✨
AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We use Almon [2] data to illustrate our theoretical results.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:The finite distributed lag models include highly correlated variables as well as lagged and unlagged values of the same variables.
📊
Key Findings & Data:The primary aim of this study is to compare the performances of alternative estimators to the OLS estimator defined by combining the Almon estimator with some other estimators according to the mean square error (MSE) criterion.
💡
Conclusion & Impact:We use Almon [2] data to illustrate our theoretical results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Finite distributed lag model · Almon estimator · Ridge estimator
Abstract
The finite distributed lag models include highly correlated variables as well as lagged and unlagged values of the same variables. Some problems are faced for this model when applying the ordinary least squares (OLS) method or econometric models such as Almon and Koyck models. The primary aim of this study is to compare the performances of alternative estimators to the OLS estimator defined by combining the Almon estimator with some other estimators according to the mean square error (MSE) criterion. We use Almon [2] data to illustrate our theoretical results.
🔖
Cite This Article
GÜLTAY, B., KAÇIRANLAR, S. (2022). Mean square error comparisons of the alternative estimators for the distributed lag models. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 5, 1215–1233. https://ajindex.com/articles/528704