t_weight: Aggregation over Sectors with a Weighted Average Approach

In dfvad: Diewert and Fox's Method of Value Added Growth Decomposition

Description

This "bottom up" approach uses weighted averages of the sectoral decompositions to provide an approximate decomposition into explanatory components at the aggregate level. Specifically, the Tornqvist index is adopted in the aggregation.

Usage

t_weight(y, p, id, t, alpha, beta, gamma, epsilon, tau, data)

Arguments

y	A string (or a vector of strings) indicating the output quantity columns.
p	A string (or a vector of strings) indicating the output price columns.
id	A string indicating the industry column.
t	A string indicating the time period column.
alpha	A string indicating net output price indexes.
beta	A string indicating input quantity indexes.
gamma	A string indicating input mixe indexes.
epsilon	A string indicating value added efficiency indexes.
tau	A string indicating technical progress indexes.
data	A data frame containing input prices, input quantities, industry identities, the time period, and explanatory factors of value added growth.

Value

A list containing a growth-value table and a level-value table of explanatory factors for value added growth decomposition. It is sorted by the time period.

References

Diewert, W. E. and Fox, K. J. (2018). Decomposing value added growth into explanatory factors. In The Oxford Handbook of Productivity Analysis, chapter 19, page 625–662. Oxford University Press: New York.

Examples

# Use the built-in dataset "sector"
table1 <- t_weight("y", "p", "industry", "year", "alpha",
        "beta", "gamma", "epsilon", "tau", sector)[[1]]
table2 <- t_weight("y", "p", "industry", "year", "alpha",
        "beta", "gamma", "epsilon", "tau", sector)[[2]]

dfvad documentation built on Oct. 15, 2021, 5:16 p.m.