A parametric approach to the estimation of cointegration vectors in panel data



Table 1: Estimation bias of various cointegration estimators

Bias

two-stage

FM-OLS

DOLS

OLS

T =

N

10

15

-0.1363

-0.4280

-0.4294

-0.4824

15

-0.1304

-0.6487

-0.6487

-0.7476

20

-0.0900

-0.5675

-0.5675

-0.6811

30

-0.0300

-0.4578

-0.4578

-0.5759

50

0.0124

-0.3233

-0.3233

-0.4390

100

0.0233

-0.1842

-0.1842

-0.2732

T =

N=

20

15

-0.1316

-0.6439

-0.6439

-0.7524

20

-0.0884

-0.5654

-0.5654

-0.6832

30

-0.0327

-0.4515

-0.4515

-0.5752

50

0.0091

-0.3158

-0.3158

-0.4362

100

0.0198

-0.1766

-0.1766

-0.2688

__________________RMSE__________________

two-stage

FM-OLS

DOLS

OLS

T =

N=

10

15

0.2100

0.6806

0.6806

0.7651

20

0.1723

0.5999

0.5999

0.6983

30

0.1227

0.4874

0.4874

0.5926

50

0.0883

0.3485

0.3485

0.4541

100

0.0650

0.2016

0.2016

0.2849

T =

N =

20

15

0.1746

0.6604

0.6604

0.7611

20

0.1355

0.5813

0.5813

0.6921

30

0.0909

0.4664

0.4664

0.5840

50

0.0633

0.3284

0.3284

0.4438

100

0.0461

0.1851

0.1851

0.2749

Note: The entries of the Table report the estimated bias and root mean
squared error (RMSE) of the cointegration parameter
b based on 5000 replica-
tion of the model (16). “two-step” indicates the two-step estimator suggested
in Section 3, “FM-OLS” denotes the Fully-modified panel cointegration esti-
mator suggested by Pedroni (1996), “DOLS” is the dynamic OLS estimator of
Kao and Chiang (2000), and “OLS” indicates the ordinary least-squares esti-
mator of the pooled model, where the first variable is regressed on the second
variable.

22



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