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Books on the topic 'Microsimulation model'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

F, Gentleman Jane, Robertson D, and Monica Tomiak, eds. Smoothing procedures for simulated longitudinal microdata. [Ottawa, Ont.]: Statistics Canada, 1990.

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2

Antcliff, Susan. An introduction to DYNAMOD: A dynamic population microsimulation model. Canberra City, ACT: National Centre for Social and Economic Modelling, Faculty of Management, University of Canberra, 1993.

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3

Holm, Einar. The SVERIGE spatial microsimulation model: Content, validation, and example applications. Umeå: Kulturgeografiska institutionen/SMC, Umeå universitet, 2002.

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4

Lifetime income distribution and redistribution: Applications of a microsimulation model. Amsterdam: North-Holland, 1993.

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5

Fredriksen, Dennis. Projections of population, education, labour supply, and public pension benfits: Analyses with the dynamic microsimulation MODEL MOSART. Oslo: Statistics Norway, 1998.

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6

Harding, Ann. Dynamic microsimulation models: Problems and prospects. London: Welfare State Programme, Suntory-International Centre for Economics and Related Disciplines, London School of Economics, 1990.

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7

Asghar, Zaidi M., Harding Ann 1958-, and Williamson Paul, eds. New frontiers in microsimulation modelling. Farnham, England: Ashgate, 2009.

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8

International Microsimulation Association. Inaugural meeting. New frontiers in microsimulation modelling. Farnham, England: Ashgate, 2009.

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9

Introduction to molecular-microsimulation of colloidal dispersions. Amsterdam: Elsevier, 2003.

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10

IARIW Special Conference on Microsimulation and Public Policy (1993 Canberra, A.C.T.). Microsimulation and public policy: Selected papers from the IARIW Special Conference on Microsimulation and Public Policy, Canberra, 5-9 December, 1993. Amsterdam: Elsevier, 1996.

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11

Ahmed, Vaqar. Using CGE and microsimulation models for income distribution analyses: A survey. Galway: Department of Economics, National University of Ireland, Galway, 2005.

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12

Microsimulation modelling of the corporate firm: Exploring micro-macro economic relations. Berlin: Springer, 1995.

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13

Davies, James B. Microsimulation, CGE and macro modelling for transition and developing economies. Helsinki: United Nations University, World Institute for Development Economics Research, 2004.

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14

National Research Council (U.S.). Panel to Evaluate Microsimulation Models for Social Welfare Programs. Improving information for social policy decisions: The uses of microsimulation modeling. Edited by Citro Constance F. 1942- and Hanushek Eric Alan 1943-. Washington, D.C: National Academy Press, 1991.

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15

Tax and benefit policies in the enlarged Europe: Assessing the impact with microsimulation models. Farnham: Ashgate, 2009.

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16

Narayan, Ambar. Knowing, when you don't know: Using microsimulation models to assess the poverty and distributional impacts of macroeconomic shocks. Washington, D.C: World Bank, 2011.

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17

Redmond, Gerry. The arithmetic of tax and social security reform: A user's guide to microsimulation methods and analysis. Cambridge: Cambridge University Press, 1998.

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18

O'Donoghue, Cathal. Practical Microsimulation Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198852872.001.0001.

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The purpose of this book is to bring together for the first time a description, with examples, of the main methods used in microsimulation modelling, used in the field of income-distribution analysis. The book provides a practical complement to the Handbook of Microsimulation Modelling, published in 2014. It is structured to develop and use the different types of models used in the field, with a focus on household-targeted policy. The book aims to fill a gap in the literature in providing a greater degree of codified knowledge through a practical guide to developing and using microsimulation models. At present, the training of researchers and analysts that use and develop microsimulation modelling is done on a relatively ad-hoc basis through occasional training programmes and lecture series, built around lecture notes. This book would enable a more formalized and organized approach. Each chapter addresses a separate modelling approach in a similar, consistent way, describing in practical terms the key methodological skills for each approach: · It provides some policy context to each modelling approach so as to understand the modelling choices made and structures developed. · As a very data-intensive modelling approach, each chapter describes key data analysis and data-preparation methods. · As a modelling approach that is used extensively for deciding policy, often involving huge budgets, validation is key. Each chapter describes an approach to validating the model. · Depending upon the policy context, the analysis is assessed in different ways. Each chapter contains a section devoted to measurement issues and tabulating output from the models. · Last, each chapter contains an example simulation of a policy analysis using the chapter’s methodological approach.

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19

John, Landt, ed. Reweighting a base population for a microsimulation model. Canberra: National Centre for Social and Economic Modelling, Faculty of Management, University of Canberra, 1995.

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20

Wright, Gemma, Michael Noble, Helen Barnes, David McLennan, and Michell Mpike. Updating NAMOD: A Namibian tax-benefit microsimulation model. UNU-WIDER, 2016. http://dx.doi.org/10.35188/unu-wider/2016/187-1.

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21

Wright, Gemma, Michael Noble, Helen Barnes, David McLennan, and Michell Mpike. SAMOD, a South African tax-benefit microsimulation model: Recent developments. UNU-WIDER, 2016. http://dx.doi.org/10.35188/unu-wider/2016/159-8.

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22

Salvini, Paul Antony. Design and development of the ilute operational prototype: A comprehensive microsimulation model of urban systems. 2003.

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23

Kavuma, Susan Namirembe, Christine Byaruhanga, Nicholas Musoke, Patrick Loke, Michael Noble, and Gemma Wright. An analysis of the distributional impact of excise duty in Uganda using a tax-benefit microsimulation model. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/827-6.

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24

Muttaqien, Arip, Denisa Sologon, and Cathal O’Donoghue. Tax-benefit microsimulation model in developing countries: A feasibility study for an extension of SOUTHMOD in Indonesia. UNU-WIDER, 2018. http://dx.doi.org/10.35188/unu-wider/2018/610-4.

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25

Wright, Gemma, Helen Barnes, Michael Noble, David McLennan, and Faith Masekesa. Assessing the quality of the income data used in SAMOD, a South African tax-benefit microsimulation model. UNU-WIDER, 2018. http://dx.doi.org/10.35188/unu-wider/2018/615-9.

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26

Osei, Robert Darko, Jukka Pirttilä, and Pia Rattenhuber. Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana. UNU-WIDER, 2017. http://dx.doi.org/10.35188/unu-wider/2017/419-3.

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27

Basu, Sanjay. Microsimulation. Edited by Sanjay Basu. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.003.0008.

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In previous chapters, the author used Markov models to estimate the burden of disease and the potential impact of interventions. One of the key limitations to Markov models is that they don’t take into account a person’s unique individual characteristics: Markov models are designed to efficiently simulate the average outcome for an entire population. For many public health and healthcare system problems, however, we need to consider heterogeneity within a population, or differences in risk and differences in benefit from our programs. For that purpose, microsimulation models, which take into account unique characteristics of individuals and the correlations between these characteristics, can be more useful. This chapter details the construction and use of microsimulation models, using examples related to diabetes prevention and treatment.

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28

Kisanga, Elineema, Vincent Leyaro, Wahabi Matengo, Michael Noble, Helen Barnes, and Gemma Wright. Assessing the distributional impact of lowering the value-added tax rate for standard-rated items in Tanzania and options for recouping revenue losses. 38th ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/976-1.

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This paper explores the distributional impact of lowering the value-added tax rate for standard-rated items in Tanzania Mainland. Using a static tax-benefit microsimulation model—TAZMOD—which is underpinned by data derived from the Household Budget Survey 2017/18, reductions in value-added taxes from 18 per cent to 17 per cent and 16 per cent are simulated. The revenue losses and impact on poverty are estimated. The rules for direct taxes are then modified in order to identify ways in which the revenue loss caused by the lowering of the standard rate of value-added taxes can be recouped.

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29

Handbook of Microsimulation Modelling. Emerald Publishing Limited, 2014.

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30

O'Donoghue, Cathal. Handbook of Microsimulation Modelling. Emerald Publishing Limited, 2014.

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31

Keegan, Marcia, and Gijs Dekkers. New Pathways in Microsimulation. Taylor & Francis Group, 2016.

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32

New Pathways in Microsimulation. Taylor & Francis Group, 2014.

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33

Ruth, Hancock, Sutherland H, and Suntory-Toyota International Centre for Economics and Related Disciplines., eds. Microsimulation models for public policy analysis: New frontiers. (London): (Suntory-Toyota International Centre for Economics and Related Disciplines), 1992.

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34

Basu, Sanjay. Modeling Public Health and Healthcare Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.001.0001.

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This book aims to empower readers to learn and apply engineering, operations research, and modeling techniques to improve public health programs and healthcare systems. Readers will engage in in-depth study of disease detection and control strategies from a “systems science” perspective, which involves the use of common engineering, operations research, and mathematical modeling techniques such as optimization, queuing theory, Markov and Kermack-McKendrick models, and microsimulation. Chapters focus on applying these techniques to classical public health dilemmas such as how to optimize screening programs, reduce waiting times for healthcare services, solve resource allocation problems, and compare macroscale disease control strategies that cannot be easily evaluated through standard public health methods such as randomized trials or cohort studies. The book is organized around solving real-world problems, typically derived from actual experiences by staff at nongovernmental organizations, departments of public health, and international health agencies. In addition to teaching the theory behind modeling methods, the book aims to confer practical skills to readers through practice in model implementation using the statistical software R.

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35

Lavinia, Mitton, Sutherland H, and Weeks Melvyn J, eds. Microsimulation modelling for policy analysis: Challenges and innovations. Cambridge, U.K: Cambridge University Press, 2000.

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36

Tim, Callan, and Economic and Social Research Institute., eds. Taxes, transfers, and labour market responses: What can microsimulation tell us? Dublin: Economic and Social Research Institute, 2000.

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37

(Editor), Lavinia Mitton, Holly Sutherland (Editor), and Melvyn Weeks (Editor), eds. Microsimulation Modelling for Policy Analysis: Challenges and Innovations (Department of Applied Economics Occasional Papers). Cambridge University Press, 2000.

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38

Neugart, Michael, and Matteo Richiardi. Agent-Based Models of the Labor Market. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.44.

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The chapter reviews the literature concerning agent-based labor market models by tracing its roots to the microsimulation literature and surveying a selection of con- tributions made since the work by Bergmann and Eliasson et al. Agent-based models have been applied to explain stylized facts of labor markets as well as labor market policy evaluations. They also constitute a major part of agent-based macroeconomic models. Besides reviewing the various results achieved, the chapter discusses modeling choices with respect to agents' behavior and the structure of interaction. The overall assessment is that agent-based labor market models have given us valuable insights into the functioning of labor markets and the consequences of labor market policies, and that they will increasingly become an essential tool of analysis, in particular, when the construction of large macro-models is involved.

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39

1949-, Creedy John, ed. Microsimulation modelling of taxation and the labour market: The Melbourne Institute tax and transfer simulator. Cheltenham, UK: Edward Elgar, 2002.

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40

(Editor), John Creedy, ed. Microsimulation Modelling of Taxation and the Labour Market: The Melbourne Institute Tax and Transfer Simulation. Edward Elgar Publishing, 2002.

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41

(Editor), Eric Alan Hanushek, and National Research Council (Corporate Author), eds. Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling : Review and Recommendations (Improving Information for Social Policy Decisions). National Academy Press, 1991.

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