Estimation & Analysis Tools in the GAUSS Platform
Overview
As part of my ongoing role at Aptech, I’ve contributed a wide range of core tools to the GAUSS analytics platform. These tools enhance the platform’s capabilities for estimation, inference, data cleaning, and diagnostics — serving both academic and applied users in economics, finance, and data science.
Estimation Methods
Core statistical estimators developed or enhanced:
- Feasible Generalized Least Squares (FGLS) for efficient estimation under heteroskedasticity.
- Quantile Regression supporting multiple
τlevels, IID and robust VCE, and bootstrapped errors. - Generalized Method of Moments (GMM) with flexible instrument setup and over-identification testing.
- Kernel Density Estimation (KDE) for flexible nonparametric distribution modeling.
Inference & Standard Error Tools
Support tools for statistical testing and confidence interval construction:
- Wald Tests with support for parameter restrictions and multi-equation systems.
- Robust, Clustered, and HAC Standard Errors, integrated with multiple estimation methods.
Data Preparation & Exploration
Tools for cleaning, tabulating, and understanding your data before modeling:
- Panel Data Tools: Detection of duplicate IDs, missing panel gaps, and panel data sorting, panel data timespans, and more.
- Categorical Data Utilities: Counting, tabulation, and frequency plots.
- Descriptive Diagnostics: Automated reporting tools for data inspection and modeling inputs.
Technical Focus
- Designed reusable modular code with optional arguments and control structures.
- Prioritized computational efficiency and batch usability for large datasets.
- Aligned tooling design with real-world use cases in academic research, consulting, and education.
Additional Contributions
- Integrated help files and usage examples across all procedures.
- Provided support for edge cases (e.g., unbalanced panels, singular instruments).
- Supported external GAUSS developers in implementing custom extensions using this toolkit.