Recent Posts

Dates and Times Made Easy

Working with dates in data analysis software can be tedious and error-prone. The new GAUSS date type, introduced in GAUSS 21, can save you time and prevent frustration and errors. The date data type is part of the GAUSS dataframe alongside the category, string, and numeric type. In this blog, we will explore the advantages the date type has to offer, including:
  1. Loading and viewing dates side-by-side with other data types.
  2. Viewing and displaying dates in easy-to-read formats.
  3. Easily changing the date format.
  4. Using familiar date formats for filtering data.

The Intuition Behind Impulse Response Functions and Forecast Error Variance Decomposition

This blog provides a non-technical look at impulse response functions and forecast error variance decomposition, both integral parts of vector autoregressive models. If you’re looking to gain a better understanding of these important multivariate time series techniques you’re in the right place. We cover the basics, including:
  1. What is structural analysis?
  2. What are impulse response functions?
  3. How do we interpret impulse response functions?
  4. What is forecast error variance decomposition?
  5. How do we interpret forecast error variance decomposition?

Understanding Errors | G0025 : Undefined symbol

Today we will help you to understand and resolve Error G0025: Undefined symbol We will answer the questions:
  1. What is a GAUSS symbol
  2. What are the most common types of GAUSS symbols?
  3. How do I define a GAUSS symbol?
  4. How do I resolve the error `G0025: Undefined symbol`?

Introduction to the Fundamentals of Vector Autoregressive Models

In today’s blog, you’ll learn the basics of the vector autoregressive model. We lay the foundation for getting started with this crucial multivariate time series model and cover the important details including:
  1. What a VAR model is.
  2. Who uses VAR models.
  3. Basic types of VAR models.
  4. How to specify a VAR model.
  5. Estimation and forecasting with VAR models.

Easy Management of Categorical Variables

Categorical variables offer an important opportunity to capture qualitative effects in statistical modeling. Unfortunately, it can be tedious and cumbersome to manage categorical variables in statistical software. The new GAUSS category type, introduced in GAUSS 21, makes it easy and intuitive to work with categorical data. In today’s blog we use real-life housing data to explore the numerous advantages of the GAUSS category type including:
  • Easy set up and viewing of categorical data.
  • Simple renaming of category labels.
  • Easy changing of the reference base case and reordering of categories.
  • Single-line frequency plots and tables.
  • Internal creation of dummy variables for regressions.
  • Proper labeling of categories in regression output.
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Introduction to Categorical Variables

Categorical variables have an important role in modeling, as they offer a quantitative way to include qualitative outcomes in our models. However, it is important to know how to appropriately use them and how to appropriately interpret models that include them. In this blog, you’ll learn the fundamentals you need to know to make the most of categorical variables.

New Release of TSPDLIB 2.0

Learn why TSPDLIB 2.0 is the easiest and most comprehensive time series and panel data unit root and cointegration testing package on the market. The tspdlib 2.0 package includes expanded functions for time series and panel data testing in the presence of structural breaks. In addition, TSPDLIB 2.0 is easier than ever to use with new implementation of default parameter settings, updated output printing, and automatic date variable detection.

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