Covid-19 is a large shock to the economic system and just a few months of data changed the mean of many time series in significant ways . But while Covid-19 has economic consequences, it is not an economic shock. This creates a problem for the \covid estimation of economic factors from the data. The persistence of its effects also suggests a need for unconventional predictors in factor-augmented regressions. This note uses covid indicators to adjust the post-covid\covid data prior to factor estimation. The adjustment preserves the pre-covid variations in the factors estimates. The real activity factor in FRED-MD is estimated to be down nearly four standard deviations in March/April 2020. Using economic and covid factors to generate h step ahead prediction errors, the JLN measure of uncertainty finds covid-19 to be the third most uncertain episode recorded since 1960.\covid\covid\covid\covid
The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-\covid\ forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use\covid\ indicators as controls to 'de-covid' the data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and\covid\ uncertainty has receded. Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling\covid\ and treating it as exogenous. These responses to economic shocks are distinctly different from those to a\covid\ shock, and distinguishing between the two types of shocks can be important in macroeconomic modeling post-\covid.
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Covid-19 is a large shock to the economic system
The coronavirus is a global event of historical proportions
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of data changed the mean of many time series in significant ways . But while Covid-19 has economic consequences, it is not an economic shock. This creates a problem for
changed
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time series properties of the data in ways that make many pre-
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\ forecasting models inadequate. It also creates a new problem for
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from the data. The persistence of its effects also suggests a need for unconventional predictors in factor-augmented regressions. This note uses covid indicators to adjust the post-covid
and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use
A
\ indicators as controls to 'de-covid' the
R
factor estimation. The adjustment preserves the pre-covid variations in the factors estimates. The real activity factor in FRED-MD is estimated to be down nearly four standard deviations in March/April 2020. Using economic and covid factors to generate h step ahead prediction errors, the JLN measure of uncertainty finds covid-19 to be the third most uncertain episode recorded since 1960.
estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and
A
\ uncertainty has receded. Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling
A
\ and treating it as exogenous. These responses to economic shocks are distinctly different from those to a
A
\ shock, and distinguishing between the two types of shocks can be important in macroeconomic modeling post-
A
.