@inbook{be30786763d34736a6dda410bc9f47fa,
title = "Outlier Detection in Simultaneous Equations with Panel Data",
abstract = "An outlier in a set of data is an observation (or subset of observation) that appears to be inconsistent with the remainder of that set of data. Outliers can have a marked effect on any type of empirical analysis. Therefore, outlier detection is very important in a statistical analysis. Outlier detection in regression model used residual to determine the anomaly score. This research used internally studentized residual (ISR) to detect outliers in panel data model and simultaneous equation with panel data. The application of data in this study is data for ten countries that are members of the ASEAN-China Free Trade Area (ACFTA) during the period 2007–2021. The result showed that outlier observations will vary depending on the type of modeling. Each model will obtain certain residuals, so that the observations detected as outliers will also be different. Outlier detection results in the simultaneous equations show that the outlier observations are a subset of the independent equation outliers that compose them.",
keywords = "ACFTA, Internally studentized residual, Outlier detection, Panel data, Simultaneous equation",
author = "Suci Ismadyaliana and Setiawan and Purnomo, {Jerry Dwi Trijoyo}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.",
year = "2024",
doi = "10.1007/978-981-97-0293-0_30",
language = "English",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "415--427",
booktitle = "Lecture Notes on Data Engineering and Communications Technologies",
address = "Germany",
}