Economic modeling in the post-Covid era: Part i

Executive Summary

The great disruption caused by COVID-19 has led to historic levels of economic volatility. As COVID-19 spread across the world, entire countries went into lockdown in an important attempt to curb the spread of the virus. Such lockdowns, however, brought economic activity to a standstill, as many businesses temporarily ceased operation or operated at a limited capacity. The halt and subsequent restart resulted in large swings in activity even within traditionally stable sectors of the economy. Further, the hole created by COVID-19 led to new outliers in many variables. These large deviations from historical trends will weigh on econometric models and alter analysts’ approach to forecasting. In a series of reports, we will discuss how this period may affect macroeconomic variables and econometric methods going forward as well as a framework for how to approach economic modeling in the post-COVID era.

In this first report, we discuss some potentially lasting effects of the recent swings in many macroeconomic variables. While the virus has injected new uncertainty in interpreting data, we advise continued caution as the economy slowly begins to dig itself out from the COVID-19 downturn. We identify issues that could arise as well as some data interpretation options to consider in the post-COVID era by drawing on some of our research after the Global Financial Crisis (GFC). Unprecedented economic volatility in the first half of 2020 may disrupt future seasonal adjustment processes and skew data. Further, some major variables may act differently in the postCOVID era, requiring adjustments in econometric models and a reconsideration of prior lead/lag relationships. It is also vital to not rely too heavily on one indicator or sector when assessing the health of the recovery.

Beware of Residual Seasonality

Widely followed macroeconomic data, such as Gross Domestic Product (GDP) and nonfarm payrolls are gathered by government agencies that provide a seasonal adjustment (SA) to the data.1 SA data can provide a clearer picture by removing fluctuations or seasonal patterns from the data that complicate time series analysis. SA also allows for a more accurate comparison of data over time. But, if SA data continue to exhibit a seasonal pattern within its data set, it could be due to residual seasonality

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