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
More intriguing information
1. The name is absent2. Behavior-Based Early Language Development on a Humanoid Robot
3. The Institutional Determinants of Bilateral Trade Patterns
4. The name is absent
5. How do investors' expectations drive asset prices?
6. The name is absent
7. Innovation Trajectories in Honduras’ Coffee Value Chain. Public and Private Influence on the Use of New Knowledge and Technology among Coffee Growers
8. Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis
9. Prizes and Patents: Using Market Signals to Provide Incentives for Innovations
10. The name is absent