Introduction

Econometrics combines statistical techniques with economic theory to analyze and quantify economic relationships, test hypotheses, and forecast future economic trends. This 5-day course focuses on advanced econometric techniques and their application in empirical research. Participants will gain practical knowledge of the methods used to model and analyze economic data, as well as develop the skills necessary to apply these techniques in real-world scenarios.

By the end of the course, participants will be proficient in the use of advanced econometric models for analyzing complex economic data, performing hypothesis testing, and drawing meaningful conclusions for policy-making, business strategy, and economic analysis.

Course Objectives

• Understand advanced econometric methods and techniques used in empirical research.
• Learn how to estimate and interpret econometric models such as time series, panel data, and limited dependent variable models.
• Gain proficiency in hypothesis testing, model selection, and addressing issues such as multicollinearity, heteroscedasticity, and endogeneity.
• Understand the applications of econometrics in economic policy, financial markets, and business decision-making.
• Use econometric software to analyze complex data and produce meaningful results.

Who Should Attend?

• Economists, financial analysts, and statisticians who want to deepen their econometric skills.
• Researchers and analysts working in government, academic, or private sector organizations.
• Students or professionals preparing for advanced research roles in economics or finance.
• Data analysts and anyone interested in applying econometrics to solve real-world economic problems.

Day 1: Introduction to Advanced Econometrics and Model Building

• Session 1: Review of Basic Econometrics Concepts
  • Recap of simple and multiple regression models
  • Key assumptions in linear regression: homoscedasticity, normality, independence
  • Diagnostic checks: residual analysis, goodness-of-fit measures (R-squared, adjusted R-squared)
• Session 2: Model Specification and Selection
  • Understanding model specification: functional form, variable selection, and avoiding omitted variable bias
  • Model selection criteria: AIC, BIC, and adjusted R-squared
  • The problem of multicollinearity and methods for detection and correction
• Session 3: Endogeneity and Instrumental Variables
  • Introduction to endogeneity: causes and consequences for model estimation
  • Instrumental variable (IV) regression: how and when to use IVs
  • Identifying valid instruments and testing for instrument relevance and exogeneity

Day 2: Time Series Analysis

• Session 1: Introduction to Time Series Econometrics
  • Characteristics of time series data: trends, seasonality, and autocorrelation
  • Time series models: AR, MA, ARMA, and ARIMA models
  • Stationarity and unit roots: detecting and addressing non-stationarity
• Session 2: Advanced Time Series Models
  • Cointegration and Error Correction Models (ECM)
  • Testing for cointegration: Engle-Granger and Johansen tests
  • Vector Autoregressive (VAR) models and their applications in macroeconomic forecasting
• Session 3: Forecasting and Model Evaluation
  • Forecasting with time series models: out-of-sample prediction, cross-validation
  • Evaluating forecast accuracy: RMSE, MAPE, and other error metrics
  • Practical application: using time series models for forecasting economic and financial data

Day 3: Panel Data Analysis

• Session 1: Introduction to Panel Data Models
  • Understanding panel data: cross-sectional vs. time series data
  • Advantages of panel data: controlling for unobserved heterogeneity
  • Fixed effects vs. random effects models
• Session 2: Estimation and Testing in Panel Data
  • Estimating fixed and random effects models
  • Hausman test: choosing between fixed and random effects models
  • Panel data regressions with time-varying covariates
• Session 3: Dynamic Panel Data Models
  • The Arellano-Bond estimator for dynamic panel data models
  • GMM estimation: general method of moments in panel data
  • Addressing endogeneity and autocorrelation in panel data

Day 4: Limited Dependent Variable Models

• Session 1: Introduction to Limited Dependent Variables
  • Types of limited dependent variables: binary outcomes, censored and truncated data
  • Logit and Probit models for binary dependent variables
  • Estimating and interpreting coefficients in logit and probit models
• Session 2: Multinomial Logit and Ordered Probit Models
  • Multinomial logit model: extensions for more than two categories
  • Ordered probit model: modeling ordinal outcomes with limited dependent variables
  • Application examples: consumer choice modeling, voting behavior, etc.
• Session 3: Tobit Model and Censored Data
  • The Tobit model: dealing with censored dependent variables
  • Estimation of Tobit models and interpretation of results
  • Applications of Tobit models in economics, such as credit scoring and labor market analysis

Day 5: Advanced Topics in Econometrics and Practical Applications

• Session 1: Structural Equation Modeling (SEM)
  • Introduction to SEM and its applications in econometrics
  • Identifying and estimating structural relationships between variables
  • Confirmatory factor analysis and path analysis
• Session 2: Non-Linear Models in Econometrics
  • Introduction to non-linear regression models: exponential, logarithmic, and polynomial models
  • Estimating non-linear models using maximum likelihood estimation
  • Applications of non-linear models in economic and financial research
• Session 3: Practical Application with Econometric Software
  • Using econometric software (e.g., STATA, EViews, R) for model estimation and analysis
  • Hands-on case studies: time series forecasting, panel data modeling, and limited dependent variable estimation
  • Interpreting econometric results and presenting findings to stakeholders

Course Conclusion

• Recap of Key Learnings
• Interactive Q&A Session
• Certification of Completion
• Networking Opportunity