The aggregate consumption puzzle in Singapore
The aggregate consumption puzzle in Singapore
The aggregate consumption puzzle in Singapore
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574 T. Abeys<strong>in</strong>ghe, K.M. Choy / Journal of Asian Economics 15 (2004) 563–578<br />
0.7<br />
0.6<br />
0.5<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
APC<br />
198 198 199 199 200<br />
loan<br />
198 198 199 199 200<br />
22.<br />
20.<br />
17.<br />
15.<br />
0.0<br />
0.0<br />
0.0<br />
Fig. 5. <strong>The</strong> APC and key ratios.<br />
f<strong>in</strong>ancial wealth<br />
198 198 199 199 200<br />
visitor expenditure<br />
198 198 199 199 200<br />
differently, a unitary <strong>in</strong>come elasticity conditional on constant wealth–<strong>in</strong>come, loan–<br />
<strong>in</strong>come, and visitor expenditure–<strong>in</strong>come ratios.<br />
<strong>The</strong> regression results for (10) are shown <strong>in</strong> column (3) of Table 2 (the estimate of the<br />
constant term is not reported). <strong>The</strong> residual-based ADF t-statistic of 4.68 is significant at<br />
the 1% level accord<strong>in</strong>g to the MacK<strong>in</strong>non (1991) critical value; hence, this test renders<br />
strong support for co<strong>in</strong>tegration. For comparison, we show <strong>in</strong> columns (1) and (2)<br />
regressions that either <strong>in</strong>clude the hous<strong>in</strong>g wealth variable or omit the visitor expenditures<br />
variable. <strong>The</strong> <strong>in</strong>significance of the hous<strong>in</strong>g wealth coefficient re<strong>in</strong>forces the earlier<br />
regression f<strong>in</strong>d<strong>in</strong>gs and v<strong>in</strong>dicates our belief that hous<strong>in</strong>g assets <strong>in</strong> S<strong>in</strong>gapore are perceived<br />
to be illiquid. Furthermore, the ADF t-statistics associated with these regressions <strong>in</strong>dicate<br />
that they do not represent co<strong>in</strong>tegrat<strong>in</strong>g relationships.<br />
In order to correct for possible endogeneity and measurement-error biases of the OLS<br />
estimates and their standard errors, we employed the ‘dynamic OLS’ (DOLS) procedure<br />
popularized byStockandWatson(1993)tore-estimate(10)(seeHayashi,2000,pp.654–657).<br />
We set the lead and lag lengths <strong>in</strong> the dynamic regression to four-quarters, correspond<strong>in</strong>g<br />
roughly to the T 1/3 rule, and ran an AR(1) regression on the residuals to compute the long-run<br />
variance. Table 2 shows that the DOLS coefficient estimates on f<strong>in</strong>ancial wealth and loans are<br />
larger than <strong>in</strong> the OLS case and reassur<strong>in</strong>gly, they come with significant corrected t-statistics.<br />
F<strong>in</strong>ally, we used the Johansen (1988) maximum likelihood procedure for further test<strong>in</strong>g<br />
and estimation. <strong>The</strong> trace test, based on a VAR(2) specification for the four variables,<br />
supports the existence of a s<strong>in</strong>gle co<strong>in</strong>tegrat<strong>in</strong>g vector. Further test<strong>in</strong>g shows that the