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.
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.
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.
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.
The aggregate function, first available in GAUSS version 20, computes statistics within data groups. This is particularly useful for panel data. In today’s blog, we take a closer look at aggregate.
In time series modeling we often encounter trending or nonstationary time series data. Understanding the characteristics of such data is crucial for developing proper time series models. For this reason, unit root testing is an essential step when dealing with time series data. In this blog post, we cover everything you need to conduct time series data unit root tests using GAUSS.
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.
