weights_beta: Compute Beta weighting curves

In sentometrics: An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction

Compute Beta weighting curves

Description

Computes Beta weighting curves as in Ghysels, Sinko and Valkanov (2007). Handy to self-select specific time aggregation weighting schemes for input in ctr_agg using the weights argument.

Usage

weights_beta(n, a = 1:4, b = 1:4, do.normalize = TRUE)

Arguments

n	a single numeric to indicate the lag length (cf., n).
a	a numeric as the first parameter (cf., a).
b	a numeric as the second parameter (cf., b).
do.normalize	a logical, if TRUE weights are normalized to unity.

Details

The Beta weighting abides by following formula: f(i/n; a, b) / \sum_{i}(i/n; a, b), where i is the lag index ordered from 1 to n, a and b are two decay parameters, and f(x; a, b) = (x^{a - 1}(1 - x)^{b - 1}\Gamma(a + b)) / (\Gamma(a)\Gamma(b)), where \Gamma(.) is the gamma function.

Value

A data.frame of beta weighting curves per combination of a and b. If n = 1, all weights are set to 1.

References

Ghysels, Sinko and Valkanov (2007). MIDAS regressions: Further results and new directions. Econometric Reviews 26, 53-90, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/07474930600972467")}.

See Also

ctr_agg

sentometrics documentation built on April 3, 2025, 6:15 p.m.