Data Analytics Blog - Page 2 of 9

Jul 26 by aptech Posted in Programming

Understanding Errors: G0064 Operand Missing

Today we will help you to understand and resolve Error G0064: Operand Missing. We will answer the questions:

What is an operand?
How do common mathematical and non-mathematical operators interact with operands?
What are common causes of operand missing errors?

Jun 29 by Eric Posted in Econometrics, Time Series

Introduction to Granger Causality

Multivariate time series analysis turns to VAR models not only for understanding the relationships between variables but also for forecasting. In today’s blog, we look at how to improve VAR model selection and achieve better forecasts using Granger-causality. We explore the questions:

What is Granger-causality?
When to use Granger causality?
How to use Granger causality?

Jun 18 by Eric Posted in Econometrics, Programming, Time Series

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:

Loading and viewing dates side-by-side with other data types.
Viewing and displaying dates in easy-to-read formats.
Easily changing the date format.
Using familiar date formats for filtering data.

Jun 14 by Eric Posted in Econometrics, Time Series, Video

How to Run the Maki Cointegration Test (Video)

Learn everything you need to know to run the Maki cointegration test with your data and interpret the results in this new GAUSS video.

May 21 by Eric Posted in Programming

What is a GAUSS Dataframe and Why Should You Care?

The GAUSS dataframe, introduced in GAUSS 21, is a powerful tool for storing data. In today’s blog, we explain what a GAUSS dataframe is and discuss the advantages of making it a part of your everyday GAUSS use.

May 06 by Eric Posted in Econometrics, Time Series

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:

What is structural analysis?
What are impulse response functions?
How do we interpret impulse response functions?
What is forecast error variance decomposition?
How do we interpret forecast error variance decomposition?

Apr 23 by aptech Posted in Programming

Understanding Errors | G0025 : Undefined symbol

Today we will help you to understand and resolve Error G0025: Undefined symbol We will answer the questions:

What is a GAUSS symbol
What are the most common types of GAUSS symbols?
How do I define a GAUSS symbol?
How do I resolve the error `G0025: Undefined symbol`?

Tagged in errors, undefined symbol

Apr 15 by Eric Posted in Econometrics, Time Series

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:

What a VAR model is.
Who uses VAR models.
Basic types of VAR models.
How to specify a VAR model.
Estimation and forecasting with VAR models.

Apr 07 by Eric Posted in Releases

Maintenance release of TSPDLIB 2.1.0

The TSPDLIB 2.1.0 update includes bug fixes, new features, and new examples. It can be easily installed using the GAUSS Package Manager.

Mar 19 by Eric Posted in Econometrics, Programming, User Interface

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.

Tagged in categorical data, categorical variables

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