Kovačević, Sanja (2014) Metoda uparivanja po vjerojatnosti sklonosti. Diploma thesis, Faculty of Science > Department of Mathematics.

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Abstract
In this paper focus was on estimating average treatment effects. We used different assumptions and different methods regression methods, methods based on the propensity score and instrumental variables methods. Variable $y_1$ denoted the outcome with treatment and $y_0$ without the treatment, while $\textbf{x}$ denoted a vector of observed covariates and w denoted receiving the treatment. To estimate the difference $y_1y_0$, we observed average treatment effect (ATE) defined as $AT E \equiv E( y_1y_0 )$ and average treatment effect on the treated ($AT E_1$ ) defined as $AT E_1 \equiv E( y_1y_0  w = 1)$. Key assumption was assumption called ignorability of treatment (given observed covariates $\textbf{x}$): ATE.1: Conditional on $\textbf{x}$, $w$ and ( $ y_0, y_1$) are independent. ATE.1’: (a) $E(y_0  \textbf{x}, w) = E(y_0  \textbf{x}$); (b) $E(y_1  \textbf{x}, w) = E(y_1  \textbf{x})$. Propensity score $p(\textbf{x})$ is the probability of receiving the treatment given the covariates. For using methods based on the propensity score we needed strong ignorability of a treatment assumption. We described matching algorithm based on the propensity score. For estimating the local average treatment effect (LATE), we used the instrumental variables method in the simplest scenario when an instrumental variable $z$ is binary. Finally, we showed how to use the propensity score matching in practice. We estimated the impact of IWT subsidies on firms’ R$\And$D intensity and employment in the Flemish region.
Item Type:  Thesis (Diploma thesis) 

Supervisor:  Radas, Sonja 
Date:  2014 
Number of Pages:  53 
Subjects:  NATURAL SCIENCES > Mathematics 
Divisions:  Faculty of Science > Department of Mathematics 
Depositing User:  Iva Prah 
Date Deposited:  03 Jun 2015 11:57 
Last Modified:  03 Jun 2015 11:57 
URI:  http://digre.pmf.unizg.hr/id/eprint/3986 
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