Recent Posts

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.

Anchoring Vignettes and the Compound Hierarchical Ordered Probit (CHOPIT) Model

Self-assessments are a common survey tool but, they can be difficult to analyze due to bias arising from systematic variation in individual reporting styles, known as reporting heterogeneity. Anchoring vignette questions combined with the Compound Hierarchical Ordered Probit (CHOPIT) model, allows researchers to address this issue in survey data (King et al. 2004). This methodology is based on two key identifying assumptions:
  • Response consistency (RC)
  • Vignette equivalence (VE)
In today’s blog we look more closely the fundamental pieces of this modeling technique including the:
  • Typical data set up.
  • Hierarchical Ordered Probit Model (HOPIT).
  • Anchoring vignettes.
  • Likelihood and identifying assumptions used for estimation.
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How to Create Tiled Graphs in GAUSS

Placing graphs next to each other can be a great way to present information and improve data visualization. Today we will learn how to create tiled graphs in GAUSS with the easy-to-use plotLayout procedure.

We will work through two simple examples where you will learn:
  • How to created tiled layouts which are uniform and layouts with graphs of different sizes.
  • Which graph types can be used with plotLayout.
  • How to clear your tiled graph layouts.

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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