Conditional Forecasting Task
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A Conditional Forecasting Task is a multi-predictor forecasting task with a sequential mixed multi-predictor dataset.
- Context:
- It can be solved by a Conditional Forecasting System (that implements a Conditional Forecasting Algorithm.
- …
- Example(s):
- Counter-Example(s):
- See: Conditional Classification Task.
References
2012
- (Kunst, 2012) ⇒ Robert M. Kunst. (2012). “Econometric Forecasting - Conditional forecasting." Lecture Notes.
- Economists are often interested in forecasts for [math]\displaystyle{ x_{t+h} }[/math] that assume [math]\displaystyle{ x_s, s \le t }[/math] as known as well as the values of other variables [math]\displaystyle{ y_s, s \le t + h }[/math]. The solution appears to be [math]\displaystyle{ E\left(x_{t+h} | x^t_{-\infty} \cup y^{t+h}_{-\infty}\right) }[/math].
- The assumed values [math]\displaystyle{ y_{t+1},..., y_{t+h} }[/math] may be incorrect;
- Any dynamic model that views [math]\displaystyle{ x_t }[/math] as a function of [math]\displaystyle{ x^{t-1}_{-\infty} }[/math] and [math]\displaystyle{ y^{t}_{-\infty} }[/math] may miss the reaction of y to past x (feedback problem, open loop, weak and strong exogeneity);
- Changing the generation mechanism for [math]\displaystyle{ y }[/math] relative to the observations may affect the reaction of [math]\displaystyle{ y }[/math] (super exogeneity).
- Economists are often interested in forecasts for [math]\displaystyle{ x_{t+h} }[/math] that assume [math]\displaystyle{ x_s, s \le t }[/math] as known as well as the values of other variables [math]\displaystyle{ y_s, s \le t + h }[/math]. The solution appears to be [math]\displaystyle{ E\left(x_{t+h} | x^t_{-\infty} \cup y^{t+h}_{-\infty}\right) }[/math].
2005
- (Geweke & Whiteman, 2005) ⇒ John Geweke, and Charles Whiteman. (2005). “Chapter 1. Bayesian Forecasting.” In: Handbook of Economic Forecasting, 1. doi:10.1016/S1574-0706(05)01001-3
- ABSTRACT: Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. …