# AIC, Overfitting Principles, and the Boundedness of Moments of Inverse Matrices for Vector Autotregressions and Related Models

@article{Findley2002AICOP, title={AIC, Overfitting Principles, and the Boundedness of Moments of Inverse Matrices for Vector Autotregressions and Related Models}, author={David F. Findley and Ching-Zong Wei}, journal={Journal of Multivariate Analysis}, year={2002}, volume={83}, pages={415-450} }

In his somewhat informal derivation, Akaike (in "Proceedings of the 2nd International Symposium Information Theory" (C. B. Petrov and F. Csaki, Eds.), pp. 610-624, Academici Kiado, Budapest, 1973) obtained AIC's parameter-count adjustment to the log-likelihood as a bias correction: it yields an asymptotically unbiased estimate of the quantity that measures the average fit of the estimated model to an independent replicate of the data used for estimation. We present the first mathematically…

## 42 Citations

Efficient and Consistent Data-Driven Model Selection for Time Series

- Mathematics, Computer Science
- 2021

This paper proves that consistent model selection criteria outperform classical AIC criterion in terms of efficiency and derives from a Bayesian approach the usual BIC criterion, by keeping all the second order terms of the Laplace approximation, a data-driven criterion denoted KC’.

Order selection for same-realization predictions in autoregressive processes

- Computer Science
- 2005

This paper presents the first theoretical verification that AIC and its variants are still asymptotically elficient (in the sense defined in Section 4) for same-realization predictions, and shows that A IC also yields a satisfactory saute- realization prediction in finite samples.

Statistica Sinica Preprint No : SS-2016-0069 . R 2 Title Nearly Unstable Processes : A Prediction Perspective

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- 2017

Prediction has long been a vibrant topic in modern probability and statistics. In addition to finding optimal forecast and model selection, it is argued in this paper that the prediction principle…

Nearly Unstable Processes: A Prediction Perspective

- Mathematics
- 2018

Prediction has long been a vibrant topic in modern probability and statistics. In addition to finding optimal forecast and model selection, it is argued in this paper that the prediction principle…

Forecast Averaging with Panel Data Vector Autoregressions

- Economics
- 2018

In this paper we propose a new forecast model averaging method for panel data vector autoregressions that permit limited forms of parameterized heterogeneity (including fixed effects or incidental…

Moment bounds and mean squared prediction errors of long-memory time series

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A moment bound for the normalized conditional-sum-of-squares (CSS) estimate of a general autoregressive fractionally integrated moving average (ARFIMA) model with an arbitrary unknown memory…

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This paper considers the conventional recursive (otherwise known as plug-in) and direct multistep forecasts in a panel vector autoregressive framework. We derive asymptotic expressions for the mean…

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An iterative method to approximate the target density by minimizing the cross-entropy information criterion (CIC) is proposed and it is demonstrated that the proposed method selects a parametric model that well approximates the targetdensity.

FROM A FINITE FAMILY OF POSSIBLY MISSPECIFIED TIME SERIES MODELS By

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A misspecification-resistant information criterion (MRIC) is proposed and it is shown that MRIC can be used in conjunction with a high-dimensional model selection method to select the (asymptotically) best predictive model across several high- dimensional misspecified time series models.

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