# Recent Posts

## 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 Errors: G0058 Index out-of-Range

Today we will help you to understand and resolve `Error G0058 Index Out-of-Range` We will :
1. Explain the cause of the index out-of-range error in GAUSS.
2. Explain why performing index assignments past the end of your data can lead to bad outcomes.
3. Show how to use some functions and operators that can assist with diagnosing and resolving this error.
4. Work through an example to resolve an indexing problem.
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## Introduction to Handling Missing Values

Handling missing values is an important step in data cleaning that can impact model validity and reliability. Despite this, it can be difficult to find examples and resources about how to deal with missing values. This blog helps to fill that void and covers:
• Types of missing values.
• Dealing with missing values.
• Missing values in practice.

## 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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## Understanding Errors: G0064 Operand Missing

Today we will help you to understand and resolve Error G0064: Operand Missing. We will answer the questions:
1. What is an operand?
2. How do common mathematical and non-mathematical operators interact with operands?
3. What are common causes of operand missing errors?
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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?

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