# Category: 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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## Understanding and Solving the Structural Vector Autoregressive Identification Problem

The structural vector autoregressive model is a crucial time series model used to understand and predict economic impacts and outcomes. In this blog, we look closely at the identification problem posed by structural vector autoregressive models and its solution. In particular, we cover:
• What is the structural VAR model and what is the reduced form VAR?
• What is the relationship between structural VAR and reduced form VAR models?
• What is the structural VAR identification problem?
• What are common solutions to the structural VAR identification problem?
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## 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?

## 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:
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.

## 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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