Quantitative methods for policy analysis course notes

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dc.creatorHowitt, Richard E.
dc.creatorMsangi, Siwa
dc.creatorMacEwan, Duncan
dc.date2014
dc.date2024-08-01T02:50:35Z
dc.date2024-08-01T02:50:35Z
dc.date.accessioned2026-06-27T15:30:40Z
dc.descriptionHistorically economists have relied on econometric (or statistical) methods to estimate parameters from observed data. In this approach we observe a rich cross-section or time-series dataset, specify an economic model which implicitly defines the underlying behavior (say, simple linear regression), and estimate key parameters of interest (such as supply and demand elasticities). Econometric analysis typically requires a large dataset and we will often specify a reduced-form model. What do we do when data are limited? What do we do when we want to predict response to policies that simultaneously affect multiple resources, production activities, prices, and markets? Computational methods such as linear programming, calibrated optimization, and dynamic programming allow us to calibrate parameters using limited data and specify a framework that is consistent with economic theory that we can then use to simulate the interaction of complex resource policies.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/150063
dc.identifier.urihttp://hdl.handle.net/123456789/106513
dc.languageen
dc.publisherInternational Food Policy Research Institute
dc.rightsOpen Access
dc.sourceHowitt, Richard E.; Msangi, Siwa and MacEwan, Duncan. 2015. Quantitative methods for policy analysis course notes. Washington, DC: International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/150063
dc.subjectmathematical models
dc.subjectpolicies
dc.subjecteconomics
dc.subjecteconometrics
dc.subjectstatistical methods
dc.titleQuantitative methods for policy analysis course notes
dc.typeTraining Material

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)