., 2007. Nonstationary discrete choice: A corrigendum and addendum. Journal of Econometrics 141(2), 1115-1130] (hereafter, PJH). The approach is applied to exchange rate regime choice by monetary authorities, and we provide an analysis of the empirical phenomenon known as fear of floating.
Discipline
Asian Studies | International Business | International Economics
Research Areas
Econometrics
Publication
IDS Bulletin (Institute of Development Studies Bulletin)
Volume
22
Issue
2
First Page
44
Last Page
52
ISSN
1759-5436
Publisher
Institute of Development Studies
Citation
LEUNG, Hing-Man; Thoburn, J. T.; Chua, E.; and Tang, S. H..
Investment in China by Hong Kong Companies. (1991). IDS Bulletin (Institute of Development Studies Bulletin). 22, (2), 44-52.
Available at: https://ink.library.smu.edu.sg/soe_research/539