GAUSS Software - Fastest Platform for Data Analytics | Data Analytics Blog | Aptech

Sep 21 by Eric Posted in Econometrics, Programming

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:

The basic theory of maximum likelihood.
The advantages and disadvantages of maximum likelihood estimation.
The log-likelihood function.
Modeling applications.

Aug 31 by Mark Harris Posted in 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 survey data

Aug 04 by aptech Posted in Graphics

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.

Jul 17 by aptech Posted in Best Practices, Programming

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.

Jun 11 by Eric Posted in Econometrics, Programming

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.

May 26 by Eric Posted in Time Series

How to Interpret Cointegration Test Results

In this blog, we will explore how to set up and interpret cointegration results using a real-world time series example. We will cover the case with no structural breaks as well as the case with one unknown structural break using tools from the GAUSS tspdlib library.

Tagged in cointegration, unit root

May 07 by aptech Posted in Graphics, User Interface

How to Interactively Create Reusable Graphics Profiles

Learn how to create reusable graphics profiles with a few clicks of your mouse.

Apr 02 by aptech Posted in Programming

How to Create a Simple Table with sprintf in GAUSS

Learn how to create a simple output table with variable names, parameter estimates and standard errors using sprintf in GAUSS.

Feb 12 by aptech Posted in Programming

The Basics of Optional Arguments in GAUSS Procedures

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.

Jan 28 by Eric Posted in Econometrics, Time Series

A Guide to Conducting Cointegration Tests

Cointegration is an important tool for modeling the long-run relationships in time series data. If you work with time series data, you will likely find yourself needing to use cointegration at some point. This blog provides an in-depth introduction to cointegration and will cover all the nuts and bolts you need to get started.

Tagged in cointegration, error correction model, time series

Recent Posts