國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Three essays in econometrics.

Lin, Chang-Ching.

Three essays in econometrics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Three essays in econometrics.
作者:	Lin, Chang-Ching.
面頁冊數:	154 p.
附註:	Adviser: Serena Ng.
附註:	Source: Dissertation Abstracts International, Volume: 67-07, Section: A, page: 2693.
Contained By:	Dissertation Abstracts International67-07A.
標題:	Economics, Theory.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3224679
ISBN:	9780542787676

Three essays in econometrics.
Lin, Chang-Ching.

Three essays in econometrics. - 154 p.

Adviser: Serena Ng.

Thesis (Ph.D.)--University of Michigan, 2006.

The second essay considers how to improve the accuracy of the kernel estimator of long run covariance matrices in time series analysis. Despite its asymptotic efficiency, the truncated flat (TF) kernel estimator of long-run covariance matrices is seldom used because the TF estimate is not always positive semidefinite. We propose estimators that are always positive semidefinite, yet inherit the asymptotic efficiency of the TF estimator and allow for a non-integer bandwidth. We also propose a bandwidth selection method for the proposed estimators, which keeps them asymptotically efficient relative to other familiar kernel estimators. Simulations show that the proposed estimators outperform existing kernel estimators in terms of the MSE.

ISBN: 9780542787676Subjects--Topical Terms:

212740
Economics, Theory.

Three essays in econometrics.
LDR:03170nmm _2200277 _450 001 180600
005 20080111103750.5
008 090528s2006 eng d
020 $a 9780542787676
035 $a 00311625
040 $a UMI $c UMI
100 0 $a Lin, Chang-Ching. $3 264176
245 1 0 $a Three essays in econometrics.
300 $a 154 p.
500 $a Adviser: Serena Ng.
500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: A, page: 2693.
502 $a Thesis (Ph.D.)--University of Michigan, 2006.
520 # $a The second essay considers how to improve the accuracy of the kernel estimator of long run covariance matrices in time series analysis. Despite its asymptotic efficiency, the truncated flat (TF) kernel estimator of long-run covariance matrices is seldom used because the TF estimate is not always positive semidefinite. We propose estimators that are always positive semidefinite, yet inherit the asymptotic efficiency of the TF estimator and allow for a non-integer bandwidth. We also propose a bandwidth selection method for the proposed estimators, which keeps them asymptotically efficient relative to other familiar kernel estimators. Simulations show that the proposed estimators outperform existing kernel estimators in terms of the MSE.
520 # $a The third essay proposes a robust method to examine if the parameters of a panel data model are constant across individuals in the population. I utilize the L-moment to measure the dispersion of the estimates to propose a test, which is robust against outlying observations. The test statistic asymptotically has the standard normal distribution under the null hypothesis, as long as some mild conditions are met. My simulations show that this test delivers a more accurate size than existing tests in panel data models. The proposed test is also applied to examine whether or not the rates of convergence of economic growth are the same across countries.
520 # $a This dissertation comprises three essays on econometric methodologies. The first essay investigates the asymptotic properties of the fixed effects (FE) estimator of the autoregressive conditional heteroskedastic (ARCH) model with individual-specific effects using panel data. I establish the consistency and asymptotic normality of the FE-ARCH estimator in a large-N-and- T asymptotic framework and show that its asymptotic bias is Op(1/T). I also propose a procedure to examine whether or not the ARCH effects exist. The test statistic has a chi2 (1) limiting distribution, and simulations show that it has an accurate finite sample size and satisfactory power. The proposed test and estimator are used to detect and estimate the ARCH effects in individual earnings using the PSID data. The ARCH(1) effect is statistically significant in earnings dynamics.
590 $a School code: 0127.
650 # 0 $a Economics, Theory. $3 212740
690 $a 0511
710 0 # $a University of Michigan. $3 212464
773 0 # $g 67-07A. $t Dissertation Abstracts International
790 $a 0127
790 1 0 $a Ng, Serena, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3224679 $z http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3224679