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The Neoclassical Growth Model Predicts That for Identical Savings Rates

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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 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   =   +   × 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,β1 = 0;H1 : β1 < 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     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? = 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   =   +   × 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,β1 = 0;H1 : β1 < 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     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? + 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   =   +   × 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,β1 = 0;H1 : β1 < 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     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? × 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,β1 = 0;H1 : β1 < 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 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   =   +   × 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,β1 = 0;H1 : β1 < 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     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? 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   =   +   × 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,β1 = 0;H1 : β1 < 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     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? 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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