Imputation Based Estimators for Treatment Effects in Impact Evaluation Framework

Show simple item record

dc.contributor.author Dongmezo, Paul Brice Kenfac
dc.date.accessioned 2019-04-26T06:46:26Z
dc.date.available 2019-04-26T06:46:26Z
dc.date.issued 2019-04-26
dc.identifier.uri http://hdl.handle.net/123456789/4939
dc.description A Thesis submitted to Pan African University, Institute for Basic Sciences, Technology and Innovation in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mathematics (Statistics Option) 2018 en_US
dc.description.abstract The problem of counterfactual has been at the core of impact evaluation frame- work. Almost all existing methods aim to find the best way to estimate efficiently the counterfactual. A solution for estimation of counterfactual was proposed and investigated in this study. The objective of this research was to use classical imputation methods to estimate counterfactual, then derive treatment effect estimators from the data sets completed using the basic definition of treatment effect described in Rubin framework. The estimators obtained, called Imputation Based Treatment Effects estimators, and were theoretically unbiased, convergent and consistent. Using simulation, the results revealed that they were asymptotically unbiased and convergent as well. Results from the data application showed that they performed as well as the classic estimators and sometimes better in cases of shortage in data. To conclude the research, a hypothesis testing procedure was proposed to test the significance of the treatment effect. The results showed that the three approaches proposed were efficient, and could detect any change between two distributions, even slight changes. en_US
dc.description.sponsorship Prof. Peter N. Mwita, Department of Mathematics and Statistics, Machakos University, Kenya. Dr. Ignace Roger Kamga Tchwaket, Department of Economics, Sub regional Institute of Statistics and Applied Economics, Cameroon. en_US
dc.language.iso en en_US
dc.publisher PAUSTI, JKUAT en_US
dc.relation.ispartofseries Doctor of Philosophy in Mathematics;Statistics Option
dc.subject Applied mathematics en_US
dc.subject Estimation en_US
dc.title Imputation Based Estimators for Treatment Effects in Impact Evaluation Framework en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository


Browse

My Account