Mastering Economic Data: Econometric Modelling with Python & R Training Course

Introduction

In today's data-driven world, the ability to effectively model economic phenomena is crucial for informed decision-making across all sectors. Econometric modeling, a discipline that blends economic theory with statistical methods, provides the rigorous framework to analyze complex relationships, forecast trends, and evaluate policy impacts. While traditional econometric software has served its purpose, the open-source powerhouses of Python and R have emerged as indispensable tools, offering unparalleled flexibility, a vast ecosystem of specialized libraries, and robust capabilities for handling large and diverse economic datasets.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of econometric modeling using both Python and R. From foundational regression analysis and time series forecasting to advanced panel data methods and causal inference techniques, you will gain hands-on expertise in applying these powerful programming languages to real-world economic challenges. This empowers you to build, estimate, interpret, and communicate sophisticated econometric models, enabling you to conduct rigorous analysis, generate actionable insights, and drive evidence-based strategies in your professional domain.

Target Audience

  • Economists and econometricians seeking to enhance their programming skills.
  • Data analysts and scientists working with economic or financial data.
  • Researchers in academia, government, and think tanks.
  • Financial professionals involved in quantitative analysis and forecasting.
  • Business intelligence specialists needing deeper economic insights.
  • Graduate students (Master's and PhD) in economics, finance, or statistics.
  • Policy analysts involved in economic impact assessment.
  • Anyone interested in applying modern computational tools to economic problems.

Duration: 10 days

Course Objectives

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

  • Understand the core principles of econometric modeling and its applications.
  • Master data manipulation, visualization, and exploration techniques using Python's Pandas and R's Tidyverse.
  • Implement various linear and non-linear regression models in both Python (Statsmodels, Scikit-learn) and R (lm, glm).
  • Apply advanced time series analysis techniques, including ARIMA, VAR, and GARCH models.
  • Conduct robust panel data analysis using fixed effects, random effects, and dynamic panel models.
  • Grasp the fundamentals of causal inference and implement methods like Instrumental Variables and Difference-in-Differences.
  • Perform diagnostic tests to assess model assumptions and validity.
  • Interpret and effectively communicate econometric results and model limitations.

Course Content

  1. Introduction to Econometrics, Python, and R
  • What is econometric modeling and why is it essential?
  • Introduction to Python for data analysis: environment setup, basic syntax, data structures (NumPy, Pandas)
  • Introduction to R for statistical computing: RStudio environment, basic syntax, data structures (vectors, data frames)
  • Advantages of using Python and R for econometrics
  • Overview of key libraries/packages: statsmodels, scikit-learn, pandas, matplotlib, seaborn (Python); lm, glm, plm, forecast, ggplot2, dplyr (R)
  1. Data Handling and Visualization
  • Importing and exporting data from various sources (CSV, Excel, databases)
  • Data cleaning and pre-processing: handling missing values, outliers, data transformations
  • Data manipulation: filtering, sorting, merging, and aggregating data
  • Descriptive statistics and exploratory data analysis (EDA)
  • Creating compelling data visualizations for economic insights using matplotlib/seaborn (Python) and ggplot2 (R)
  1. Linear Regression Fundamentals
  • The Classical Linear Regression Model (CLRM) assumptions
  • Ordinary Least Squares (OLS) estimation: theory and implementation in Python (statsmodels) and R (lm)
  • Interpreting regression coefficients and statistical significance
  • Hypothesis testing: t-tests, F-tests, and p-values
  • Model diagnostics: R-squared, adjusted R-squared, residuals analysis
  1. Advanced Linear Regression and Specification
  • Multiple regression: incorporating multiple independent variables
  • Dummy variables: modeling categorical effects
  • Interaction terms: understanding conditional relationships
  • Non-linear transformations for modeling curvilinear relationships
  • Multicollinearity: detection and remedies
  1. Model Diagnostics and Robustness
  • Heteroscedasticity: detection (e.g., White test, Breusch-Pagan) and robust standard errors (e.g., White, HAC)
  • Autocorrelation: detection (e.g., Durbin-Watson) and correction (e.g., Cochrane-Orcutt, AR models)
  • Endogeneity: introduction to omitted variable bias, measurement error, simultaneity
  • Model specification tests (e.g., Ramsey RESET test, Hausman test for endogeneity)
  • Outlier detection and influence diagnostics
  1. Time Series Analysis I: Basics and ARIMA Models
  • Characteristics of time series data: trends, seasonality, cycles
  • Stationarity: tests (e.g., Augmented Dickey-Fuller) and transformations (differencing)
  • Autocorrelation and Partial Autocorrelation Functions (ACF, PACF)
  • Autoregressive (AR), Moving Average (MA), and Autoregressive Integrated Moving Average (ARIMA) models
  • Model identification, estimation, and diagnostic checking for ARIMA models in Python (statsmodels, pmdarima) and R (forecast)
  1. Time Series Analysis II: Advanced Models and Forecasting
  • Vector Autoregression (VAR) models for multivariate time series
  • Cointegration and Error Correction Models (ECM)
  • Volatility modeling: ARCH and GARCH models for financial time series
  • Forecasting with time series models: point forecasts and confidence intervals
  • Evaluating forecast accuracy: RMSE, MAE, Theil's U
  1. Panel Data Econometrics
  • Structure and advantages of panel data (cross-sectional and time series dimensions)
  • Pooled OLS, Fixed Effects (FE) models, and Random Effects (RE) models
  • Estimating FE and RE models in Python (linearmodels, statsmodels) and R (plm)
  • Choosing between FE and RE: Hausman test
  • Dynamic Panel Data models (e.g., Arellano-Bond GMM) (brief introduction)
  1. Causal Inference with Econometrics
  • Review of correlation vs. causation and the counterfactual framework
  • Introduction to common quasi-experimental designs:
    • Instrumental Variables (IV): theory and 2SLS estimation
    • Difference-in-Differences (DiD): assumptions and implementation
    • Regression Discontinuity Design (RDD): sharp and fuzzy RDD
  • Implementing these methods in Python (linearmodels, causalinference) and R (estimatr, lfe, rdrobust)
  • Limitations and validity checks for causal inference methods
  1. Advanced Topics and Practical Applications
  • Limited Dependent Variable Models: Probit and Logit for binary outcomes
  • Survival analysis (duration models)
  • Introduction to Bayesian econometrics (conceptual)
  • Applying econometric models to real-world economic datasets (e.g., labor economics, macroeconomics, finance, development)
  • Reproducible research practices: using Jupyter Notebooks (Python) and R Markdown (R) for combining code, output, and commentary.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

 

Mastering Economic Data: Econometric Modelling With Python & R Training Course in Kenya
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