Title

get_pwmse: Execute the PWMSE-based model evaluation, after forming proximity norms with the Command form_norm.

Syntax

Execute the PWMSE-based model evaluation:

get_pwmse
using filename ,
yvar(depvar)
xvar(indepvars)
[trends(varlist)]
unit(varname)
time(varname)
t(#)
train_ratio(#)
num_simulations(#)
[norms(chars)]
h(#)
seed(#)
[quiet]

Option
trends(varlist) Specify additional controls such as time trends.

norms(chars) Choose the specific MSEs to be reported. Options include: N, D1, D2, M1, M2, Y1, Y2 as in Cui, Gafarov, Ghanem, and Kuffner (2024).

quiet If not specified, the program will report the running of each round of simulations.

Description

filename: Declare the dta file that contains the proximity norms (previously obtained using form_norms).

yvar(depvar): Declare the dependent variable in the regression model of interest. Note: this variable will be demeaned as in a FE estimation framework.

xvar(indepvars): Declare the list of explanatory variables in the regression model of interest. Note: all these variables will be demeaned as in a FE estimation framework.

unit(varname): Declare the variable indicating cross-sectional units in the empirical analysis.

time(varname): Declare the time dimension in the empirical analysis. This dimension should be the lowest frequency one as in dim_0() in form_norms.

t(#): Declare the last period of the historical data. All data after this period will be dropped in the model evaluation.

train_ratio(#): Specify the training-to-full sample ratio of the cross-validation procedure. This number must be between 0 and 1.

num_simulations(#): Specify the number of times for cross-validation.

h(#): Specify the tuning parameter h in specifying the weight. This should be an integer number.

seed(#): Declare the seed for replication.

Options

Options

trends(varlist): Specify additional controls such as time trends. This is optional and can be left out if not applied. Note: these variables will NOT be demeaned.

norms(chars): Choose the specific MSEs to be reported. Options include: N, D1, D2, M1, M2, Y1, Y2 as in Cui, Gafarov, Ghanem, and Kuffner (2024). All norms will be reported if not specified.

quiet: If not specified, the program will report the running of each round of simulations.

Additional Notes

- Running this program requires first obtaining the proximity norms for constructing weights.

- Running this program requires pre-loading the data for regressions, with necessary variables for different model specifications.

- MSEs are not comparable across different weights. They are only comparable across different models under the same weight specification.

References

Cui, X., Gafarov, B., Ghanem, D., & Kuffner, T. (2024). "On model selection criteria for climate change impact studies". Journal of Econometrics, 239(1), 105511.