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Mar 14 by Eric Posted in Releases

GAUSS 22.1.0 Maintenance Release Now Available
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.
Dec 14 by Eric Posted in Graphics, Panel data

Visualizing COVID-19 Panel Data With GAUSS 22
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.
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Dec 02 by Eric Posted in Econometrics, Programming, User Interface

Getting to Know Your Data With GAUSS 22
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.
  • Exploring data.
Nov 16 by aptech Posted in Releases

GAUSS 22
GAUSS 22 brings many substantial new features that will save you hours of time and frustration with everyday tasks including:
  • Data exploration
  • Data cleaning and management
  • Graphics
See some of the ways that GAUSS 22 will help you make the most of your limited research time below!
Oct 21 by Eric Posted in Econometrics, Time Series

The Quantile Autoregressive-Distributed Lag Parameter Estimation and Interpretation in GAUSS
The QARDL model has grown increasingly popular in time series analysis. It is a convenient model for addressing autocorrelation, disentangling long-term and short-term relationships, and addressing asymmetric relationships. In today’s blog, we look at the basics of the QARDL model including:
  1. The intuition behind the QARDL model.
  2. How to estimate the QARDL model in GAUSS.
  3. How to interpret the QARDL results.
Sep 29 by Eric Posted in Econometrics, Time Series

The Structural VAR Model at Work: Analyzing Monetary Policy
In today’s blog, we put the building blocks of the structural vector autoregressive (SVAR) model to work in a practical application. We’ll use one of the most common applications of SVAR models, monetary policy analysis, to see the SVAR in action. After this blog, you should have a stronger understanding of:
  • How to use Granger causality testing to inform model selection.
  • How to implement short-run identification restrictions.
  • How to conduct and interpret structural VAR analysis.
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Sep 03 by Eric Posted in Time Series

Introduction to Markov-Switching Models
Markov-switching models offer a powerful tool for capturing the real-world behavior of time series data. Today’s blog provides an introduction to Markov-switching models including:
  • What a regime switching model is and how it differs from a structural break model.
  • When we should use the regime switching model.
  • What a Markov-switching model is.
  • What tools we use to estimate Markov-switching models.
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Aug 27 by aptech Posted in Programming

Understanding Errors: G0058 Index out-of-Range
Today we will help you to understand and resolve Error G0058 Index Out-of-Range We will :
  1. Explain the cause of the index out-of-range error in GAUSS.
  2. Explain why performing index assignments past the end of your data can lead to bad outcomes.
  3. Show how to use some functions and operators that can assist with diagnosing and resolving this error.
  4. Work through an example to resolve an indexing problem.
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Aug 20 by Eric Posted in Econometrics, Programming

Introduction to Handling Missing Values
Handling missing values is an important step in data cleaning that can impact model validity and reliability. Despite this, it can be difficult to find examples and resources about how to deal with missing values. This blog helps to fill that void and covers:
  • Types of missing values.
  • Dealing with missing values.
  • Missing values in practice.

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