On the Selection of Forecasting Models

By A. Inoue, Lutz Kilian. 2006

197 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7e70257b4144b2ed3c9ad87d52b214d09413afff

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Citations in the corpus

FORECAST EVALUATION (background): “See recent work by Chen (2004), Clark and McCracken (2005b) and Inoue and Kilian (2004a, 2004b) for different takes on the possible power advantages of using out of sample tests.”
Forecast comparisons in unstable environments (2010, background): “…to select the model with the best global forecasting performance, which in practice amounts to selecting the model that forecasts best on average over the in-sample sample period or over the (simulated) out-of-sample period (see, e.g. Rissanen, 1982; Wei, 1992; and Inoue and Kilian, 2006).”
Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests (2015, unknown): “This point was first made and elaborated upon by Inoue and Kilian (2004) and Inoue and Kilian (2006).”
A Simple Parametric Model Selection Test (2014, background): “One strand of the literature (Nishii 1988; Vuong 1989; Sin andWhite 1996; Inoue and Kilian 2006 among others) uses this criterion together with earlier ideas about embedding the model selection problem into a classical hypothesis testing framework (e.g., Hotelling 1940; Chow 1980).”
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].”
Averaging estimators for autoregressions with a near unit root (2010, background): “Using local-to-unity asymptotic methods, we found that two measures of risk of a variety of estimators are functions only of the local- to-unity parameter, facilitating direct comparisons.”
Out-of-Sample Predictions of Bond Excess Returns and Forward Rates: An Asset Allocation Perspective (2010, unknown): “literature on portfolio choice under parameter uncertainty, we follow Kandel and Stambaugh (1996) and Connor (1997), who recommend imposing an informative prior to dene the distribution of the parameter estimates used to carry out the asset allocation problem.”
ARE EXCHANGE RATES REALLY RANDOM WALKS? SOME EVIDENCE ROBUST TO PARAMETER INSTABILITY (2005, background): “Another related paper is Inoue and Kilian (2002) ,”
What Central Bankers Need to Know about Forecasting Oil Prices (2014, background): “Although the common notion that rolling VAR forecasts protect the forecaster against future structural changes is not correct, as demonstrated in Inoue and Kilian (2006), it is useful to investigate whether rolling VAR forecasts would have improved on the MSPE of recursive VAR forecasts.”
Forecasting the Price of Oil (2010, background): “It has been shown that the presence of structural breaks at unknown points in the future invalidates the use of forecasting model rankings obtained in forecast accuracy comparisons whether one uses rolling or recursive regression forecasts (see Inoue and Kilian 2006).”
Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes (2007, unknown): “(iv) the parametric bootstrap of Kilian (1999), applied in the spirit of McCracken and Sapp (MS: 2004). As in MS, application of the parametric bootstrap begins with the estimation of a VAR model for xt and yt (with lags selected using the Schwarz Information Criterion (SIC)) and resample the residu”