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.
The preliminary econometric package for Time Series and Panel Data Methods has been updated and functionality has been expanded in this first official release of tspdblib 1.0. The tspdlib 1.0 package includes functions for time series unit root tests in the presence of structural breaks, time series and panel data unit root tests in the presence of structural breaks, and panel data causality tests. It is available for direct installation using the GAUSS Package Manager.
The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.
We use regression analysis to understand the relationships, patterns, and causalities in data. Often we are interested in understanding the impacts that changes in the dependent variables have on our outcome of interest. However, not all models provide such straightforward interpretations. Coefficients in more complex models may not always provide direct insights into the relationships we are interested in.
In this blog, we look more closely at the interpretation of marginal effects in three types of models:
Purely linear models.
Models with transformations in independent variables.
Models with transformations of dependent variables.
In this blog, we examine one of the fundamentals of panel data analysis, the one-way error component model. We cover the theoretical background of the one-way error component model, we examine the fixed-effects and random-effects models, and provide an empirical example of both.
When policy changes or treatments are imposed on people, it is common and reasonable to ask how those people have been impacted. This is a more difficult question than it seems at first glance. In today’s blog, we examine difference-in-differences (DD) estimation, a common tool for considering the impact of treatments on individuals.
In this blog, we extend our analysis of unit root testing with structural breaks to panel data. Using panel data unit roots tests found in the GAUSS tspdlib we consider if a panel of international current account balances collectively shows unit root behavior.
In this blog, we examine the issue of identifying unit roots in the presence of structural breaks. We will use the quarterly US current account to GDP ratio to compare results from a number of unit root test found in the GAUSS tspdlib library including the: Zivot-Andrews (1992) unit root test with a single structural break, Narayan and Popp (2010) unit root test with two structural breaks, Lee and Strazicich (2013, 2003) LM tests with one and two structural breaks, Enders and Lee Fourier (2012) ADF and LM tests.
This week’s blog brings you the second video in the series examining running publicly available GAUSS code. This video runs the popular code by Hatemi-J for testing cointegration with multiple structural breaks. In this video you will learn how to:
Substitute your own dataset.
Modify the indexing commands for your data.
Remove missing values.
Preview your data after loading with the Ctrl+E keyboard shortcut.
