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Dergiler / International Econometric Review / 2010 / Cilt: 2 - Sayı: 2

Variance Estimates and Model Selection

Sıdıka BAŞÇI
Asad ZAMAN
Arzdar KİRACI
Sayfa
57–72
Yayın Tarihi
2010-09-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Such replacements improve the probability of finding the true model, at least in large samples.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The large majority of the criteria for model selection are functions of the usual variance estimate for a regression model.
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Temel Bulgular & Veri:A cross validated variance estimate is more robust to specification errors (see, for example, Efron, 1983).
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Sonuç & Katkı:Such replacements improve the probability of finding the true model, at least in large samples.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Autoregressive Process · Lag Order Determination · Model Selection Criteria

Abstract

The large majority of the criteria for model selection are functions of the usual variance estimate for a regression model. The validity of the usual variance estimate depends on some assumptions, most critically the validity of the model being estimated. This is often violated in model selection contexts, where model search takes place over invalid models. A cross validated variance estimate is more robust to specification errors (see, for example, Efron, 1983). We consider the effects of replacing the usual variance estimate by a cross validated variance estimate, namely, the Prediction Sum of Squares (PRESS) in the functions of several model selection criteria. Such replacements improve the probability of finding the true model, at least in large samples.

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Bu Makaleye Atıf Yap

BAŞÇI, S., ZAMAN, A., KİRACI, A. (2010). Variance Estimates and Model Selection. International Econometric Review, Cilt: 2 - Sayı: 2, 57–72. https://ajindex.com/articles/832176

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Kaynakça (14)

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