.png){kind=icon}
.png){kind=icon}
Return
Economic forecasting
.png){kind=icon}
PRE
AI
DOI：10.1257/jel.46.1.3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context Of such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives. Decision theory provides a framework for both the construction and evaluation of forecasts. This framework allows an understanding of the challenges that arise from the explosion in the sheer volume of predictor variables under consideration and the forecaster's ability to entertain an endless array of forecasting models and time-varying specifications, none of which may coincide with the true model. We show this along with reviewing methods for comparing the forecasting performance of pairs Of models or evaluating the ability of the best of many models to beat a benchmark specification.
Keywords:
MACROECONOMIC TIME-SERIES
LINEAR-MODELS
ERROR-CORRECTION
EXCHANGE-RATES
RANDOM-WALKS
TESTS
RATIONALITY
SELECTION
PERFORMANCE
PREDICTION
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.6
Papers:
689
Citations:
1.3W
