National Institute Economic Review

World overview: Forecast summary

  • Increases in tariffs and uncertainty about both future tariff impositions and their potential implications for production activity have continued to have negative effects on global trade and industrial production.
  • Several central banks, facing below target inflation, have loosened monetary policy to mitigate the effects of slower economic growth and a deterioration in the prospects for trade. While we expect that further monetary loosening will occur, fiscal policy could be more effective in boosting demand.

Prospects for the UK economy: Forecast Summary

  • The economic outlook is clouded by significant economic and political uncertainty and depends critically on the United Kingdom’s trading relationships after Brexit. Domestic economic weakness is further amplified by slowing global demand.
  • We would not expect economic activity to be boosted by the approval of the government’s proposed Brexit deal. We estimate that, in the long run, the economy would be 3½ per cent smaller with the deal compared to continued EU membership.

The Dasgupta review: Supplementary notes on investment in conservation and restoration, family planning, and reproductive health

Three broad categories of transformative changes have been recommended in The Economics of Biodiversity: The Dasgupta Review (henceforth Review): (i) The need to address the imbalance between our demands on Nature and its supply. (ii) The need to change our measures of economic success. (iii) The need for institutional change. However, what the private citizen would like to find in the Review differs from what someone in a government department or an international agency or a private company seeks.

Nowcasting GDP growth in a small open economy

Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models.

Tracking the mutant: Forecasting and nowcasting COVID-19 in the UK in 2021

A new class of time series models is used to track the progress of the COVID-19 epidemic in the UK in early 2021. Models are fitted to England and the regions, as well as to the UK as a whole. The growth rate of the daily number of cases and the instantaneous reproduction number are computed regularly and compared with those produced by SAGE. The results from figures published each day are compared with results based on figures by specimen date, which may be more accurate but are subject to substantial revisions.

Can machine learning catch the COVID-19 recession?

Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components).

Nowcasting ‘true’ monthly U.S. GDP during the pandemic

Expenditure-side and income-side gross domestic product (GDP) are measured at the quarterly frequency and contain measurement error. Econometric methods exist for producing reconciled estimates of underlying true GDP from these noisy estimates. Recently, the authors of this paper developed a mixed-frequency reconciliation model which produces monthly estimates of true GDP. In the present paper, we investigate whether this model continues to work well in the face of the extreme observations that occurred during the pandemic year and consider several extensions of it.

The value of robust statistical forecasts in the COVID-19 pandemic

The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes.

Introduction: The COVID-19 pandemic and macroeconomic forecasting

The Spring 2021 issue of the National Institute Economic Review examines the challenges of macroeconomic forecasting during the Covid-19 pandemic and publishes four solutions proposed by leading academic researchers in macroeconomic forecasting. These contributions show how innovative methodologies can be applied to deal with the forecasting challenges thrown open by Covid-19. By organising this special issue, the National Institute Economic Review’s editors hope to contribute to the debate on how to develop forecasting methods for economies subject to large shocks and instability.

Commentary: Proposals for a new fiscal framework

Modern representative democracy is faced with the Whiggish directive to improve living standards with the main lever in possession of any government being its fiscal policy. That is its choice on how much to spend, tax and borrow and on what. The electorate is supposed to assess the alternate options offered by the political marketplace, and the set of offered policies that most closely match those of the electorate, or more precisely its median voter, will be the basis for the government’s fiscal strategy (Persson and Tabellini, 1994).