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.
Reliable unit root testing is an important step of any time series analysis or panel data analysis.
However, standard time series unit root tests and panel data unit root tests aren’t reliable when structural breaks are present. Because of this, when structural breaks are suspected, we must employ unit root tests that properly incorporate these breaks.
Today we will examine one of those tests, the Carrion-i-Silvestre, et al. (2005) panel data test for stationarity in the presence of multiple structural breaks.
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.
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.
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.
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.
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.
