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Using NiGEM in uncertain times: Introduction and overview of NiGEM

Published online by Cambridge University Press:  01 January 2020

Abstract

This paper introduces a special issue of the Review on how the National Institute Global Econometric Model (NiGEM) is being used to navigate uncertain times. NiGEM is the leading global macroeconomic model, used by both policy-makers and the private sector across the globe for economic forecasting, scenario building and stress testing. The paper summarises the main features of NiGEM and describes some standard model simulations to illustrate how the model responds to monetary, fiscal and technology shocks.

Type
Research Articles
Copyright
Copyright © 2018 National Institute of Economic and Social Research

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References

Barrell, R., Becker, B., Byrne, J., Gottschalk, S., Hurst, I. and van Welsum, D. (2004), ‘Macroeconomic policy in Europe: experiments with monetary responses and fiscal impulses’, Economic Modelling, 21, pp. 877931.CrossRefGoogle Scholar
Barrell, R. and Davis, E.P. (2007), ‘Financial liberalisation, consumption and wealth effects in 7 OECD countries’, NIESR Discussion paper no. 247.Google Scholar
Barrell, R., Dury, K. and Hurst, I. (2003), ‘International monetary policy coordination: an evaluation using a large econometric model’, Economic Modelling, 20(3), pp. 507–27.CrossRefGoogle Scholar
Barrell, R. and Pain, N. (1997), ‘Foreign direct investment, technological change, and economic growth within Europe’, Economic Journal, 107, pp. 1770–6.CrossRefGoogle Scholar
Blanchard, O. (2018), ‘On the future of macroeconomic models’, Oxford Review of Economic Policy, 34 (1–2), pp. 4354.CrossRefGoogle Scholar
Byrne, J.P. and Davis, E.P. (2003), ‘Disaggregate wealth and aggregate consumption: an investigation of empirical relationships for the G7’, Oxford Bulletin of Economics and Statistics, 65(2), pp.197–220.CrossRefGoogle Scholar
Calvo, G.A. (1983), ‘Staggered prices in a utility-maximizing framework’, Journal of Monetary Economics, 12(3), pp. 383–98.CrossRefGoogle Scholar
Carreras, O., Davis, E.P., Hurst, I., Liadze, I., Piggott, R. and Warren, J. (2018), ‘Implementing macroprudential policy in NiGEM’, NIESR Discussion paper No. 490.Google Scholar
Christiano, L.J., Eichenbaum, M. and Evans, C.L. (2005), ‘Nominal rigidities and the dynamic effects of a shock to monetary policy’, Journal of Political Economy, 113(1), pp. 145.CrossRefGoogle Scholar
Cloyne, J. and Hürtgen, P. (2016), ‘The macroeconomic effects of monetary policy: a new measure for the United Kingdom’, American Economic Journal: Macroeconomics, 8, 4, October, pp. 75102.Google Scholar
Corsetti, G., Duarte, J.B. and Mann, S. (2018), ‘One money, many markets: a factor model approach to monetary policy in the Euro Area with high-frequency identification’, CFM Discussion Paper Series, CFM-DP2018–05, London: Centre For Macroeconomics.Google Scholar
Ebell, M., Hurst, I. and Warren, J. (2016), ‘Modelling the long-run economic impact of leaving the European Union’, Economic Modelling, 59, pp. 196209.CrossRefGoogle Scholar
Fair, R.C. and Taylor, J.B. (1983), ‘Solution and maximum likelihood estimation of dynamic nonlinear rational expectations models’, Econometrica, pp. 1169–85.CrossRefGoogle Scholar
Fair, R.C. and Taylor, J.B. (1990), ‘Full information estimation and stochastic simulation of models with rational expectations’, Journal of Applied Econometrics, 5(4), pp. 381–92.CrossRefGoogle Scholar
Francis, N. and Ramey, V.A. (2005), ‘Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations’, Journal of Monetary Economics, 52, pp. 1379–99.CrossRefGoogle Scholar
Gall, J. (1999), ‘Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations?American Economic Review, 89, pp. 249271.CrossRefGoogle Scholar
Giavazzi, F., Amighini, A. and Blanchard, O. J. B. (2010), Macroeconomics: A European Perspective, Financial Times Prentice Hall.Google Scholar
Haincourt, S. (2018), ‘The nature of the shock matters: NiGEM estimations of the macroeconomic effects of recent dollar and euro fluctuations’, National Institute Economic Review, 244, May.CrossRefGoogle Scholar
Jorra, M., Esser, A. and Slopek, U.D. (2018), ‘The import content of expenditure components and the size of international spillovers’, National Institute Economic Review, 244, May.CrossRefGoogle Scholar
Slopek, U.D. (2018), Export pricing and the macroeconomic effects of US import tariffs’, National Institute Economic Review, 244, May.CrossRefGoogle Scholar
Wallis, K. (2000) ‘Macroeconometric modelling’, in Gudmundson, M., Herbertsson, T. and Zoega, G. (eds), Macroeconomic Policy: Iceland in an Era of Global Integration, pp. 399–41.Google Scholar