Author: Erica

New Release of TSPDLIB 2.0

Learn why TSPDLIB 2.0 is the easiest and most comprehensive time series and panel data unit root and cointegration testing package on the market. The tspdlib 2.0 package includes expanded functions for time series and panel data testing in the presence of structural breaks. In addition, TSPDLIB 2.0 is easier than ever to use with new implementation of default parameter settings, updated output printing, and automatic date variable detection.

Maximum Likelihood Estimation in GAUSS

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.
  • How to interpret the results from maxlikmt.
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Panel Data Stationarity Test With Structural Breaks

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.
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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.

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.

Advanced Search and Replace in GAUSS

You’re probably familiar with the basic find-and-replace. However, large projects with many files across several directories, require a more powerful search tool. The GAUSS Source Browser is the powerful search-and-replace tool you need. In this blog, you’ll learn more about using the advanced search-and-replace tools in GAUSS to effectively navigate and edit in projects with multiple files and directories.

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.

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