Eric has been working to build, distribute, and strengthen the GAUSS universe since 2012. He is an economist skilled in data analysis and software development. He has earned a B.A. and MSc in economics and engineering and has over 18 years of combined industry and academic experience in data analysis and research.
The GAUSS FRED database integration, introduced in GAUSS 23, is a time-saving feature that allows you to import FRED data directly into GAUSS. This means you have thousands of datasets at your fingertips without ever leaving GAUSS. These tools also ensure that FRED data is imported directly into a GAUSS dataframe format, which can eliminate hours of data cleaning and the headaches that come with it.
In today’s blog, we will learn how to use the FRED import tools to:
Search for a FRED data series.
Import FRED data to GAUSS, including merging multiple series.
Use advanced import tools to perform data transformations.
In today’s blog, we explore a simple but powerful member of the unobserved components family – the local level model. This model provides a straightforward method for understanding the dynamics of time series data.
This blog will examine:
Time series decomposition.
Unobserved components and the local level model.
Understanding the estimated results for a local level model.
State-space models provide a powerful environment for modeling dynamic systems. Their flexibility has resulted in a wide variety of applications across fields including radar tracking, 3-D modeling, monetary policy modeling, weather forecasting, and more.
In this blog, we look more closely at state-space modeling using a simple time series model of inflation.
We cover:
The components of state-space models.
Representing state-space models in GAUSS.
Estimating model parameters using state-space models.
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.
