Category: 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:
1. What is Granger-causality?
2. When to use Granger causality?
3. How to use Granger causality?

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:
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?

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.

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.

Preparing and Cleaning FRED data in GAUSS

In today’s blog, we look at how to save time and reduce errors using GAUSS’s new data management tools. Using the quarterly real GDP dataset from the FRED database we explore GAUSS’s new data management tools. In particular, we examine how to:
• Deal with irregular dataset headers.
• Change variable names.
• Filter dates and change the date display.

How to Interpret Cointegration Test Results

In this blog, we will explore how to set up and interpret cointegration results using a real-world time series example. We will cover the case with no structural breaks as well as the case with one unknown structural break using tools from the GAUSS tspdlib library.
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A Guide to Conducting Cointegration Tests

Cointegration is an important tool for modeling the long-run relationships in time series data. If you work with time series data, you will likely find yourself needing to use cointegration at some point. This blog provides an in-depth introduction to cointegration and will cover all the nuts and bolts you need to get started.
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How to Conduct Unit Root Tests in GAUSS

In time series modeling we often encounter trending or nonstationary time series data. Understanding the characteristics of such data is crucial for developing proper time series models. For this reason, unit root testing is an essential step when dealing with time series data. In this blog post, we cover everything you need to conduct time series data unit root tests using GAUSS.

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