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Aug 27 by aptech Posted in Programming

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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Aug 20 by Eric Posted in Econometrics, Programming

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.
Aug 02 by Eric Posted in Econometrics, Time Series

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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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:
  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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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:
  1. What is Granger-causality?
  2. When to use Granger causality?
  3. 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:
  1. Loading and viewing dates side-by-side with other data types.
  2. Viewing and displaying dates in easy-to-read formats.
  3. Easily changing the date format.
  4. Using familiar date formats for filtering data.
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:
  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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