A Principal Components Approach to Cross-Section Dependence in Panels



Table 5(A). Long run PPP coefficient estimates (CPI)

MG_______FE

POLS

i)DM____________________________________________

ADF

P [SE1]

-.0377[.0037]   -.0237[.0029]

-.0127[.0018]

ADF(Wτ )

P [SE1]

-.0398[.0036]   -.0247[.0024]

-.0131[.0015]

ARDL

Y [SE1]

-.01967[.0069] -.0044[.00079]

-.0045[.00071]

ARDL(Wτ )

Y [SE1]

-.01989[.0087] -.0041[.00066]

-.0041[.00059]

ii) US$__________________________________________________________

ADF

P [SE1]

-.0256[.0017] -.0225[.0028]

-5.96e-5[.00021]

ADF(Wτ )

P [SE1]

-.0274[.0019] -.0245[.0016]

-4.86e-5[.00012]

ARDL

Y [SE1]

-.0064[.0056] -.0025[.0014]

-.0034[.0012]

ARDL(Wτ )

Y [SE1]

-.0024[.0030] -.0024[.00077]

-.0040[.00067]

Note: Tables 5A-5B report the estimation results for two dynamic PPP equa-
tions, ADF and ARDL. The number of augmentation lags is conservatively set at
k=6 in all equations to eliminate serial correlation. ADF(
Wτ) and ARDL(Wτ)
denote the models with
τ factors as additional regressors. The conventional s.e.
for the MG, POLS and FE estimators are in brackets. These are likely to be biased
downwards in all regressions.

27



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