The University of Southampton
University of Southampton Institutional Repository

Testing convergence using HAR Inference

Testing convergence using HAR Inference
Testing convergence using HAR Inference
Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak O-convergence tests, have recently been developed (Kong et al., 2019)) to evaluate such trends in dispersion in panel data using simple linear trend regressions.
To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present paper examines the behavior of these convergence tests when heteroskedas- tic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are
reflected in finite sample performance in numerical exercises. The explanation is that weak O-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.
0731-9053
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Kong, Jianning
df500309-26de-4b2b-a604-9335dcb93f47
Sul, Donggyu
4437e259-2402-4223-b027-aea2a1dfb730
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Kong, Jianning
df500309-26de-4b2b-a604-9335dcb93f47
Sul, Donggyu
4437e259-2402-4223-b027-aea2a1dfb730

Phillips, Peter Charles Bonest, Kong, Jianning and Sul, Donggyu (2019) Testing convergence using HAR Inference. Advances in Econometrics. (In Press)

Record type: Article

Abstract

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak O-convergence tests, have recently been developed (Kong et al., 2019)) to evaluate such trends in dispersion in panel data using simple linear trend regressions.
To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present paper examines the behavior of these convergence tests when heteroskedas- tic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are
reflected in finite sample performance in numerical exercises. The explanation is that weak O-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

Text
HAR_22_pcb - Accepted Manuscript
Available under License Other.
Download (491kB)

More information

Accepted/In Press date: 7 June 2019

Identifiers

Local EPrints ID: 431811
URI: https://eprints.soton.ac.uk/id/eprint/431811
ISSN: 0731-9053
PURE UUID: d491568d-d9a9-4bb8-9556-a5fce262bfe5
ORCID for Peter Charles Bonest Phillips: ORCID iD orcid.org/0000-0003-2341-0451

Catalogue record

Date deposited: 18 Jun 2019 16:30
Last modified: 10 Sep 2019 04:01

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×