Learn everything you need to know to run the Fourier LM unit root test with your data and interpret the results.Tagged in
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:Tagged in
- 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.
Today we will help you to understand and resolveTagged in
Error G0058 Index Out-of-RangeWe will :
- Explain the cause of the index out-of-range error in GAUSS.
- Explain why performing index assignments past the end of your data can lead to bad outcomes.
- Show how to use some functions and operators that can assist with diagnosing and resolving this error.
- Work through an example to resolve an indexing problem.
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.
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:Tagged in
- 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?
Today we will help you to understand and resolve Error G0064: Operand Missing. We will answer the questions:Tagged in
- What is an operand?
- How do common mathematical and non-mathematical operators interact with operands?
- What are common causes of operand missing errors?
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?
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.
Learn everything you need to know to run the Maki cointegration test with your data and interpret the results in this new GAUSS video.
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.