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The Neoclassical Growth Model Predicts That for Identical Savings Rates g6090^\widehat { g 6090 }

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The neoclassical growth model predicts that for identical savings rates and population growth rates, countries should converge to the per capita income level. This is referred to as the convergence hypothesis. One way to test for the presence of convergence is to compare the growth rates over time to the initial starting level, i.e., to run the regression g6090^\widehat { g 6090 } = β0^\widehat { \beta 0 } + β1^\widehat { \beta 1 } × RelProd60 , where g6090 is the average annual growth rate of GDP per worker for the 1960-1990 sample period, and RelProd60 is GDP per worker relative to the United States in 1960. Under the null hypothesis of no convergence, ?1 = 0; H1 : ?1 < 0, implying ("beta")convergence. Using a standard regression package, you get the following output:
Dependent Variable: G6090
Method: Least Squares
Date: 07/11/06 Time: 05:46
Sample: 1 104
Included observations: 104
White Heteroskedasticity-Consistent Standard Errors & Covariance  Variable  Coefficient  Std. Error  t-Statistic  Prob.  C 0.0189890.0023927.9398640.0000 YL60 0.0005660.0050560.1119480.9111\begin{array} { c c c c l } \text { Variable } & \text { Coefficient } & \text { Std. Error } & \text { t-Statistic } & \text { Prob. } \\\hline \text { C } & 0.018989 & 0.002392 & 7.939864 & 0.0000 \\\text { YL60 } & - 0.000566 & 0.005056 & - 0.111948 & 0.9111 \\\hline\end{array}  R-squared 0.000068 Mean dependent var 0.018846 Adjusted R-squared 0.009735 S.D. dependent var 0.015915 S.E. of regression 0.015992 Akaike info criterion 5.414418 Sum squared resid 0.026086 Schwarz criterion 5.363565 Log likelihood 283.5498 F-statistic 0.006986 Durbin-Watson stat 1.367534 Prob(F-statistic) 0.933550\begin{array} { l l l l } \text { R-squared } & 0.000068 & \text { Mean dependent var } & 0.018846 \\\text { Adjusted R-squared } & - 0.009735 & \text { S.D. dependent var } & 0.015915 \\\text { S.E. of regression } & 0.015992 & \text { Akaike info criterion } & - 5.414418 \\\text { Sum squared resid } & 0.026086 & \text { Schwarz criterion } & - 5.363565 \\\text { Log likelihood } & 283.5498 & \text { F-statistic } & 0.006986 \\\text { Durbin-Watson stat } & 1.367534 & \text { Prob(F-statistic) } & 0.933550\end{array}
You are delighted to see that this program has already calculated p-values for you. However, a peer of yours points out that the correct p-value should be 0.4562. Who is right?


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