Introduction
This comprehensive course is designed to equip participants with the theoretical understanding and practical skills needed to conduct rigorous longitudinal panel and time series data analysis using Stata. It begins with an overview of data structures, emphasizing the unique characteristics of panel and time series datasets, including their temporal dimension and potential for individual-level heterogeneity. Participants are introduced to Stata’s interface and commands tailored to manage and explore such data efficiently.
The course then delves into techniques for managing panel and time series datasets, including data reshaping, variable transformations, and handling missing values. Special emphasis is placed on understanding fixed effects, random effects, and the Hausman test to decide between the two. Participants will learn how to estimate linear panel data models, interpret coefficients, and diagnose model adequacy using robust standard errors and specification tests.
In the context of time series analysis, the course explores essential concepts such as stationarity, autocorrelation, unit root testing, and model specification. Participants will gain hands-on experience estimating ARIMA models, vector autoregressions (VAR), and error correction models (ECM). Forecasting techniques and model evaluation using in-sample and out-of-sample accuracy metrics are also covered to enhance predictive capacity.
The final part of the course integrates applied case studies to reinforce learning and allow participants to apply techniques to real-world datasets. Topics such as Granger causality, impulse response analysis, and cointegration in panel and time series contexts are addressed. By the end of the course, participants will be proficient in using Stata to analyze longitudinal and time series data, supporting robust empirical research and evidence-based decision-making.
Duration
5 days
Course Objectives
By the end of this course the learners should be able to:
· To equip participants with a strong understanding of the structure, characteristics, and applications of longitudinal panel and time series data.
· To develop practical skills in managing, cleaning, and transforming panel and time series datasets using Stata.
· To enable participants to estimate, interpret, and compare fixed effects and random effects models for panel data analysis.
· To provide the knowledge required to conduct diagnostic tests and validate the assumptions of panel and time series models.
· To train participants in the application of time series techniques, including ARIMA, VAR, and error correction models, using Stata.
· To enhance forecasting abilities through model estimation, evaluation, and interpretation of predictive results.
· To apply advanced techniques such as unit root testing, cointegration, Granger causality, and impulse response analysis.
· To empower participants to independently conduct empirical research and policy analysis using real-world longitudinal and time series data.
Who should Attend?
This course is ideal for
· Economists, statisticians, and data analysts involved in policy analysis or forecasting.
· Researchers and academics working with panel or time series data in social sciences, public health, or economics.
· Monitoring and evaluation (M&E) professionals handling project data collected over time.
· Government and NGO officers engaged in evidence-based planning, budgeting, or reporting.
· Graduate students and PhD candidates conducting quantitative research with longitudinal datasets.
· Central bank, finance, and investment professionals analyzing market trends or macroeconomic indicators.
· Data scientists and quantitative analysts looking to enhance their econometric modeling skills using Stata.
Course Outline
Module 1: Introduction to Panel and Time Series Data Analysis
- Overview of panel and time series data structures
- Why use panel and time series data? Benefits and limitations
- Key distinctions: time-varying vs. time-invariant variables
- Opportunities and challenges: unobserved heterogeneity, dependence, and measurement errors
- Data requirements, sources, and management considerations
- Causal inference and temporal ordering in longitudinal research
- Overview of statistical software tools with a focus on Stata
Module 2: Panel Data Linear Models
- Data preparation and reshaping in Stata (long vs. wide format)
- Fixed effects, random effects, and mixed models
- Hausman test and model selection criteria
- Between-within and first-difference estimators
- Generalized Estimating Equations (GEE) for correlated outcomes
- Cluster-robust standard errors and diagnostics
Module 3: Nonlinear Panel Models – Binary and Ordinal Outcomes
- Fixed and random effects logistic regression models
- Conditional logit and population-averaged (marginal) models
- Mixed-effects logit/probit models using xtlogit, xtprobit
- GEE for nonlinear models
- Interpreting odds ratios and predicted probabilities in panel data
Module 4: Models for Count and Limited Dependent Variables
- Poisson and negative binomial regression for panel count data
- Overdispersion and zero-inflated models
- Fixed and random effects approaches for count data
- Marginal effects and interpretation of coefficients
Module 5: Time Series Analysis with Stata
- Stationarity and non-stationarity: ADF and KPSS tests
- AR, MA, ARIMA, and SARIMA modeling
- Seasonality and trend decomposition
- Forecasting techniques and evaluation metrics (RMSE, MAPE)
- Autocorrelation and partial autocorrelation functions (ACF, PACF)
- Dynamic regression models and distributed lags
Module 6: Advanced Time Series and Panel Techniques
- Vector autoregression (VAR) and vector error correction models (VECM)
- Granger causality testing and impulse response functions
- Panel unit root and cointegration tests (Levin-Lin-Chu, Im-Pesaran-Shin)
- Dynamic panel models (Arellano-Bond estimator, GMM techniques)
- Long-run and short-run relationships in panel settings
Module 7: Structural Equation Modeling for Longitudinal Data
- Introduction to linear SEM and path analysis in longitudinal designs
- Fixed vs. random effects in SEM
- Cross-lagged panel models and reciprocal causation
- Latent growth curve models for repeated measures
- Application using Stata's sem and gsem commands
Module 8: Emerging Topics and Applications
- Machine learning integration for panel data forecasting (e.g., LASSO, random forests)
- Synthetic control methods for policy evaluation
- Event history analysis and survival models for longitudinal data
- Multilevel and hierarchical linear modeling (HLM) for nested longitudinal data
- Practical issues in real-world datasets: attrition, missing data, and imputation strategies
- Ethics and reproducibility in longitudinal research
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: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill 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 Upskill 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: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
