Robust out-of-sample inference

By Michael W. McCracken. 2000

202 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/288e894d358ab88c8d3627c5c05b6947d92c2bc6

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Citations in the corpus

ACCUMULATED PREDICTION ERRORS, INFORMATION CRITERIA AND OPTIMAL FORECASTING FOR AUTOREGRESSIVE TIME SERIES (2007, methodology): “Recently, a modification of APE, APEδn(k) = n−1 ∑ i=nδn (xi+1 − x̂i+1(k)) 2,(1.5) with 1/n ≤ δn ≤ 1 − (1/n) depending on n, has also been considered by several authors, for example, West [30], McCracken [20] and Inoue and Kilian [15].”
Tests of Equal Forecast Accuracy and Encompassing for Nested Models (2001, background): “Clark and McCracken (2000) show that while the ENC-T and ENC-REG tests (considered below) have non-standard distributions for π > 0 under the rolling scheme, the statistics are asymptotically standard normal for π > 0 under the fixed scheme.”
Can Managers Forecast Aggregate Market Returns (2005, methodology): “3 Following McCracken (2000) and White (2000), to measure the forecasting ability of each model, we calculate the out-of-sample forecast error of each model using the following equations: c Et 0,t-1 1,t-1 t-1Rf Sβ β = − − (4) and ' u Et 0,t-1Rf β = − (5) where fc and fu are the out-of-sample…”
Inference on Predictability of Foreign Exchange Rates via Generalized Spectrum and Nonlinear Time Series Models (2003, background): “For more discussion on rolling estimation, see West (1996) and McCracken (2000). Our main aim is to investigate out-of-sample forecasts of the models described in Table 5, relative to the martingale model.”
Statistically significant forecasting improvements: how much out-of-sample data is likely necessary? ☆ (2003, methodology): “The attractiveness of this 2West (1996), West and McCracken (1998), Stock and Watson (1999), McCracken (2000), and Chao, Corradi and Swanson (2000) provide alternative asymptotically justified tests.”
Detecting and Predicting Forecast Breakdowns (2009, methodology): “(c) [vec (ltβ)0 , h0t]0 is covariance (1)See also McCracken (2000) for weaker assumptions.”
Forecasting Euro Area Inflation: Does Aggregating Forecasts by Hicp Component Improve Forecast Accuracy? (2003, unknown): “West (1996) and McCracken (2000) analyse the effects of parameter estimation uncertainty on tests of equal forecast accuracy for non-nested models. According to McCracken (2004) estimation uncertainty can be ignored when the parameters are estimated consistently and the forecast is evaluated by MSFE”
Backtesting Parametric Value-at-Risk With Estimation Risk (2010, methodology): “…is difficult, but different theories based on heteroscedasticity and autocorrelation consistent (HAC) estimations using mixing conditions (see McCracken 2000) or bootstrap methods for correct inference (see, e.g., Corradi and Swanson 2007) are attractive alternatives that deserve further…”
Asymptotics for out of sample tests of Granger causality (2007, methodology): “We also exclude applications like those in Diebold and Nason (1990), Swanson and White (1997) and McCracken (2000b) who use local-regression, series-based and kernel-based nonparametric methods respectively to construct forecasts.”
ROBUST BACKTESTING TESTS FOR VALUE-AT-RISK MODELS (2011, methodology): “Our paper draws on the literature on out-of-sample predictive ability in e.g. West (1996), and more closely in McCracken (2000), adapting their general methods to the specific case of backtesting tests.”
Change-Point Monitoring in Linear Models (2006, unknown): “We will further illustrate the behaviour of the monitoring schemes by simulations based on a part of the data set compiled by McCracken (2000). Our goal is to come closer to a real world application, in which the assumptions of our theory are not perfectly satisfied.”
Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management (2004, unknown): “McCracken (2000) extends West’s framework to allow for non-differentiable loss functions in a regression set-up.”
Statistical tests for multiple forecast comparison (2012, background): “5 For related results, see also McCracken (2000) and Corradi et al. (2001).”