gnrprod {gnrprod}

Estimate production functions and productivity: Gandhi, Navarro, and Rivers (2020)

Description

The gnrprod function is the front end of the gnrprod package. It estimates production functions and productivity in two stages: gnrflex (estimate flexible input elasticity) and gnriv (estimate fixed input elasticities and productivity). If the production-related inputs are characters, a data.frame must be specified under data. Alternatively, matrices/vectors may be directly specified without specifying data. gnrprod currently supports only one flexible input.

Usage

gnrprod(output, fixed, flex, share, in_price = NULL,
               out_price = NULL, id, time, data, B = NULL,
               fs_control = NULL, ss_control = NULL, ...)

Arguments

output

name (character) of variable of log gross output in data or a numeric vector.

fixed

name (character or character vector) of variables of log fixed inputs in data or a numeric matrix.

flex

name (character) of variable of log flexible input in data or a numeric vector.

share

name (character) of variable of log intermediate input's revenue share in data or a numeric vector.

in_price

optional (required if share is not specified) name (character) of variable of common flexible input price or a numeric vector.

out_price

optional (required if share is not specified) name (character) of variable of common output price or a numeric vector.

id

name (character) of variable of firm ID in data or a numeric vector.

time

name (character) of variable of time in data or a numeric vector.

data

data.frame containing all variables with names specified by arguments above (left empty if arguments above are vector/matrix rather than strings).

B

number of bootstrap repetitions to retrieve standard errors of elasticity estimates. By default, gnrprod does not bootstrap, i.e., B = NULL. Setting B > 1 will output bootstrapped standard errors.

fs_control

an optional list of convergence settings of the first stage. See gnrflex.control for listing.

ss_control

an optional list of convergence settings of the second stage. See gnriv.control for listing.

...

additional optional arguments to be passed to optim in the second stage.

Value

a list of class 'gnr' with five elements:

estimates: a list with two elements: elas the parameter estimates and std_errors the standard errors.

data: a data.frame containing: output, fixed, flex, share, id, time, estimated elasticities for each observation, estimated productivity, and first stage residuals.

first_stage: a list containing five elements describing the share regression (first stage):

• coefficients: a numeric vector of the coefficients of the first stage estimator scaled by a constant. See Gandhi, Navarro, and Rivers (2020, p. 1994, equation (21)).

• SSR: sum of squared residual.

• iterations: number of iterations performed.

• convergence: boolean indicating whether convergence was achieved.

• control: list of convergence control parameters (see gnrflex.control).

second_stage: a list containing four elements describing the second stage:

• optim_method: the method for optimization. Defaults to 'BFGS'. See optim for a listing of available methods.

• optim_info: the returned list of the optim function estimating the coefficients of the constant of integration. See Gandhi, Navarro, and Rivers (2020, p. 1994, equation (21)).

• optim_control: the list of control parameters passed to optim.

• degree_w: degree of Markov process for persistent productivity.

• degree_tau: degree of expansion for constant of integration.

call: the function call.

References

Gandhi, Amit, Salvador Navarro, and David Rivers. 2020. "On the Identification of Gross Output Production Functions." Journal of Political Economy, 128(8): 2973-3016. doi:10.1086/707736.

Examples

require(gnrprod)
data <- colombian
industry_311 <- gnrprod(output = "RGO", fixed = c("L", "K"),
                        flex = "RI", share = "share", id = "id",
                        time = "year", data = data,
                        fs_control = list(degree = 2, maxit = 200),
                        ss_control = list(trace = 1))