This paper derives a formula for the optimal forecast of a discounted sum of future values of a random variable, where optimal is defined in terms of the minimized H-norm of the forecast error. This problem reflects a preference for robustness in the presence of (unstructured) model uncertainty. The paper shows that revisions of a robust forecast are more sensitive to new information, and discusses the relevance of this result to previous findings of excess sensitivity of consumption and asset prices to new information. Ó 2001 Elsevie
This study comprehensively investigates the uncertainty on parameter instability and model selection...
The question we address is how robust solutions react to changes in the uncertainty set. We prove th...
We study stochastic programs where the decision maker cannot observe the distribution of the exogeno...
This presentation summarizes the results of the author on development of the theory of robust stati...
We consider forecasting in systems whose underlying laws are uncertain, while contextual information...
© Springer International Publishing Switzerland 2013 This work is subject to copyright. All rights ...
The problems of robustness in Bayesian forecasting are considered under distortions of the hypothet...
Evaluation of forecast optimality in economics and finance has almost exclusively been conducted und...
This paper proposes forecast optimality tests that can be used in unstable environments. They includ...
This thesis investigates optimality of heuristic forecasting. According to Goldstein a Gigerenzer (2...
Data underlie understanding of processes and prediction of the future. However, things change; data ...
We investigate alternative robust approaches to forecasting, using a new class of robust devices, co...
A mathematical model can be analysed to construct policies for action that are close to optimal for ...
A robust minimax approach for optimal investment decisions with imprecise return forecasts and risk ...
This paper uses a permanent income model as a laboratory to study how consumption /savings profiles ...
This study comprehensively investigates the uncertainty on parameter instability and model selection...
The question we address is how robust solutions react to changes in the uncertainty set. We prove th...
We study stochastic programs where the decision maker cannot observe the distribution of the exogeno...
This presentation summarizes the results of the author on development of the theory of robust stati...
We consider forecasting in systems whose underlying laws are uncertain, while contextual information...
© Springer International Publishing Switzerland 2013 This work is subject to copyright. All rights ...
The problems of robustness in Bayesian forecasting are considered under distortions of the hypothet...
Evaluation of forecast optimality in economics and finance has almost exclusively been conducted und...
This paper proposes forecast optimality tests that can be used in unstable environments. They includ...
This thesis investigates optimality of heuristic forecasting. According to Goldstein a Gigerenzer (2...
Data underlie understanding of processes and prediction of the future. However, things change; data ...
We investigate alternative robust approaches to forecasting, using a new class of robust devices, co...
A mathematical model can be analysed to construct policies for action that are close to optimal for ...
A robust minimax approach for optimal investment decisions with imprecise return forecasts and risk ...
This paper uses a permanent income model as a laboratory to study how consumption /savings profiles ...
This study comprehensively investigates the uncertainty on parameter instability and model selection...
The question we address is how robust solutions react to changes in the uncertainty set. We prove th...
We study stochastic programs where the decision maker cannot observe the distribution of the exogeno...