Description Usage Arguments Details Value References See Also Examples

Computes (time-varying) dispersion measures for the cross section of individual model forecasts that are the input of forecast combination.

1 | ```
cs_dispersion(x, measure = "SD", plot = FALSE)
``` |

`x` |
An object of class |

`measure` |
Cross-sectional dispersion measure, one of: |

`plot` |
logical. If |

The available measures of scale are defined as in Davison (2003). Let *y_(i)* denote the i-th order statistic of the sample, then:

*Range_t = y_{(n), t} - y_{(1), t}*

*IQR_t = y_{(3n/4),t} - y_{(n/4),t}*

*SD_t = sqrt(1/(n-1) Σ_{i=1}^n (y_{i,t} - \bar{y}_t))*

Previous research in the forecast combination literature has documented that regression-based combination methods tend to have relative advantage when one or more individual model forecasts are better than the rest, while eigenvector-based methods tend to have relative advantage when individual model forecasts are in the same ball park.

Returns a vector of the evolution of cross-sectional dispersion over the sample period (using the selected dispersion measure)

Davison, A. C. (2003). Statistical Models. *Cambridge University Press*.

Hsiao, C., and Wan, S. K. (2014). Is There An Optimal Forecast Combination? *Journal of Econometrics*, **178(2)**, 294–309.

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GeomComb documentation built on May 1, 2019, 8:06 p.m.

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