Panel and Time Series Data Analysis Using Stata

Course Date Onsite fees: Location Register
29/07/2024 To 02/08/2024 1,000 USD, 90,000 Ksh Nairobi
28/10/2024 To 01/11/2024 1,000 USD, 90,000 Ksh Nairobi



Longitudinal or panel data are multi-dimensional data involving measurements over time. Such data are analysed using dynamic model. Dynamic models have become increasingly popular due to their ability to take into account both short and long term effects and unobserved heterogeneity between economic agents in the estimation of the parameter estimates. Stata is very specialized in handling dynamic data.


This training course provides an overview of existing dynamic data analysis techniques. Participants will be taken through a series of illustrative examples, with a theoretical and applied overview. Recent issues in dynamic panel data analysis will also be covered. The course concludes by addressing the issues of; i) non-stationarity in long panels, where the time series (as opposed to cross-sectional) characteristic of the data dominates; and ii) cointegration. The training will pay particular attention (using a combination of both official Stata and user written dynamic panel data analysis commands) to: i) evaluating which specific econometric methodology/specification is more appropriate for the analysis in hand; ii) selection of the appropriate instruments; iii) rigorous post estimation diagnostic/specification testing; and iv) the problems of inference resulted from weak-instrument bias, instrument-proliferation bias and small-sample bias. Special attention will also be given to the interpretation and presentation of results.



5 Days



By the end of this training, participants will become knowledgeable in the following:

  • Usefulness and problems with Panel Data
  • Opportunities and challenges of panel data.
  • Linear models data analysis with dynamic data
  • Logistic regression models with dynamic data
  • Count data models with dynamic data
  • Linear structural equation models with dynamic data



Module 1: Introduction

Introduction to Panel Data

        Why Are Panel Data Desirable?

        Problems with Panel Data

        Examples of Time-varying and time-invariant variables


Opportunities and challenges of panel data.

        Data requirements

        Control for unobservables

        Determining causal order

        Problem of dependence

        Software considerations


Module 2:Linear models

        Robust standard errors

        Generalized estimating equations

        Random effects models

        Fixed effects models

        Between-within models


Module 3: Logistic regression models

        Robust standard errors


        Subject-specific vs. population averaged methods

        Random effects models

        Fixed effects models

        Between-within models


Module 4: Count data models

        Poisson models

        Negative binomial models


Module 5: Linear structural equation models

        Fixed and random effects in the SEM context

        Models for reciprocal causation with lagged effects



This training can also be customized for your institution upon request. You can also have it delivered to your preferred location. For further inquiries, please contact us through: Telephone: +254 732 776 700 or Email: 



Participants should be reasonably proficient in English.  During the trainings, participants should come with their own laptops.



The course fee covers the course tuition, training materials, two break refreshments, lunch and a study visit (where applicable).



FineResults Research Services arranges accommodation for their clients upon request. For reservations contact us through Telephone: +254732776700 or Email: 



Payment should be transferred to FineResults Research Limited bank before commencement of training. Upon effecting the payment, participants should send scanned proof of payment through the email: 



  • All requests for cancellations must be received in writing.
  • Changes will become effective on the date of written confirmation being received.


Call us on +254 732 776 700/ +254 759 285 295
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