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Hands-on Data Learning Session On Regression Analysis With Qualitative Variables- Categorical Dependent Variable Regression

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Hands-on Data Learning Session on Regression Analysis with Qualitative variables- Categorical Dependent Variable Regression

Generation Alpha Data Center (GenAlphaDC), IMPRI Impact and Policy Research Institute, organized Data Analytics for Policy Research Cohort 2.0, a One-Month Immersive Online Hands-On Certificate Training Course, to equip policymakers, researchers, and data enthusiasts with cutting-edge analytical skills. On 25 November, 2023, Dr Soumyadip ChattopadhyayAssociate Professor, Economics, Visva-Bharati, Santiniketan; Visiting Senior Fellow, IMPRI, conducted a Hands-on Data Learning Session on Regression Analysis with Qualitative variables- Categorical Dependent Variable Regression.

Dr. Soumyadip Chattopadhyay, in an enlightening presentation, delved into the intricacies of Regression Analysis, placing a specific focus on the challenges and methodologies associated with Qualitative Variables, particularly emphasizing Categorical Dependent Variable Regression. The session aimed not only to disseminate theoretical knowledge but also to provide practical insights into the application of these regression techniques.

The presentation commenced with a thorough exploration of binary choice models, crucial tools when dealing with categorical dependent variables. Dr. Chattopadhyay set the stage by underscoring the inadequacy of linear probability models, steering the audience towards more nuanced and suitable alternatives.

The core of the discussion revolved around the logit and probit models, both catering to binary outcomes but with distinctive characteristics. Dr. Chattopadhyay provided a detailed examination of each model, elucidating their theoretical underpinnings and practical applications.

Logit Model Analysis

A focal point of the presentation was the logit model, wherein Dr. Chattopadhyay dissected its theoretical foundations. He highlighted the indirect relationship between changes in independent variables (X) and their impact on the dependent variable (Y) through an intermediate variable (Z). The interpretation of marginal effects in logit models was discussed in-depth, emphasizing the deviations from interpretations in multiple linear regression models.

Probit Model

A succinct comparison between logit and probit models was presented, considering factors that influence the choice between the two. The assumption of a normal distribution for the error term in probit models was briefly touched upon, providing a comparative understanding. Practical considerations, including personal preferences and sample characteristics, were underscored as determinants for choosing between logit and probit models.

Goodness of Fit Measures

A critical part of the discussion involved an exploration of the limitations of conventional goodness-of-fit measures like R-squared in the context of binary choice models. Dr. Chattopadhyay introduced the audience to alternative measures such as McFadden’s R-squared and likelihood ratio test statistics, offering more nuanced assessments of model fit.

Empirical Application

The theoretical knowledge seamlessly transitioned into an empirical application, wherein a dataset of 266 rural nonfarm workers from West Bengal became the canvas for investigating the determinants of employment mode (self-employed or casually employed). Dr. Chattopadhyay practically demonstrated the step-by-step implementation of a logit regression model, utilizing the EViews software.

Interpretation of Results

The interpretation of coefficients for variables such as age, sex, education, and assets was dissected, providing a profound understanding of their impact on the probability of self-employment. Significance levels of variables were meticulously assessed through Z statistics, contributing to a comprehensive interpretation of the model’s findings. Overall model significance was established through likelihood ratio test statistics, ensuring a robust understanding of the employed regression model.

Marginal Effect Calculation

Dr. Chattopadhyay provided a detailed demonstration of the manual calculation of marginal effects, shedding light on the limitations of EViews in this regard. The audience gained insight into the importance of marginal effects in discerning the relative significance of independent variables, offering a nuanced perspective beyond coefficient values.

In conclusion, Dr. Soumyadip Chattopadhyay’s presentation stood as a beacon of comprehensive insight into Regression Analysis with Qualitative Variables, with a dedicated focus on Categorical Dependent Variable Regression. The audience not only gleaned a deeper theoretical understanding of logit and probit models but also witnessed their practical applications through empirical analysis.

The empirical application served as a bridge between theory and real-world scenarios, showcasing the relevance and importance of these regression techniques in empirical research. Despite some limitations with EViews in calculating marginal effects, the session equipped the audience with valuable knowledge and practical tools for conducting meaningful regression analyses with qualitative variables and categorical dependent variables.

Dr. Chattopadhyay’s presentation served as a rich source of theoretical understanding and practical insights, catering to both seasoned researchers and newcomers in the field. The exploration of binary choice models, logit and probit models, goodness-of-fit measures, and the empirical application collectively provided a holistic view of regression analysis, enriching the audience’s analytical toolkit for future research endeavors. The session’s impact extends beyond the confines of a lecture, empowering researchers with the skills to navigate the complexities of regression analyses with categorical dependent variables.

Posted by Reet Lath, a Research Intern at IMPRI.

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