Building and Teaching Advanced Time-Series Tools in GAUSS
Overview
As a lead developer and educator for the Time Series Modeling Tools (TSMT) library in GAUSS, I played a central role in extending and refining GAUSS’s capabilities for advanced time series econometrics. This work combined product development, API design, statistical implementation, documentation, testing, tutorials, and customer education.
The project is one of the clearest examples of how I work: I can understand complex technical functionality, contribute to product direction, build usable tools, and then teach users how to apply those tools in real analytical workflows.
My Role
- Built new functionality from the ground up for advanced time series estimation models.
- Refactored and modernized existing code to improve usability, consistency, and performance.
- Designed streamlined user-facing APIs using optional arguments and structured outputs.
- Collaborated on product planning, helping define scope, prioritize features, and respond to customer needs.
- Wrote a comprehensive documentation suite with practical examples and usage guidance.
- Authored 30+ educational blog posts and tutorials on time series modeling topics.
- Supported users through documentation, examples, live guidance, and technical troubleshooting.
New Features Developed
State-Space Estimation for ARIMA and SARIMA Models
Added Kalman-filter-based likelihood estimation for models with latent components and seasonal structure.
Structural VAR Models with Restrictions
Implemented tools for structural VAR modeling, including long-run restrictions, short-run restrictions, and sign restrictions.
Nonlinear Time Series Tools
Developed and tested procedures for:
- Markov-switching autoregressive models
- Threshold autoregression
- Structural break models
These features expanded GAUSS’s modeling suite and supported a broader range of forecasting, macroeconomic, financial, and policy analysis workflows.
Product and API Improvements
- Simplified API patterns to reduce boilerplate and improve learnability.
- Unified input and output structures across time series functions.
- Improved consistency across related procedures.
- Reworked estimation routines and diagnostic tools for better performance.
- Designed examples and docs that helped users understand both syntax and methodology.
Education and Documentation
The education layer was integral to the product work. I created documentation, tutorials, and examples that helped users move from theoretical model concepts to usable workflows.
Topics included:
- Forecasting with ARIMA and VARIMA
- Unit root and cointegration testing
- Impulse response analysis
- Markov-switching model interpretation
- Structural decomposition in SVAR models
- State-space estimation
- Model selection and diagnostics
Example Resources
- Estimating SVAR Models with GAUSS
- Easier ARIMA Modeling with State Space
- SVAR with Sign Restrictions
- Unit Root Testing
- GAUSS TSMT Documentation
Skills Demonstrated
- Product development
- API design
- Technical education
- Econometric modeling
- Technical documentation
- Code examples and tutorials
- Customer support and adoption
- Product planning
- Statistical validation