# Category: Econometrics

## 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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## 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.

## Easy Management of Categorical Variables

Categorical variables offer an important opportunity to capture qualitative effects in statistical modeling. Unfortunately, it can be tedious and cumbersome to manage categorical variables in statistical software. The new GAUSS category type, introduced in GAUSS 21, makes it easy and intuitive to work with categorical data. In today’s blog we use real-life housing data to explore the numerous advantages of the GAUSS category type including:
• Easy set up and viewing of categorical data.
• Simple renaming of category labels.
• Easy changing of the reference base case and reordering of categories.
• Single-line frequency plots and tables.
• Internal creation of dummy variables for regressions.
• Proper labeling of categories in regression output.
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## Introduction to Categorical Variables

Categorical variables have an important role in modeling, as they offer a quantitative way to include qualitative outcomes in our models. However, it is important to know how to appropriately use them and how to appropriately interpret models that include them. In this blog, you’ll learn the fundamentals you need to know to make the most of categorical variables.

## Maximum Likelihood Estimation in GAUSS

Maximum likelihood is a fundamental workhorse for estimating model parameters with applications ranging from simple linear regression to advanced discrete choice models. Today we learn how to perform maximum likelihood estimation with the GAUSS Maximum Likelihood MT library using our simple linear regression example. We’ll show all the fundamentals you need to get started with maximum likelihood estimation in GAUSS including:
• How to create a likelihood function.
• How to call the `maxlikmt` procedure to estimate parameters.
• How to interpret the results from `maxlikmt`.
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