Recent Posts

Getting Started with Time Series in GAUSS

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 :
  • Load and verify time series data.
  • Filter observations by date.
  • Merge data from different sources.
  • Create basic time series plots.
  • Perform stationarity testing.
  • Fit a basic ARIMA model.

How to Load Excel Data into GAUSS

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.
  • Use autogenerated code in a program file.
  • Change variable names
  • Set up categoical labels and and base cases.

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.

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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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.

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!

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.

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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