ROBUST FORECAST COMPARISON
نویسندگان
چکیده
منابع مشابه
Robust Forecast Comparison*
Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of g...
متن کاملVolatility forecast comparison using imperfect volatility proxies
The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We motivate our study with analytical results on the distortions caused by some widely-used loss functions, when used with standard volatility proxies such as squared returns, the intra-daily range or realised volatility. We then...
متن کاملRobust forecast methods and monitoring during structural change
We examine how to forecast after a recent break. We consider a new approach, monitoring for change and then combining forecasts from two models, one using the full sample and the other solely data from after the identified break point. We compare this to some robust techniques: rolling regressions, forecast averaging over all possible windows and exponentially weighted forecasts. We examine the...
متن کاملForecast comparison of principal component regression and principal covariate regression
Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. This paper compares two methods for dealing with many predictors, that is, principal component regression (PCR) and principal covariate regression (PCovR). The forecast performance of these methods is compared by simulating data from factor models and from regression models. The simulations show that, ...
متن کاملComparison of multilabel classification models to forecast project dispute resolutions
Early forecasting of project dispute resolutions (PDRs) provides decision-support information for resolving potential procurement problems before a dispute occurs. This study compares the performances of classification and ensemble models for predicting dispute handling methods in public–private partnership (PPP) projects. Model analyses use machine learners (i.e., Support Vector Machines (SVMs...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Econometric Theory
سال: 2016
ISSN: 0266-4666,1469-4360
DOI: 10.1017/s0266466616000426