R For Econometrics: Unleash The Power Of Data Analysis Training Course

Introduction:

Econometrics provides the tools to analyze economic data and test economic theories. This R for Econometrics training course empowers you to master these techniques using R, a powerful and versatile programming language specifically designed for statistical computing and data visualization. You'll learn how to apply econometric methods, from basic regression to advanced time series analysis and causal inference, using R, 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. 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 R

Course Objectives:

Upon completion of this R for Econometrics training course, participants will be able to:

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

Duration

10 Days

Course Content

Module 1: Introduction to R and Econometrics

• What is econometrics? The role of data analysis in economics.
• Introduction to R: Setting up the environment, RStudio interface, basic syntax, and data types.
• Why use R for econometrics? Advantages, packages, and community support.
• Course overview: Structure, learning objectives, and assessment methods.

Module 2: Data Handling and Manipulation in R

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

Module 3: Descriptive Statistics and Data Visualization in R

• Descriptive statistics: Calculating summary statistics (mean, median, sd, etc.) using dplyr and summary().
• Data visualization: Creating informative charts and graphs using ggplot2.
• Exploring relationships between variables: Scatter plots, histograms, boxplots, correlation matrices.
• Communicating data insights effectively.

Module 4: Linear Regression I: Basic Concepts in R

• The linear regression model: Assumptions, estimation, and interpretation.
• Ordinary least squares (OLS) in R: Using lm() to estimate regression coefficients.
• Hypothesis testing: t-tests, F-tests, and p-values using summary() and anova().
• Model diagnostics: Assessing model fit and checking assumptions using plot() and other diagnostic tools.

Module 5: Linear Regression II: Advanced Topics in R

• Multiple regression: Including multiple independent variables in lm().
• Dummy variables: Incorporating categorical variables using factor().
• Interaction terms: Modeling interactions between variables.
• Non-linear transformations: Modeling non-linear relationships using poly() or custom functions.

Module 6: Model Diagnostics and Specification in R

• Multicollinearity: Detecting and addressing multicollinearity using vif().
• Heteroscedasticity: Detecting and correcting for heteroscedasticity using bptest() and robust standard errors.
• Autocorrelation: Detecting and addressing autocorrelation using acf() and pacf().
• Model selection: Choosing the best model based on AIC, BIC, and other criteria.

Module 7: Time Series Analysis I: Basic Concepts in R

• Time series data in R: Creating time series objects using ts().
• Stationarity: Testing for stationarity using adf.test() and transformations using diff().
• Autocorrelation and autocovariance: Understanding time series patterns using acf() and pacf().
• ARIMA models: Autoregressive integrated moving average models using arima().

Module 8: Time Series Analysis II: Forecasting in R

• Forecasting with ARIMA models: Point forecasts and interval forecasts using forecast().
• Model evaluation: Assessing forecast accuracy using various metrics.
• Advanced forecasting techniques: Exponential smoothing and other methods using the forecast package.

Module 9: Panel Data Analysis in R

• Panel data in R: Creating panel data objects.
• Fixed effects models: Controlling for unobserved heterogeneity using plm().
• Random effects models: Allowing for variation across individuals using plm().
• Applications of panel data analysis in economics.

Module 10: Causal Inference in R

• Correlation vs. causation: Understanding the difference.
• Randomized controlled trials (RCTs) analysis in R.
• Instrumental variables (IV) estimation using ivreg().
• Regression discontinuity (RD) analysis.

Module 11: Advanced Econometric Techniques in R (Optional)

• Limited dependent variable models: Logit and Probit models using glm().
• Survival analysis: Modeling time-to-event data.
• Non-parametric methods.

Module 12: Applications and Case Studies in R

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

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. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

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. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.