ACCUMULATED PREDICTION ERRORS, INFORMATION CRITERIA AND OPTIMAL FORECASTING FOR AUTOREGRESSIVE TIME SERIES

By Ching-Kang Ing. 2007

75 citations recorded by the source corpus.

The predictive capability of a modification of Rissanen's accumulated prediction error (APE) criterion, APE$_{\delta_{n}}$,is investigated in infinite-order autoregressive (AR($\infty$)) models. Instead of accumulating squares of sequential prediction errors from the beginning, APE$_{\delta_{n}}$ is obtained by summing these squared errors from stage $n\delta_{n}$, where $n$ is the sample size and $0

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4644d8bdd2bc030a3b5eef7841385d3fe2bb9d80

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

In Statistics and Probability

Citations in the corpus

Parametric or nonparametric? A parametricness index for model selection (2011, methodology): “According to [38], when k̂n is selected by BIC for order selection, we have that k̂n basically falls within a constant from 1 2c log(n/ log(n)) in probability.”
Model Selection (2018, background): “While this can be achieved in a pointwise asymptotic framework (Yang (2007), Ing (2007)), it is not surprising – given the preceding discussion – that it is impossible to achieve this goal in a uniform sense: No model selection procedure can simultaneously be consistent (like BIC) and minimax-rate…”
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models (2023, background): “This topic has been extensively studied in time series analysis, and several theoretical models have been established to investigate it (Hannan et al., 1989; Ing, 2007; Ding et al., 2017).”
Model Selection Techniques: An Overview (2018, background): “For example, it has been shown under some conditions that PLS with t 0 /n → 1 shares the same asymptotic property of AIC (see, e.g., [43] and the references therein).”
Cross-validation for selecting a model selection procedure (2015, methodology): “…a pre-test, George and Foster (2000) based on an empirical Bayes approach, Yang (2007a) by examining the history of BIC at different sample sizes, Ing (2007) by choosing between AIC and BIC through accumulated prediction errors in a time series setting, Liu and Yang (2011) by choosing between…”