Category: 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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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.

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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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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Beginner's Guide To Maximum Likelihood Estimation

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:
  1. The basic theory of maximum likelihood.
  2. The advantages and disadvantages of maximum likelihood estimation.
  3. The log-likelihood function.
  4. Modeling applications.

Basics of GAUSS Procedures

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.

How To Create Dummy Variables 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.

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