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.

Maximum likelihood is a widely used technique for estimation with applications in many areas including time series modeling, panel data, discrete data, and even machine learning.
In today’s blog, we cover the fundamentals of maximum likelihood including:

The basic theory of maximum likelihood.

The advantages and disadvantages of maximum likelihood estimation.

GAUSS procedures are user-defined functions that allow you to combine a sequence of commands to perform desired tasks. In this blog, you will learn the fundamentals of creating and using procedures in GAUSS.

Dummy variables are a common econometric tool, whether working with time series, cross-sectional, or panel data. Unfortunately, raw datasets rarely come formatted with dummy variables that are regression ready.
In today’s blog, we explore several options for creating dummy variables from categorical data in GAUSS, including:

Creating dummy variables from a file using formula strings.

Creating dummy variables from an existing vector of categorical data.

Creating dummy variables from an existing vector of continuous variables.

Optional input arguments can make your statistical computing more efficient and enjoyable. GAUSS version 20 added a new suite of tools to make it easy for you to add optional input arguments to your GAUSS procedures. This blog lays the foundation to start using optional arguments in your GAUSS programs.

You’re probably familiar with the basic find-and-replace. However, large projects with many files across several directories, require a more powerful search tool. The GAUSS Source Browser is the powerful search-and-replace tool you need. In this blog, you’ll learn more about using the advanced search-and-replace tools in GAUSS to effectively navigate and edit in projects with multiple files and directories.

The aggregate function, first available in GAUSS version 20, computes statistics within data groups. This is particularly useful for panel data. In today’s blog, we take a closer look at aggregate.

Often times we need to mix multiple graph types in order to create a plot which most effectively tells the story of our data. In this post, we will create a plot of the Phillips Curve in the United States over two separate time periods. We will show how to add scatter points and lines as well as data series’ of different lengths to a single plot. However, our main focus will be showing you how to control the styling of all aspects of the plot in these cases.

GAUSS packages provide access to powerful tools for performing data analysis. This guide covers all you need to know to get the most from GAUSS packages including: