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Chris Ball's avatar

Not sure if I'm missing something, but I think your "primary surplus/gdp" graph is wrong. You say "Surplus is net lending or borrowing, AD02RC1Q027SBEA, primary surplus subtracts interest payments A091RC1Q027SBEA" But if I take the first series minus the second, I don't get that. I get this: https://fred.stlouisfed.org/graph/?g=1o5sF It is never positive.

Finally, I ADDED the two series and divided by real GDP (GDPC1) although most measures or real or potential real look the same... and I get a graph that looks like yours. Here's the link: https://fred.stlouisfed.org/series/AD02RC1Q027SBEA#0 .

If your graph has them added, then the comments/stories around it are off too.

I suspect my graph with them subtracted isn't right because we did indeed run primary surpluses in the late 1990s... but what data to look at?

Any thoughts?

I'm very interested in finding good data to play with and think about FTPL. So, I'd be interested in any suggestions on good time series for real primary surpluses or appropriate measures of debt, etc. I plan to look at these globally over the summer, but wanted to start with the best data I know of which is USA FRED data so I feel confident that I'm looking at what I want to look at then I can worry about non-US measures.

Thanks.

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D. J. Roach's avatar

Robert J. Barro's and Francesco Bianchi's Figure 1, "Change in Headline CPI Inflation Rate versus Composite Government-Spending Variable", shows Norway's ("NOR") data plotted (incorrectly) at (-0.053, -0.014). The correct data point for NOR is (-0.053, +0.014).

The plotting error skews the linear OLS regression line slope and intercept giving the impression of greater statistical agreement than the correctly plotted linear OLS regression line is capable of supporting.

The linear OLS regression model with NOR incorrectly plotted at (-0.056, -0.014) gives the following regression parameter values (m, b), and statistics (r^2, F-stat) for the 21 independent data points (dof = 19) in Table 1:

slope, m = 0.4999

intercept b = 0.00868

r^2 = 0.635

F-statistic = 33.1

The linear OLS regression model with NOR correctly plotted at (-0.056, +0.014) gives the following regression parameter values (m, b), and statistics (r^2, F-stat) for the 21 independent data points (dof = 19) in Table 1:

slope, m = 0.3662

intercept b =0.01344

r^2 = 0.4212

F-statistic = 13.8

When the data is correctly plotted, Barrio and Bianchi's regression model's explanatory power is lower by a significant amount. The correlation coefficient ( r ) is 0.649 for the correctly plotted data, versus 0.797 for the incorrectly plotted data. The difference in the F-statistic for the correctly plotted data and the incorrectly plotted data is notable, 13.8 for the correctly plotted data vs. 33.1 for the incorrectly plotted data.

Would this correction necessarily alter Barro's and Bianchi's conclusions? Perhaps.

One further point. Barro and Bianchi use reduced statistics for the Euro-zone data. This single data point replaces the seventeen Euro-zone countries that would otherwise have been plotted individually. This short-cut approach can only be justified if the Euro-zone country data is identical for each of the seventeen Euro-zone member countries. A quick scan of Table 1, Table 2, and Table 3, data suggests that the short-cut is unwarranted, a. s.

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