Module 1: Introduction to Longitudinal, Panel, and Time Series Data

• Defining Longitudinal Data: Repeated observations on the same subjects.
• Defining Panel Data: Cross-sectional units observed over time.
• Defining Time Series Data: Single unit observed over time.
• Key Characteristics and Advantages of Each Data Type.
• Common Applications Across Disciplines (Economics, Public Health, Social Sciences).
• Practical session: Identifying and distinguishing between different data types from provided datasets.

Module 2: Stata Fundamentals for Time-Series Data

• Importing and Exporting Data in Stata.
• Basic Data Cleaning and Transformation in Stata.
• Stata Syntax and Do-Files for Reproducibility.
• Introduction to Stata's Time-Series Operators (L., F., D., S.).
• Setting Time-Series and Panel Data in Stata (tsset, xtset).
• Practical session: Importing a raw dataset, cleaning it, and correctly tsseting or xtsetting it in Stata.

Module 3: Data Management for Longitudinal and Panel Data

• Reshaping Data: Long vs. Wide format.
• Merging and Appending Datasets.
• Handling Missing Values in Panel Data.
• Creating Lagged and Lead Variables.
• Generating Time-Varying and Time-Invariant Covariates.
• Practical session: Reshaping a dataset from wide to long format and generating lagged variables in Stata.

Module 4: Descriptive Analysis of Longitudinal and Panel Data

• Summary Statistics for Panel Data: xtsum, xttab.
• Visualizing Panel Data: Line plots, scatter plots by panel.
• Exploring Within-Group and Between-Group Variation.
• Autocorrelation and Cross-Correlation in Panel Data.
• Descriptive Statistics for Time Series: summarize, tabstat.
• Practical session: Generating descriptive statistics and visualizations for a panel dataset, interpreting within and between variations.

Module 5: Introduction to Linear Panel Data Models

• Pooled OLS Regression: Assumptions and limitations for panel data.
• Why Pooled OLS is Inefficient for Panel Data: Unobserved heterogeneity.
• The Concept of Unobserved Heterogeneity (Individual-Specific Effects).
• Fixed Effects vs. Random Effects: Conceptual differences.
• Introduction to xtreg command in Stata.
• Practical session: Running a pooled OLS regression on a panel dataset and discussing its shortcomings.

Module 6: Fixed Effects (FE) Model

• The Fixed Effects (Within) Estimator: Eliminating time-invariant unobserved effects.
• Assumptions of the Fixed Effects Model.
• Estimating FE Models in Stata: xtreg, fe.
• Interpreting FE Regression Output: Within-group variation.
• Advantages and Disadvantages of FE.
• Practical session: Estimating a Fixed Effects model in Stata, interpreting coefficients, and comparing with pooled OLS.

Module 7: Random Effects (RE) Model

• The Random Effects (GLS) Estimator: Modeling unobserved effects as random variables.
• Assumptions of the Random Effects Model.
• Estimating RE Models in Stata: xtreg, re.
• Interpreting RE Regression Output: Weighted average of within and between variation.
• Advantages and Disadvantages of RE.
• Practical session: Estimating a Random Effects model in Stata and interpreting coefficients.

Module 8: Choosing Between Fixed and Random Effects

• The Hausman Test: Formal test for choosing between FE and RE.
• Interpreting Hausman Test Results.
• Practical Considerations for Model Choice.
• Robust Standard Errors in Panel Data Models.
• Introduction to Clustered Standard Errors.
• Practical session: Performing the Hausman test in Stata and making an informed decision on model choice.

Module 9: Dynamic Panel Data Models

• Introduction to Dynamic Panel Data: Lagged dependent variables as regressors.
• The Endogeneity Problem in DPD Models.
• Instrumental Variables (IV) and GMM (Generalized Method of Moments) Estimators.
• Arellano-Bond (Difference GMM) Estimator.
• Arellano-Bover/Blundell-Bond (System GMM) Estimator.
• Practical session: Estimating a dynamic panel data model using xtabond or xtdpd in Stata and interpreting the results.

Module 10: Introduction to Time Series Analysis

• Characteristics of Time Series Data: Trends, seasonality, cycles, irregular components.
• Time Series Decomposition.
• Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF).
• White Noise and Random Walk Processes.
• Introduction to Time Series Plotting in Stata.
• Practical session: Plotting various time series, calculating and interpreting ACF and PACF plots.

Module 11: Stationarity and Unit Root Tests

• The Concept of Stationarity: Mean, variance, and autocorrelation constant over time.
• Importance of Stationarity for Time Series Regression.
• Visual Inspection for Stationarity.
• Formal Unit Root Tests: Dickey-Fuller (DF), Augmented Dickey-Fuller (ADF).
• Interpreting Unit Root Test Results.
• Practical session: Performing ADF tests on several time series and determining their order of integration.

Module 12: Autoregressive (AR) and Moving Average (MA) Models

• Autoregressive (AR) Models: Modeling current values based on past values.
• Moving Average (MA) Models: Modeling current values based on past error terms.
• Identifying AR and MA Orders using ACF and PACF.
• Estimating AR and MA Models in Stata.
• Interpreting AR and MA Coefficients.
• Practical session: Estimating AR and MA models for stationary time series data and interpreting the output.

Module 13: Autoregressive Integrated Moving Average (ARIMA) Models

• The Concept of Integration (I): Differencing to achieve stationarity.
• Combining AR, I, and MA: The ARIMA(p,d,q) model.
• Steps in ARIMA Modeling: Identification, Estimation, Diagnostic Checking, Forecasting.
• Estimating ARIMA Models in Stata: arima command.
• Forecasting with ARIMA Models.
• Practical session: Building and forecasting with an ARIMA model for a non-stationary time series.

Module 14: Vector Autoregression (VAR) Models

• Introduction to VAR Models: Modeling multiple interdependent time series.
• Lag Length Selection Criteria for VAR Models.
• Estimating VAR Models in Stata: var command.
• Impulse Response Functions (IRFs): Tracing the effect of shocks.
• Variance Decomposition: Attributing forecast error variance.
• Practical session: Estimating a VAR model for a set of macroeconomic variables and interpreting IRFs.

Module 15: Cointegration and Error Correction Models

• The Concept of Cointegration: Long-run equilibrium relationships between non-stationary series.
• Why Cointegration is Important.
• Johansen Cointegration Test: Testing for cointegrating relationships.
• Vector Error Correction Models (VECM): Modeling short-run dynamics and long-run equilibrium.
• Estimating VECM in Stata: vec command.
• Practical session: Performing Johansen cointegration tests and estimating a VECM in Stata.

Module 16: Advanced Topics in Panel Data

• Unbalanced Panel Data: Handling missing observations.
• Panel Data with Cross-Sectional Dependence.
• Spatial Panel Data Models: Accounting for spatial autocorrelation.
• Panel Data with Endogenous Regressors: Instrumental Variable (IV) approaches.
• Non-linear Panel Data Models (e.g., Panel Logit/Probit).
• Practical session: Implementing a panel data model with robust standard errors accounting for cross-sectional dependence.

Module 17: Advanced Topics in Time Series

• ARCH/GARCH Models: Modeling volatility clustering.
• Structural Breaks in Time Series: Identifying and testing for regime changes.
• State-Space Models and Kalman Filter (Conceptual).
• Time Series Cross-Sectional (TSCS) Data: Combining time series and panel aspects.
• Forecasting Evaluation and Model Comparison.
• Practical session: Estimating a GARCH model for financial time series data.

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