*! version 1.0.1 06Jun2017

*! version 1.1.1 20Jul2020

help prodest_predict also see: prodest

Title

prodest predict -- Postestimation tool for prodest

Syntax for predict

predict [newvar] [if] [in] [, residuals exponential parameters omega markups inputvar(varname) corrected]

options

TFP
residuals stores the residuals (y_it - hat_it) in newvar

exponential stores the exponentiated residuals in newvar

parameters returns the estimated input elasticities

omega stores the estimated log(TFP) (phi_it - hat) in newvar. Only available after prodest, fsresidual(newvar)

Markups
markups stores the estimated markups as in De Loecker & Warzynski (AER, 2012) in newvar

* inputvar(varname) variable input for markups estimation. Mandatory when running markups

corrected markups correction for first-stage residuals. Only available after prodest, fsresidual(newvar)

Options for predict - TFP

Cobb-Douglas PF

residuals: log(residuals) values calculated from the log production function as:
omega_it + epsilon_it = y_it - beta_ * l - beta_ * k

exponential: calculated as the exponential of the residuals from the log production function as:
exp(omega_it + epsilon_it) = exp(y_it - beta_ * l - beta_ * k)

parameters: The estimated parameters for free, state and control variables.

omega Omega calculated as in De Loecker & Warzynski: omega_it = phi_it - hat. Only available after prodest, fsresidual(newvar) .

Translog PF

residuals log(TFP) values calculated from the log production function as:
omega_it = y_it - beta_ * l - beta_ * k - beta_ * l^2 - beta_ * k^2 - beta_ * (l * k)

exponentials TFP values calculated as the exponential of the residuals from the log production function as:
exp(omega_it) = exp(y_it - beta_ * l - beta_ * k - beta_ * l^2 - beta_ * k^2 - beta_ * (l * k))

parameters The estimated elasticities for free and state variables. With translog PF, the elasticities (beta) are defined as:
hat(beta)_ = E( beta_ + 2 beta_ * l + beta_ * k )
hat(beta)_ = E( beta_ + 2 beta_ * k + beta_ * l )

omega Omega calculated as in De Loecker & Warzynski: omega_it = phi_it - f(l,k,beta). Only available after prodest, fsresidual(newvar).

Options for predict - markups

markups: markups calculated as in De Loecker & Warzynski (AER, 2012), for each input variable l: markups_ = theta_ * (alpha_)^-1
where theta_ = beta_ for Cobb-Douglas PFs, theta_ = beta_ + 2 beta_ * l + beta_ * k for Translog PFs
and alpha_ = L / Y is the l input share.

inputvar(varname) the l input variable on which to compute markups. It is mandatory when when running prodest predict
with markups, and it must match one of the free variables previously specified

corrected: Correction of alpha_ which accounts for the inclusion of first-stage residuals in output observation.
markups_corrected_ = theta_ * ( alpha_ * exp(epsilon) )^-1 . Only available after prodest, fsresidual(newvar).

Examples

. insheet using "https://raw.githubusercontent.com/GabrieleRovigatti/prodest/master/stata/data/prodest.csv", names clear}

(load data)

. xtset id year, y /* not run */

Levinsohn and Petrin method

. prodest log_y, free(log_lab1 log_lab2) state(log_k) proxy(log_materials) va met(lp) opt(dfp) reps(50) id(id) t(year) fsresiduals(fs_lp)

(click to run)

. predict lpfit, residuals

(click to run)

Ackerberg, Caves and Frazer with translog

. prodest log_y, free(log_lab1 log_lab2) state(log_k) proxy(log_materials) va met(lp) acf reps(20) id(id) t(year) translog

(click to run)

. predict, parameters

(click to run)

Authors

Gabriele Rovigatti

Bank of Italy

Rome, Italy

gabriele.rovigatti@gmail.com

Vincenzo Mollisi

University of Rome Tor Vergata

Rome, Italy

vincenzo.mollisi@gmail.com

Also see

Online: opreg, levpet, acfest

Yasar, M., Raciborski, R. and Poi, B., 2008.
Production function estimation in Stata using the Olley and Pakes method.
The Stata Journal, 8, pp. 221-231.

Petrin, A., Poi, B. and Levinsohn, J., 2004.
Production function estimation in Stata using inputs to control for unobservables.
The Stata Journal, 4, pp. 113-123.

Manjon, M. and Manez, J., 2016.
Production function estimation in Stata using the Ackerberg-Caves-Frazer method.
The Stata Journal, 16, pp. 900-916.

Rovigatti, G., and Mollisi, V., 2018.
Theory and practice of total-factor productivity estimation: The control function approach using Stata.
The Stata Journal, 18(3), pp. 618-662.

Rovigatti, G., 2020.
Markup estimation using Stata: the Micro and the Macro approaches.
Working Paper

Bug Reporting

prodest is part of an ongoing project. Thus, it might contain errors and malfunctions. Please submit bugs, comments and suggestions via email to:

gabriele.rovigatti@gmail.com

Please try to include steps to reproduce issues and possibly the output of the Stata command -creturn list-.