Introduction The GAUSS TSMT application module provides a comprehensive suite of tools for MLE and state-space estimation, model diagnostics, and forecasting of univariate, multivariate, and nonlinear time series models. The latest Time Series MT (TSMT) 3.1. is now available. If you own TSMT 3.0 the update is available for free. Installation The TSMT update requires [...]
GAUSS packages provide access to powerful tools for performing data analysis. Learn how to install the GAUSS Package Manager, and get the quickest access to the full suite of GAUSS packages, in this short video. Additional Resources GAUSS Package Manager Using GAUSS Packages a Complete Guide
In this video, you’ll learn the basics of time series analysis in GAUSS. See how quick and easy it is to get started with everything from data loading to ARIMA analysis!
You’ll see first hand how to :
Loading data is often the first step to your data analysis in GAUSS. In this video, you’ll learn how to save time and avoid data loading errors when working with Excel files.
Our video demonstration shows just how quick and easy it can be to load time series, categorical and numeric variables from Excel files into GAUSS. You’ll learn how to:
Interactively load Excel data files.
Perform advanced loading steps, Such as loading specific sheets, or specifying values as missing values.
Our new Introduction to GAUSS for Stata Users offers a guide for Stata Users who are looking to get started quickly in GAUSS. It offers side-by-side comparisons of essential analysis tasks in GAUSS and Stata.
The latest GAUSS 22.1.0 update is available now and is free if you own GAUSS 22. This maintenance release is one of our most extensive with over 40 enhancements, new functions, and new examples, and bug fixes.
When they’re done right, graphs are a useful tool for telling compelling data stories and supporting data models. However, too often graphs lack the right components to truly enhance understanding.
In this blog, we look at how a few quick customizations help make graphs more impactful. In particular, we will consider:
Using grid lines without cluttering a graph.
Changing tick labels for readability.
Using clear axis labels.
Marking events and outcomes with lines, bars, and annotations.
There is no getting around the fact that data wrangling, cleaning, and exploring plays an important role in any empirical research. Data management can be time-consuming, error-prone, and can make or break results.
GAUSS 22 is built to take the pain out of dealing with your data and to let you move seamlessly towards tackling your important research questions.
In today’s blog, we walk through how to efficiently prepare and explore real-world data before modeling or estimation. We’ll look at:
Loading and merging data.
Cleaning data to eliminate misentries, missing values, and more.
