Category: Econometrics

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.
Tagged in

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.

Introduction to the Fundamentals of Panel Data

Panel data, sometimes referred to as longitudinal data, is data that contains observations about different cross sections across time. Panel data exhibits characteristics of both cross-sectional data and time-series data. This blend of characteristics has given rise to a unique branch of time series modeling made up of methodologies specific to panel data structure. This blog offers a complete guide to those methodologies including the nature of panel data series, types of panel data, and panel data models.

Introduction to the Fundamentals of Time Series Data and Analysis

The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time series data requires a unique set of tools and methods, collectively known as time series analysis. This article covers the fundamental concepts of time series analysis and should give you a foundation for working with time series data. Everything is covered from time series plotting to time series modeling.

New release of tspdlib 1.0

The preliminary econometric package for Time Series and Panel Data Methods has been updated and functionality has been expanded in this first official release of tspdblib 1.0. The tspdlib 1.0 package includes functions for time series unit root tests in the presence of structural breaks, time series and panel data unit root tests in the presence of structural breaks, and panel data causality tests. It is available for direct installation using the GAUSS Package Manager.

Fundamental Bayesian Samplers

The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.

Have a Specific Question?

Get a real answer from a real person

Need Support?

Get help from our friendly experts.

Try GAUSS for 14 days for FREE

See what GAUSS can do for your data

© Aptech Systems, Inc. All rights reserved.

Privacy Policy