@quantile |

Empirical quantile.

Compute the quantile value where approximately 100*q percent of the data is less than or equal to the value,

Syntax: @quantile(x, q[, m, s])

x: series, vector, matrix

q: number, series, vector, matrix

m: (optional) string

s: (optional) sample string or object when x is a series and assigning to a series

Return: number

• The quantile value q must satisfy .

• m is an optional string controlling the method of calculating the empirical distribution function: “b” (Blom), “r” (Rankit-Cleveland), “o” (Ordinary), “t” (Tukey), “v” (van der Waerden), “g” (Gumbel). The default value is “r”.

Rankit-Cleveland (default) | |

Ordinary | |

Van der Waerden | |

Blom | |

Tukey | |

Gumbel |

To compute the -quantile, first find , the smallest rank such that,

where the order statistics represent data for the observations ordered from low to high, and is the assumed empirical distribution function. For purposes of computing , tied ranks are assumed to take the last tied value.

Then the quantile is computed as

where the interpolating constant is

for the smallest integer where . In the leading case where there are no tied values, .

For series calculations, EViews will use the current or specified workfile sample.

Examples

= @quantile(x, 0.5)

returns the median of the series x.

= @quantile(x, 0.1)

returns the first decile (10th percentile) of the series x.

Cross-references

See also
@pctiles.