dc.contributor.author |
Huang, Wenxin |
|
dc.contributor.author |
Jin, Sainan |
|
dc.contributor.author |
Phillips, Peter CB |
|
dc.contributor.author |
Su, Liangjun |
|
dc.date.accessioned |
2022-03-03T02:18:21Z |
|
dc.date.available |
2022-03-03T02:18:21Z |
|
dc.date.issued |
2021-3-1 |
|
dc.identifier.citation |
Journal of Econometrics 221(1):198-222 01 Mar 2021 |
|
dc.identifier.issn |
0304-4076 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/58404 |
|
dc.description.abstract |
This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an asymptotically non-negligible bias from correlations between the nonstationary regressors and unobserved stationary common factors and/or the equation errors. To remedy these shortcomings we provide three bias-correction procedures under which the estimators are re-centered about zero as both dimensions (N and T) of the panel tend to infinity. We establish a mixed normal limit theory for the estimators of the group-specific long-run coefficients, which permits inference using standard test statistics. Simulations suggest good finite sample performance. An empirical application applies the methodology to study international R&D spillovers and the results offer a convincing explanation for the growth convergence puzzle through the heterogeneous impact of R&D spillovers. |
|
dc.language |
en |
|
dc.publisher |
Elsevier BV |
|
dc.relation.ispartofseries |
Journal of Econometrics |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
dc.subject |
Social Sciences |
|
dc.subject |
Science & Technology |
|
dc.subject |
Physical Sciences |
|
dc.subject |
Economics |
|
dc.subject |
Mathematics, Interdisciplinary Applications |
|
dc.subject |
Social Sciences, Mathematical Methods |
|
dc.subject |
Business & Economics |
|
dc.subject |
Mathematics |
|
dc.subject |
Mathematical Methods In Social Sciences |
|
dc.subject |
Nonstationarity |
|
dc.subject |
Parameter heterogeneity |
|
dc.subject |
Latent group patterns |
|
dc.subject |
Penalized principal component |
|
dc.subject |
Cross-section dependence |
|
dc.subject |
Classifier Lasso |
|
dc.subject |
R&D spillover |
|
dc.subject |
0104 Statistics |
|
dc.subject |
1402 Applied Economics |
|
dc.subject |
1403 Econometrics |
|
dc.title |
Nonstationary panel models with latent group structures and cross-section dependence |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.jeconom.2020.05.003 |
|
pubs.issue |
1 |
|
pubs.begin-page |
198 |
|
pubs.volume |
221 |
|
dc.date.updated |
2022-02-07T00:10:26Z |
|
dc.rights.holder |
Copyright: Elsevier BV |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000615745700011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
|
pubs.end-page |
222 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Article |
|
pubs.subtype |
Journal |
|
pubs.elements-id |
810370 |
|
dc.identifier.eissn |
1872-6895 |
|