An important and helpful thread by @EdConwaySky. If I may I will pick up his theme on counterfactuals and just state that is the standard way to think about policy interventions or the impact of news in economics. As I think @ChrisGiles_ has also recently argued. 1/6 https://twitter.com/EdConwaySky/status/1324819551335403520

By examining the impact of a number of possible interventions given the benchmark case, the policy maker (the person deciding what to do) can choose the best alternative or as I prefer the most robust alternative that minimises their welfare losses. 2/6

Exhibit 1: Late last month by published a WP by Andrew Harvey @CamEcon and @NIESRorg examining the counterfactual had the UK entered an earlier lockdown in the Spring. By examining responses elsewhere he thinks so and we agree. More work is to come. 3/6 https://www.niesr.ac.uk/publications/time-series-models-epidemics-leading-indicators-control-groups-and-policy-assessment

Exhibit 2: Last week we published an analysis of national and regional patterns in Destitution by Arnab Bhattacharjee @HeriotWattUni and @ELisauskaite @NIESRorg as a counterfactual with and without the Covid-shock. It is a call for a policy response. 4/6 https://www.niesr.ac.uk/publications/regional-distribution-destitution-covid-19-crisis

Exhibit 3: Following the announcement of a 2nd lockdown, @NIESRorg projected the new path of GDP. This helps us understand GDP compared to case where there had been no need to call a further lockdown. H/t to @RNMacqueen, @clenoel and @hande_kucuk1 5/6 https://www.niesr.ac.uk/publications/policy-note-update-our-review-forecast-november-lockdown

Further Reading: Why Forecast?

@NIESRorg Commentary from February 2017.

6/6. https://www.niesr.ac.uk/publications/commentary-why-forecast