arXiv 3 Mar 2021 · Econometrics · 23 citations (OpenAlex)
arXiv:2103.02732 · PDF · DOI · OpenAlex · Extracted main text
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 which are much larger but shorter lived. Disentangling the two types of shocks can be important in macroeconomic modeling post-covid.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lenza and Primiceri (2020) How to Estimate a VAR after March 2020, mimeo, Northwestern University | 0.644 | 2 | 2 | 100% |
| 2 | Primiceri and Tambalotti (2020) Macroeconomic Forecasting the Time of COVID-19, mimeo, Northwestern University | 0.644 | 2 | 2 | 100% |
| 3 | Stock and Watson (2002) Macroeconomic Forecasting Using Diffusion Indexes, Journal of Business and Economic Statistics 20:2, 147–162 | 0.511 | 2 | 1 | 100% |
| 4 | Bai and Ng (2002) Determining the Number of Factors in Approximate Factor Models, Econometrica 70(1), 191–221 | 0.405 | 1 | 1 | 100% |
| 5 | Bai and Ng (2006) Confidence Intervals for Diffusion Index Forecasts and Inference with Factor-Augmented Regressions, Econometrica 74:4, 1133–1150 | 0.405 | 1 | 1 | 100% |
| 6 | Carriero, Clark, Marcellino and Mertens (2021) Addressing COVID-19 Outliers in BVARs with Stochastic Volatility, mimeo | 0.405 | 1 | 1 | 100% |
| 7 | Chudik, Mohaddes, Pesaran, Raissi and Rebucci (2020) A Counterfactual Economic Analysis of Covid-19 Using A Threshold Augmented Multi-Country Model, unpublished Manuscript | 0.405 | 1 | 1 | 100% |
| 8 | Davis and Ng (2021) Time Series Estimation of the Dynamic Effects of Disaster-Type Shocks, arXiv: 2107.06663 | 0.405 | 1 | 1 | 100% |
| 9 | Doshi (2008) Trends in recorded influenza mortality: United States, 1900-2004., American journal of public health 98 5, 939–45 | 0.405 | 1 | 1 | 100% |
| 10 | Foroni, Marcellino and Stevanovic (2020) Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis, unpublished Manuscript | 0.405 | 1 | 1 | 100% |
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