Hypernormal Densities
Hypernormal Densities
We propose a new family of density function that posses both flexibility and closed form expressions for moments and anti-derivatives, making them particularly appealing for applications. We illustrate its usefulness by applying our new family to obtain density forecasts of U.S. inflation. Our methods generate forecasts that improve on standard methods based on AR-ARCH models relying on normal or Student's t-distributional assumptions.
- Institute for the Study of Labor Germany
- University of California, San Diego United States
- University of California, Los Angeles United States
- University College London United Kingdom
- University of California United States
forecast accuracy, neural networks, nonparametric density estimation, ARMA-GARCH models, neural networks, nonparametric density estimation, forecast accuracy, Statistics, Econometrics and Quantitative Methods, arma-garch models, ARMA-GARCH Models, neural networks, nonparametric density estimation, forecast accuracy, ARMA-GARCH Models, jel: jel:C63, jel: jel:C53, jel: jel:C45
forecast accuracy, neural networks, nonparametric density estimation, ARMA-GARCH models, neural networks, nonparametric density estimation, forecast accuracy, Statistics, Econometrics and Quantitative Methods, arma-garch models, ARMA-GARCH Models, neural networks, nonparametric density estimation, forecast accuracy, ARMA-GARCH Models, jel: jel:C63, jel: jel:C53, jel: jel:C45
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