Micro-level analysis of unregistered employment in Turkey with group comparisons

Download

index.pdf

Date

2019

Author

İner, Mehmet

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

192
views

118
downloads

Group comparison of logistic regression models in a similar way with OLS is manipulating depending on the unobserved heterogeneity in logistic regression. In this sense, this study focuses on the group comparison problem in logistic regression. In order to get to the root of the comparison problem in logistic regression, the theoretical background of the logistic regression is explained with the latent propensity interpretation in which the extent of the dependent variable’s closeness to success is taken into consideration. In this respect, the discussions on the diagnosis and the remediation of the problem in the literature are revealed and analyzed. The application of group comparison in logistic regression is made by means of unregistered employment data in Turkey. In this respect, comparisons among genders, regions and years are made in terms of unregistered employment in this thesis. For this aim, Long’s (2009) and Long & Mustillo’s (2018) methods to conduct comparisons among groups by means of predicted probabilities and marginal effects are utilized since the test of difference in predicted probabilities based on the models and the marginal effects are not scaled by unobserved heterogeneity. Various important socio-economical results and implications including gender differences and regional differences are reached through the comparisons. Moreover, the differences in marginal effects in 10 years period are analyzed and the changes over time are associated with the measures taken in the field.

Subject Keywords

Logistic regression analysis., Keywords: Logistic Regression, Latent Propensity, Unobserved Heterogeneity, Unregistered Employment, Micro Level Analysis

URI

http://etd.lib.metu.edu.tr/upload/12624322/index.pdf
https://hdl.handle.net/11511/45464

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Cluster based model diagnostic for logistic regression Tanju, Özge; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2016) Model selection methods are commonly used to identify the best approximation that explains the data. Existing methods are generally based on the information theory, such as Akaike Information Criterion (AIC), corrected Akaike Information Criterion (AICc), Consistent Akaike Information Criterion (CAIC), and Bayesian Information Criterion (BIC). These criteria do not depend on any modeling purposes. In this thesis, we propose a new method for logistic regression model selection where the modeling purpose is c...
Score test for testing etiologic heterogeneity in two-stage polytomous logistic regression Karagülle, Saygın; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2013) Two-stage polytomous logistic regression was proposed by Chatterjee (2004) as an effective tool to analyze epidemiological data when disease subtype information is available. In this modeling approach, a classic logistic regression is employed in the first level of the model. In the second level, the first-stage regression parameters are modeled as a function of some contrast parameters in a somehow similar spirit of an ANOVA model. This modeling also enables a practical way of estimating the heterogeneity ...
Consensus clustering of time series data Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014) In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Temporal neuro-fuzzy MAR Algorithm for time series data in rule-based systems Sisman, NA; Alpaslan, Ferda Nur (1998-04-23) This paper introduces a new neuro-fuzzy model for constructing a knowledge-base of temporal fuzzy rules obtained by MAR (Multivariate Autoregressive) Algorithm. The model described contains two main parts which are fuzzy-rule extraction and storage of them. The fuzzy rules are obtained from time series data using MAR Algorithm. Fuzzy linear function with fuzzy number coefficients are used. The extracted rules are fed into the temporal fuzzy multilayer feedforward neural network.
Dynamical Gene-Environment Networks Under Ellipsoidal Uncertainty: Set-Theoretic Regression Analysis Based on Ellipsoidal OR Kropat, Erik; Weber, Gerhard Wilhelm; Belen, Selma (2008-09-12) We consider dynamical gene-environment networks under ellipsoidal uncertainty and discuss the corresponding set-theoretic regression models. Clustering techniques are applied for an identification of functionally related groups of genes and environmental factors. Clusters can partially overlap as single genes possibly regulate multiple groups of data items. The uncertain states of cluster elements are represented in terms of ellipsoids referring to stochastic dependencies between the multivariate data varia...

Citation Formats

M. İner, “Micro-level analysis of unregistered employment in Turkey with group comparisons,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Statistics., Middle East Technical University, 2019.