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.
In today’s blog, we look at how to save time and reduce errors using GAUSS’s new data management tools. Using the quarterly real GDP dataset from the FRED database we explore GAUSS’s new data management tools. In particular, we examine how to:
- Deal with irregular dataset headers.
- Change variable names.
- Filter dates and change the date display.
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:Tagged in
- How to create a likelihood function.
- How to call the
maxlikmtprocedure to estimate parameters.
- How to interpret the results from
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.Tagged in
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.
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.Tagged in
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
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.
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.