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
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:Tagged in
- 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.
Though many standard econometric models assume that variance is constant, structural breaks in variance are well-documented, particularly in economic and finance data. If these changes are not accurately accounted for, they can hinder forecast inference measures, such as forecast variances and intervals. In this blog, we consider a tool that can be used to help locate structural breaks in variance — the iterative cumulative sum of squares algorithm(ICSS) (Inclan and Tiao, 1994).Tagged in
Many estimations and forecasting methods are not valid if the mean and variance are not constant across time. Today we examine how to test for both using GLS-unit root tests with multiple structural breaks.Tagged in
[markdown] While structural breaks are a widely examined topic in pure time series, their impacts on panel data models have garnished less attention. However, in their forthcoming paper [Chowdhury and Russell (2018)](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjpe.12142) demonstrate that [structural breaks](https://www.aptech.com/structural-breaks/) can cause bias in the instrumental variable panel estimation framework. This work highlights that structural breaks shouldn’t be limited to pure time series models and warrant equal attention in panel data models. [/markdown]Tagged in