Econometrics is the essential toolkit for economists, providing the methods to analyze economic data and test economic theories. This Econometrics with R/Python training course empowers you to master these techniques using two of the most powerful and versatile programming languages: R and Python. You'll learn how to apply econometric methods, from basic regression to advanced time series analysis and causal inference, using these languages, enhancing your ability to conduct rigorous economic research, analyze data, and make data-driven decisions. This course bridges the gap between theoretical econometrics and practical application, equipping you with in-demand skills for today's data-driven world.

Target Audience:

This course is designed for individuals seeking to apply econometric methods using R or Python. The target audience includes:

• Economists working in academia, government, and the private sector
• Financial analysts and researchers
• Data scientists and analysts working with economic data
• Researchers and PhD students in economics and related fields
• Anyone seeking to enhance their econometric skills using programming languages

Course Objectives:

Upon completion of this Econometrics with R/Python training course, participants will be able to:

• Use R or Python to import, clean, manipulate, and visualize economic data.
• Implement core econometric techniques, including linear regression, hypothesis testing, and model diagnostics.
• Apply advanced econometric methods, such as time series analysis, panel data analysis, and causal inference.
• Build and evaluate econometric models using R or Python.
• Interpret and communicate econometric results effectively.
• Automate econometric analyses using scripting and programming.
• Utilize relevant R/Python packages for econometrics and data analysis.
• Understand the connection between econometric theory and its practical implementation.
• Develop proficiency in using R or Python for data analysis in economics.
• Enhance their career prospects in the data-driven economics.

Duration:           10 Days

Course Content

Module 1: Introduction to Econometrics and R/Python

• What is econometrics? The role of data analysis in economics.
• Introduction to R or Python: Setting up the environment, basic syntax, and data structures.
• Why use R/Python for econometrics? Advantages and applications.
• Course overview: Structure, learning objectives, and assessment methods.

Module 2: Data Handling and Manipulation

• Importing data: Reading data from various sources (CSV, Excel, databases, APIs).
• Data cleaning: Handling missing values, outliers, and inconsistencies.
• Data transformation: Creating new variables, recoding, and transforming data.
• Data manipulation: Filtering, sorting, merging, and aggregating data.

Module 3: Descriptive Statistics and Data Visualization

• Descriptive statistics: Calculating summary statistics and exploring data distributions.
• Data visualization: Creating informative charts and graphs using ggplot2 (R) or matplotlib/seaborn (Python).
• Exploring relationships between variables: Scatter plots, correlation matrices.
• Communicating data insights effectively.

Module 4: Linear Regression I: Basic Concepts

• The linear regression model: Assumptions, estimation, and interpretation.
• Ordinary least squares (OLS): Estimating regression coefficients.
• Hypothesis testing: t-tests, F-tests, and p-values.
• Model diagnostics: Assessing model fit and checking assumptions.

Module 5: Linear Regression II: Advanced Topics

• Multiple regression: Including multiple independent variables.
• Dummy variables: Incorporating categorical variables.
• Interaction terms: Modeling interactions between variables.
• Non-linear transformations: Modeling non-linear relationships.

Module 6: Model Diagnostics and Specification

• Multicollinearity: Detecting and addressing multicollinearity.
• Heteroscedasticity: Detecting and correcting for heteroscedasticity.
• Autocorrelation: Detecting and addressing autocorrelation.
• Model selection: Choosing the best model based on various criteria.

Module 7: Time Series Analysis I: Basic Concepts

• Time series data: Characteristics and properties.
• Stationarity: Testing for stationarity and transformations.
• Autocorrelation and autocovariance: Understanding time series patterns.
• ARIMA models: Autoregressive integrated moving average models.

Module 8: Time Series Analysis II: Forecasting

• Forecasting with ARIMA models: Point forecasts and interval forecasts.
• Model evaluation: Assessing forecast accuracy.
• Advanced forecasting techniques: Exponential smoothing, dynamic regression.

Module 9: Panel Data Analysis

• Panel data: Structure and types.
• Fixed effects models: Controlling for unobserved heterogeneity.
• Random effects models: Allowing for variation across individuals.
• Applications of panel data analysis in economics.

Module 10: Causal Inference

• Correlation vs. causation: Understanding the difference.
• Randomized controlled trials (RCTs): The gold standard for causal inference.
• Instrumental variables (IV): Addressing endogeneity.
• Regression discontinuity (RD): Exploiting sharp changes in policies.

Module 11: Advanced Econometric Techniques (Optional)

• Limited dependent variable models: Logit, Probit.
• Survival analysis: Modeling time-to-event data.
• Non-parametric methods.

Module 12: Applications and Case Studies

• Real-world applications of econometrics in various economic fields.
• Case studies: Analyzing economic data and solving real-world problems using R/Python.
• Best practices for reproducible econometric research.
• Communicating econometric results effectively.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement.

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request.

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 5 working days before commencement of the training.

Course Booking

Please use the “book now” or “inquire” buttons on this page to either book your space or make further enquiries.