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Article Contents

# On the impact of the Covid-19 health crisis on GDP forecasting: An empirical approach

• * Corresponding author: Andrés Sosa
• Statistical dependence between the GDP growth projection adjustments for the end of 2020 and the health impact of the Covid-19 pandemic is detected and quantified for a broad set of countries. A $\texttt{rate }$ that captures this health impact for each country is contrasted to the difference in GDP growth projections for the end of 2020 released in two subsequent times: 2019 (pre-pandemic) and early 2020 (post-pandemic). The difference of this two variables exhibited a significant rank correlation with the $\texttt{rate }$, and a linear model was successfully fitted, concluding that at the beginning of the pandemic health conditions played a significant role in the GDP projections.

Mathematics Subject Classification: Primary: 62P20: ; Secondary: 91B55.

 Citation:

• Figure 1.  Smoothed deaths per one million population (03/01/2020 - 12/31/2020)

Figure 2.  Boxplots of the variables. Left: $\texttt{rate}$. Right: GDP growth projections for 2020

Figure 3.  Relation of the duration (in years) of the pandemic, deaths per million people due to Covid-19, and the levels of the variable rate for 30 reference countries. Data for April 2020 is shown in red, whereas data for June 2020 is shown in blue

Figure 4.  Scatter plot of the IMF GDP growth projections for 2020 generated in April 2020, and the $\texttt{rate}$. Left: countries without deaths due to the pandemic. Right: countries with positive $\texttt{rate}$. Countries are represented by their alpha-3 ISO 3166 code

Figure 5.  Scatter plot of the IMF GDP growth projections for 2020 generated in April and June 2020, and the $\texttt{rate}$. Only the 30 reference countries selected by the IMF are considered. Left: Data set for April 2020. Right: Data set for June 2020

Figure 6.  Scatter plot of the WB GDP growth projections for 2020 generated in June 2020, and the $\texttt{rate}$

Table 1.  Spearman rank correlation $r_s$

 Growth projection difference Variable $r_s$ $p$-value $\texttt{Apr2020_IMF} - \texttt{Apr2019_IMF}$ $\texttt{Rate April}$ -0.423 2.4e-09 $\texttt{Jun2020_IMF} - \texttt{Apr2019_IMF}$ $\texttt{Rate June}$ -0.554 8.8e-04 $\texttt{Jun2020_WB} - \texttt{Dic2019_WB}$ $\texttt{Rate June}$ -0.37 2.0e-05

Table 2.  IMF data set for April 2020, countries without deaths due to Covid-19. Residual standard error: $4.102$; Adjusted R-squared: $0.1954$. Significance codes: 0 $‘ \ast\ast\ast ’$; 0.001 $‘\ast\ast’$; 0.01 $‘\ast’$; 0.05 $‘ \ \cdot\ ’$; 0.1 $‘ \quad ’$

 Par. Estimate Std. Error $t$-value ${\bf{Pr}}(>|t|)$ Signif. codes $\texttt{Intercept}$ $\hat{b}$ -5.77 1.62 -3.60 0.00140 $\ast\ast$ $\texttt{Apr2019_IMF}$ $\hat{a}_1$ 0.83 0.30 2.79 0.00948 $\ast\ast$

Table 3.  IMF data set for April 2020, countries with deaths due to Covid-19. Residual standard error: 2.079; Adjusted R-squared: 0.6417

 Par. Estimate Std. Error $t$-value ${\bf{Pr}}(>|t|)$ Signif. codes $\texttt{Intercept}$ $\hat{b}$ -5.43 0.63 -8.65 1.34e-14 $\ast\ast\ast$ $\texttt{Apr2019_IMF}$ $\hat{a}_1$ 1.12 0.20 10.22 2.0e-16 $\ast\ast\ast$ $\texttt{Rate April}$ $\hat{a}_2$ -0.65 0.10 -6.21 6.16e-09 $\ast\ast\ast$

Table 4.  IMF data set for April 2020, only 30 reference countries. Residual standard error: 1.568; Adjusted R-squared: 0.7522

 Par. Estimate Std. Error $t$-value ${\bf{Pr}}(>|t|)$ Signif. codes $\texttt{Intercept}$ $\hat{b}$ -6.39 0.99 -6.47 6.16e-07 $\ast\ast\ast$ $\texttt{Apr2019_IMF}$ $\hat{a}_1$ 1.25 0.18 6.86 2.28e-07 $\ast\ast\ast$ $\texttt{Rate April}$ $\hat{a}_2$ -0.37 0.16 -2.35 0.0262 $\ast$

Table 5.  IMF data set for June 2020, only 30 reference countries. Residual standard error: 2.625; Adjusted R-squared: 0.5478

 Par. Estimate Std. Error $t$-value ${\bf{Pr}}(>|t|)$ Signif. codes $\texttt{Intercept}$ $\hat{b}$ -3.91 2.15 -1.82 0.08016 $\texttt{Apr2019_IMF}$ $\hat{a}_1$ 0.82 0.32 2.52 0.01813 $\ast$ $\texttt{Rate June}$ $\hat{a}_2$ -0.99 0.31 -3.15 0.00402 $\ast\ast$

Table 6.  WB data set for June 2020, all countries. Residual standard error: 2.674; Adjusted R-squared: 0.4546

 Par. Estimate Std. Error $t$-value ${\bf{Pr}}(>|t|)$ Signif. codes $\texttt{Intercept}$ $\hat{b}$ -4.92 0.98 -5.01 2.84e-06 $\ast\ast\ast$ $\texttt{Dic2019_WB}$ $\hat{a}_1$ 1.02 0.15 6.79 1.39e-09 $\ast\ast\ast$ $\texttt{Rate June}$ $\hat{a}_2$ -0.56 0.18 -3.03 0.00322 $\ast\ast$
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