ADVANCED ECONOMETRIC

Overview

Theoretical and Applied Advanced Econometric Models with STATA

Deepen your understanding of modern econometric methods through this comprehensive instructor-led course. Spread over 5–6 weeks, it is designed for learners who want to master STATA-based application of advanced quantitative methods like MLE, GMM, Logit, Panel Regression, and more.

Whether you’re conducting academic research or evaluating public policies, this course will help you build rigorous, empirically validated models with confidence.

Advanced Econometric Analysis

Duration: 45–50 Hours | Mode: Instructor-led | Tools Covered: STATA (Primary)

This program is ideal for students and professionals in Economics, Public Policy, Social Sciences, or Business, offering deep insights into advanced techniques used for causal inference, forecasting, and structural modeling.

Participants from Europe, North America, South America and Africa can also request us for alternative timings.

Course Highlights:

Interactive Live Classes: Conducted Monday to Friday, 8:00 PM to 9:30 PM IST (GMT+5:30), with flexible timing options for international participants.
Software Training: Learn the practical use of STATA for real-world econometric modeling and advanced estimation.
Research-Focused Curriculum: Equips learners with skills for applied research and policy analysis using industry-approved statistical tools.

What You’ll Learn:

OLS, MLE, and GMM Estimation
Logit, Probit, Tobit, and Multinomial/Ordinal Regression
Count Data Models (Poisson, Negative Binomial, Hurdle)
Simultaneous Equation Models (2SLS, 3SLS)
Dynamic Models and Lag Structures (Koyck, Almon)
Panel Data Techniques (FE, RE, ARDL)
Difference-in-Differences (DID) Estimation
Quantile Regression Methods
Nonlinear Regression and Functional Forms
Principal Component Analysis (PCA)
Linear Discriminant Analysis (Upcoming)

This course helps learners build actionable skills in econometric theory, model estimation, and statistical software. Ideal for academic researchers and working professionals in data, development, and economics.

Practice Datasets Used

STATA Press datasets
NSSO Data (India) – various rounds
IHDS Data – 2004–05 & 2011–12
NSE Datasets (in progress)
World Bank Indicators
Wooldridge & Gujarati textbook datasets

Join a global classroom of learners from Europe, North America, South America, Africa, and beyond – and take your analytical skills to the expert level.

Curriculum

11 Sections
52 Lessons
50 Hours

Expand all sectionsCollapse all sections

1. Estimation Techniques & Econometric Theory
Get a solid foundation in estimation strategies used in econometric modeling. Learn the fundamentals of OLS, the logic of Maximum Likelihood Estimation, and the robustness of Generalized Method of Moments for real-world applications.
5
2. Qualitative Response Regression Models
Understand how to model binary, censored, and categorical response variables using advanced regression frameworks such as Logit, Probit, and Tobit models.
6
3. Count Data Models
Explore regression models tailored for count-dependent variables, often used in health, labor, and policy research. Handle over-dispersion, zero-inflation, and truncation challenges.
5
4. Simultaneous Equation Models
Learn to build and estimate multi-equation systems with interdependent variables. Understand the critical concepts of identification and apply Two-Stage and Three-Stage Least Squares methods.
5
5. Dynamic Econometric Models
Model the dynamics of time and behavior using lag structures. Learn how past values influence present outcomes and build autoregressive frameworks for economic data.
7
6. Panel Data Models
Combine cross-sectional and time series analysis using panel data techniques. This section trains you on advanced estimation with both fixed and random effects.
5
7. Difference-in-Differences (DID)
Master quasi-experimental techniques using DID models for program and policy evaluation. Use longitudinal data to estimate treatment effects.
3
8. Quantile Regression
Go beyond average effects by modeling different points of the conditional distribution of your outcome variable. Ideal for skewed or outlier-prone data.
4
9. Non-Linear Regression Models
Learn when and how to apply nonlinear modeling techniques using flexible functional forms and iterative estimation methods.
5
10. Principal Component Analysis (PCA)
Reduce data dimensionality while preserving structure. Learn how to extract uncorrelated principal components from multivariate datasets.
4
11. Linear Discriminant Analysis (LDA) (Upcoming)
Get introduced to supervised classification techniques for group prediction. Learn how LDA works and its future application in economic classification tasks.
3

₹4,999.00

Course Features

Lectures 52
Quiz 0
Duration 45–50 Hours
Skill level All levels
Language English
Students 341
Assessments Yes

Overview

Theoretical and Applied Advanced Econometric Models with STATA