Module 1: STATA Programming and Data Management Fundamentals

• Advanced Do-file structuring and project management for reproducibility
• Mastering Local and Global Macros for efficient, reusable code blocks
• Utilizing Loops (foreach, forvalues) to automate repetitive data tasks and analyses
• Creating and calling Ado-files for custom command development
• Techniques for debugging and error handling in complex STATA programs
• Practical session: Writing a robust Do-file that automates data cleaning, generates descriptive statistics, and exports the results using local and global macros.

Module 2: Advanced Data Reshaping and Cleaning Techniques

• Converting data between Wide and Long formats using reshape
• Utilizing merge and append for integrating data from multiple disparate sources
• Techniques for handling and imputing missing data (e.g., multiple imputation overview)
• Efficiently working with string variables, date/time formats, and variable labeling
• Managing large datasets by using sampling techniques and efficient memory allocation
• Practical session: Merging a large panel dataset with time-variant and time-invariant information, followed by reshaping the data from wide to long format for panel estimation.

Module 3: Mastering Linear Regression Diagnostics and Extensions

• Detecting and addressing heteroskedasticity and autocorrelation using robust standard errors
• Testing for multicollinearity and strategies for variable selection
• Assessing and addressing influential observations and outliers
• Performing advanced post-estimation testing: Omitted Variables, functional form (e.g., Ramsey RESET)
• Implementing interaction effects and interpreting marginal effects for continuous and discrete variables
• Practical session: Running an OLS regression, performing the necessary diagnostic tests, and re-estimating the model using robust and clustered standard errors as required.

Module 4: Generalized Linear Models (GLM) and Non-Linear Regression

• Introduction to the GLM framework, link functions, and variance functions
• Applying Logistic and Probit Regression for binary and categorical outcomes
• Modeling count data using Poisson and Negative Binomial Regression
• Implementing Ordered Logit/Probit and Multinomial Logit for multiple choice outcomes
• Interpreting marginal effects and odds ratios in non-linear models
• Practical session: Analyzing a dataset with a count outcome variable (e.g., number of events) and fitting both a Poisson and a Negative Binomial model, comparing the interpretation of results.

Module 5: Time Series Analysis and Forecasting in STATA

• Declaring time series data using the tsset command and working with time operators
• Testing for stationarity using unit root tests (e.g., Dickey-Fuller, Phillips-Perron)
• Estimating and interpreting ARIMA (Autoregressive Integrated Moving Average) models
• Performing Vector Autoregression (VAR) for analyzing dynamic relationships between multiple time series
• Generating in-sample and out-of-sample forecasts and calculating confidence intervals
• Practical session: Using financial data, run a unit root test, difference the series to achieve stationarity, and estimate an ARIMA model to generate a one-year forecast.

Module 6: Panel Data Econometrics: Fixed and Random Effects

• Understanding the structure and challenges of longitudinal/panel data
• Applying the Pooled OLS and comparing it with panel-specific models
• Implementing the Fixed Effects (Within) estimator to control for unobserved individual heterogeneity
• Implementing the Random Effects (GLS) estimator and conducting the Hausman test for model selection
• Introduction to Dynamic Panel Data models (e.g., System GMM) for dealing with lagged dependent variables
• Practical session: Estimating Fixed Effects and Random Effects models on a socioeconomic dataset, then conducting the Hausman test to select the appropriate model.

Module 7: Instrumental Variables (IV) and Treatment Effects Modeling

• Identifying endogeneity problems (omitted variable bias, simultaneity) and the need for IV
• Implementing the Two-Stage Least Squares (2SLS) estimator using the ivregress command
• Conducting diagnostic tests for IV validity (e.g., weak instruments, overidentification)
• Introduction to Propensity Score Matching (PSM) for estimating treatment effects
• Implementing Difference-in-Differences (DiD) models for causal inference in quasi-experiments
• Practical session: Using a dataset where a key variable is suspected of endogeneity, apply the 2SLS method with an appropriate instrument and interpret the causal effect.

Module 8: Survival Analysis and Event History Modeling

• Introduction to Survival Analysis concepts: hazard function, survival function, censoring
• Non-parametric estimation: Creating and interpreting Kaplan-Meier survival curves
• Implementing the semi-parametric Cox Proportional Hazards Model
• Testing and addressing the Proportional Hazards assumption
• Utilizing parametric survival models (e.g., Exponential, Weibull)
• Practical session: Analyzing a public health dataset on time-to-event, generating a Kaplan-Meier curve, and fitting a Cox proportional hazards model with covariates.

Module 9: Survey Data Analysis and Weighting Methods

• Understanding complex survey design features (stratification, clustering, primary sampling units)
• Declaring the survey design in STATA using the svyset command
• Implementing different types of survey weights (e.g., probability, post-stratification)
• Applying the svy: prefix to run descriptive statistics and regression models that account for design effects
• Calculating and interpreting design-based standard errors and confidence intervals
• Practical session: Utilizing a nationally representative survey dataset (e.g., Demographic and Health Survey) to correctly svyset the data and run a weighted logistic regression.

Module 10: Automation, Custom Reports, and Reproducible Research

• Generating high-quality, customized tables of results using estout and tabout commands
• Exporting graphics and formatted tables directly to Word or LaTeX using community-contributed commands
• Creating dynamic documentation and reports using STATA's Markdown/HTML integration
• Utilizing STATA's Project Manager for organized and collaborative workflows
• Comprehensive review of best practices for code archiving, version control, and ensuring reproducibility
• Practical session: Developing a comprehensive script to run multiple models, store the results, and automatically export a final report containing formatted tables and high-resolution graphics.

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588