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Estimation and Inference in Cointegrated Panels.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Estimation and Inference in Cointegrated Panels./
Author:	Li, Yi.
Description:	1 online resource (192 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
Subject:	Economics. -
Online resource:	click for full text (PQDT)
ISBN:	9781369730050

Estimation and Inference in Cointegrated Panels.
Li, Yi.

Estimation and Inference in Cointegrated Panels. - 1 online resource (192 pages)

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.

Thesis (Ph.D.)--Michigan State University, 2017.

Includes bibliographical references

This dissertation investigates parameter estimation and inference in cointegrated panel data model. In Chapter 1, for homogeneous cointegrated panels, a simple, new estimation method is proposed based on Vogelsang and Wagner [2014]. The estimator is labeled panel integrated modified ordinary least squares (panel IM-OLS). Similar to panel fully modified ordinary least squares (panel FM-OLS) and panel dynamic ordinary least squares (panel DOLS), the panel IM-OLS estimator has a zero mean Gaussian mixture limiting distribution. However, panel IM-OLS does not require estimation of long run variance matrices and avoids the need to choose tuning parameters such as kernel functions, bandwidths, leads and lags. Inference based on panel IM-OLS estimates does require an estimator of a scalar long run variance, and critical values for test statistics are obtained from traditional and fixed-b methods. The properties of panel IM-OLS are analyzed using asymptotic theory and finite sample simulations. Panel IM-OLS performs well relative to other estimators. Chapter 2 compares asymptotic and bootstrap hypothesis tests in cointegrated panels with cross-sectional uncorrelated units and endogenous regressors. All the tests are based on the panel IM-OLS estimator from Chapter 1. The aim of using the bootstrap tests is to deal with the size distortion problems in the finite samples of fixed-b tests. Finite sample simulations show that the bootstrap method outperforms the asymptotic method in terms of having lower size distortions. In general, the stationary bootstrap is better than the conditional-on-regressors bootstrap, although in some cases, the conditional-on-regressors bootstrap has less size distortions. The improvement in size comes with only minor power losses, which can be ignored when the sample size is large. Chapter 3 is concerned with parameter estimation and inference in a more general case than Chapter 1 with endogenous regressors and heterogeneous long run variances in the cross section. In addition, the model allows a limited degree of cross-sectional dependence due to a common time effect. The panel IM-OLS estimator is provided for this less restricted model. Similar as in Chapter 1, this panel IM-OLS estimator has a zero mean Gaussian mixture limiting distribution. However, standard asymptotic inference is infeasible due to the existence of nuisance parameters. Inference based on panel IM-OLS relies on the stationary bootstrap. The properties of panel IM-OLS are analyzed using the stationary bootstrap in finite sample simulations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369730050Subjects--Topical Terms:

555568
Economics.
Index Terms--Genre/Form:

554714
Electronic books.

Estimation and Inference in Cointegrated Panels.
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245 1 0 $a Estimation and Inference in Cointegrated Panels.
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300 $a 1 online resource (192 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
500 $a Adviser: Tim Vogelsang.
502 $a Thesis (Ph.D.)--Michigan State University, 2017.
504 $a Includes bibliographical references
520 $a This dissertation investigates parameter estimation and inference in cointegrated panel data model. In Chapter 1, for homogeneous cointegrated panels, a simple, new estimation method is proposed based on Vogelsang and Wagner [2014]. The estimator is labeled panel integrated modified ordinary least squares (panel IM-OLS). Similar to panel fully modified ordinary least squares (panel FM-OLS) and panel dynamic ordinary least squares (panel DOLS), the panel IM-OLS estimator has a zero mean Gaussian mixture limiting distribution. However, panel IM-OLS does not require estimation of long run variance matrices and avoids the need to choose tuning parameters such as kernel functions, bandwidths, leads and lags. Inference based on panel IM-OLS estimates does require an estimator of a scalar long run variance, and critical values for test statistics are obtained from traditional and fixed-b methods. The properties of panel IM-OLS are analyzed using asymptotic theory and finite sample simulations. Panel IM-OLS performs well relative to other estimators. Chapter 2 compares asymptotic and bootstrap hypothesis tests in cointegrated panels with cross-sectional uncorrelated units and endogenous regressors. All the tests are based on the panel IM-OLS estimator from Chapter 1. The aim of using the bootstrap tests is to deal with the size distortion problems in the finite samples of fixed-b tests. Finite sample simulations show that the bootstrap method outperforms the asymptotic method in terms of having lower size distortions. In general, the stationary bootstrap is better than the conditional-on-regressors bootstrap, although in some cases, the conditional-on-regressors bootstrap has less size distortions. The improvement in size comes with only minor power losses, which can be ignored when the sample size is large. Chapter 3 is concerned with parameter estimation and inference in a more general case than Chapter 1 with endogenous regressors and heterogeneous long run variances in the cross section. In addition, the model allows a limited degree of cross-sectional dependence due to a common time effect. The panel IM-OLS estimator is provided for this less restricted model. Similar as in Chapter 1, this panel IM-OLS estimator has a zero mean Gaussian mixture limiting distribution. However, standard asymptotic inference is infeasible due to the existence of nuisance parameters. Inference based on panel IM-OLS relies on the stationary bootstrap. The properties of panel IM-OLS are analyzed using the stationary bootstrap in finite sample simulations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Economics. $3 555568
655 7 $a Electronic books. $2 local $3 554714
690 $a 0501
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Michigan State University. $b Economics - Doctor of Philosophy. $3 1189337
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272713 $z click for full text (PQDT)