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PointFore  

Interpretation of Point Forecasts as State-Dependent Quantiles and Expectiles
View on CRAN: Click here


Download and install PointFore package within the R console
Install from CRAN:
install.packages("PointFore")

Install from Github:
library("remotes")
install_github("cran/PointFore")

Install by package version:
library("remotes")
install_version("PointFore", "0.2.0")



Attach the package and use:
library("PointFore")
Maintained by
Patrick Schmidt
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-22
Latest Update: 2019-02-22
Description:
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast. Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible. The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the European Centre for Medium-Range Weather Forecasts (ECMWF, ) and GDP growth Greenbook data by the US Federal Reserve. See Schmidt, Katzfuss and Gneiting (2015) for more details on the identification and estimation of a directive behind a point forecast.
How to cite:
Patrick Schmidt (2019). PointFore: Interpretation of Point Forecasts as State-Dependent Quantiles and Expectiles. R package version 0.2.0, https://cran.r-project.org/web/packages/PointFore. Accessed 21 Nov. 2024.
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Complete documentation for PointFore
Functions, R codes and Examples using the PointFore R package
Some associated functions: GDP . PointFore . constant . estimate.functional . expectiles . lag . logistic_linear . plot.pointfore . precipitation . probit_break . probit_linear . probit_spline2 . probit_spline3 . quantiles . summary.pointfore . 
Some associated R codes: PointFore.R . data.R . dependencies.R . estimate_functional.R . plot.R .  Full PointFore package functions and examples
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