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Nowcasting in Microsimulation Models: A Methodological Survey

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In this paper, we survey the use of nowcasting methods in Microsimulation models. These nowcasting methods differ in a number of respects to the more established methods of forecasting. The main distinction is that while forecasting extrapolates from current data to estimate the future, the methods of nowcasting extrapolate from data of the recent past to reflect the present situation. In this paper, we undertake a survey of a number of modelling teams globally, selected for their experience and breadth of use with the methodologies of nowcasting and to ascertain the modelling choices made. Different methodologies are used to adjust the different components, with indexation or price uprating applied for the adjustments to growth in wages or prices, the updating of tax-benefit policy to adjust for policy change and either static or dynamic ageing to account for changes to the population and labour market structure. Our survey reports some of the choices made. We find that these model teams are increasingly utilising variants of these methods for short-term projections, which is relatively novel relative to the published literature.

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  • Cathal O'Donoghue & Jason Loughrey, 2014. "Nowcasting in Microsimulation Models: A Methodological Survey," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(4), pages 1-12.
    • Handle: RePEc:jas:jasssj:2013-171-2
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    1. Fran??ois Bourguignon & Francisco H. G. Ferreira & Phillippe G. Leite, 2002. "Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions Across Countries," William Davidson Institute Working Papers Series 478, William Davidson Institute at the University of Michigan.
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    3. Sutherland, Holly, 2001. "EUROMOD: an integrated European benefit-tax model: final report," EUROMOD Working Papers EM9/01, EUROMOD at the Institute for Social and Economic Research.
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    5. Arthur van Soest, 1995. "Structural Models of Family Labor Supply: A Discrete Choice Approach," Journal of Human Resources, University of Wisconsin Press, vol. 30(1), pages 63-88.
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    1. Herwig Immervoll & Cathal O’Donoghue & Jules Linden & Denisa Sologon, 2023. "Who pays for higher carbon prices?: Illustration for Lithuania and a research agenda," OECD Social, Employment and Migration Working Papers 283, OECD Publishing.
    2. Michal Myck & Mateusz Najsztub, 2015. "Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 33-66.
    3. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    4. Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020. "Modelling the Distributional Impact of the COVID‐19 Crisis," Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
    5. O'Donoghue, Cathal & Sologon, Denisa Maria, 2023. "The Transformation of Public Policy Analysis in Times of Crisis – A Microsimulation-Nowcasting Method Using Big Data," IZA Discussion Papers 15937, Institute of Labor Economics (IZA).
    6. O'Donoghue, Cathal & M. Sologon, Denisa & Kyzyma, Iryna & McHale, John, 2020. "Modelling the distributional impact of the Covid-19 crisis in Ireland," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA4/20, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    7. Denisa M. Sologon & Cathal O’Donoghue & Iryna Kyzyma & Jinjing Li & Jules Linden & Raymond Wagener, 2022. "The COVID-19 resilience of a continental welfare regime - nowcasting the distributional impact of the crisis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 777-809, December.
    8. Gary Cornwall & Marina Gindelsky, 2024. "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," BEA Papers 0130, Bureau of Economic Analysis.

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