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.
Today we will help you to understand and resolveTagged in
Error G0025: Undefined symbolWe will answer the questions:
- What is a GAUSS symbol
- What are the most common types of GAUSS symbols?
- How do I define a GAUSS symbol?
- How do I resolve the error `G0025: Undefined symbol`?
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:Tagged in
- 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.
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:Tagged in
- How to create a likelihood function.
- How to call the
maxlikmtprocedure to estimate parameters.
- How to interpret the results from
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.
- The log-likelihood function.
- Modeling applications.
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.
Learn how to create a simple output table with variable names, parameter estimates and standard errors using sprintf in GAUSS.
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.