eff_nopar: Compute direct, indirect and total effects functions for...

In rominsal/sptpsar: Spatio-Temporal Semiparametric Models with Spatial Lags

Compute direct, indirect and total effects functions for continous non-parametric covariates in PS-SAR regression models.

Description

Compute direct, indirect and total effect functions for non-parametric covariates included in a semiparametric spatial or spatio-temporal SAR model. This model must include a spatial lag of the dependent variable (SAR) to have indirect effects different from 0, otherwise, total and direct function effects are the same.

Usage

eff_nopar(sptsarfit, variables, conflevel = 0.95)

Arguments

sptsarfit	psar object fitted using psar function.
variables	vector including names of non-parametric covariates.
conflevel	numerical value for the confidence interval of the effect functions. Default 0.95.

Details

DESCRIBE ALGORITHM TO COMPUTE EFFECT FUNCTIONS AND THE SMOOTHING TO PLOT

Value

A list including

effnopar_tot	Matrix including total effects in columns.
effnopar_dir	Matrix including direct effects in columns.
effnopar_ind	Matrix including indirect effects in columns.
effnopar_tot_up	Matrix including upper bounds of total effects in columns.
effnopar_dir_up	Matrix including upper bounds of direct effects in columns.
effnopar_ind_up	Matrix including upper bounds of indirect effects in columns.
effnopar_tot_low	Matrix including lower bounds of total effects in columns.
effnopar_dir_low	Matrix including lower bounds of direct effects in columns.
effnopar_ind_low	Matrix including lower bounds of indirect effects in columns.

Author(s)

Roman Minguez [e-mail]

References

• Basile, R., Durbán, M., Mínguez, R., Montero, J. M., and Mur, J. (2014). Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities. Journal of Economic Dynamics and Control, (48), 229-245.

• LeSage, J. and Pace, K. (2009). Introduction to Spatial Econometrics. CRC Press, Boca Raton.

• Mínguez, R.; Basile, R. and Durbán, M. (2018). An Alternative Semiparametric Model for Spatial Panel Data. Under evaluation in Statistical Methods and Applications.

See Also

• psar estimate spatial or spatio-temporal semiparametric PS-SAR regression models.

• eff_par compute and simulate total, direct and indirect effect (or impacts) for parametric continuous covariates.

• fit_terms compute terms for smooth functions for non-parametric continuous covariates and for non-parametric trends.

• plot_effects_nopar plot the non-parametric effects functions allowing for previous smoothing.

Other Direct, Indirect and Total Effects.: eff_par, plot_eff_nopar

Examples

################################################
 ###################### Examples using a panel data of rate of
 ###################### unemployment for 103 Italian provinces in period 1996-2014.
library(sptpsar)
data(unemp_it); Wsp <- Wsp_it

######################  No Spatial Trend: PSAR including a spatial 
######################  lag of the dependent variable
form1 <- unrate ~ partrate + agri + cons +
                 pspl(serv,nknots=15) +
                 pspl(empgrowth,nknots=20) 
 gamsar <- psar(form1,data=unemp_it,sar=TRUE,Wsp=Wsp_it)
 summary(gamsar)
 ###### Non-Parametric Total, Direct and Indirect Effects
 list_varnopar <- c("serv","empgrowth")
 eff_nparvar <- eff_nopar(gamsar,list_varnopar)
 plot_effects_nopar(eff_nparvar,unemp_it,smooth=TRUE)
 plot_effects_nopar(eff_nparvar,unemp_it,smooth=FALSE)
 
######################   PSAR-ANOVA with spatial trend
form2 <- unrate ~ partrate + agri + cons +
                  pspl(serv,nknots=15) + pspl(empgrowth,nknots=20) +
                  pspt(long,lat,nknots=c(20,20),psanova=TRUE,
                  nest_sp1=c(1,2),nest_sp2=c(1,2))
##### Spatial trend fixed for period 1996-2014
geospanova_sar <- psar(form2,data=unemp_it,Wsp=Wsp_it,sar=TRUE,
                   control=list(thr=1e-1,maxit=200,trace=FALSE))
summary(geospanova_sar)
 ###### Non-Parametric Total, Direct and Indirect Effects
 list_varnopar <- c("serv","empgrowth")
 eff_nparvar <- eff_nopar(geospanova_sar,list_varnopar)
 plot_effects_nopar(eff_nparvar,unemp_it,smooth=TRUE)
 plot_effects_nopar(eff_nparvar,unemp_it,smooth=FALSE)
 
######################   PSAR-ANOVA with spatio-temporal trend and 
######################   temporal autorregresive noise
 form3 <- unrate ~ partrate + agri + cons +
                   pspl(serv,nknots=15) + pspl(empgrowth,nknots=20) +
                   pspt(long,lat,year,nknots=c(18,18,8),psanova=TRUE,
                   nest_sp1=c(1,2,3),nest_sp2=c(1,2,3),
                   nest_time=c(1,2,2),ntime=19)
sptanova_sar_ar1 <- psar(form3,data=unemp_it,Wsp=Wsp_it,sar=TRUE,ar1=TRUE,
                    control=list(thr=1e-1,maxit=200,trace=FALSE))
summary(sptanova_sar_ar1)
 ###### Non-Parametric Total, Direct and Indirect Effects
 list_varnopar <- c("serv","empgrowth")
 eff_nparvar <- eff_nopar(sptanova_sar_ar1,list_varnopar)
 plot_effects_nopar(eff_nparvar,unemp_it,smooth=TRUE)
 plot_effects_nopar(eff_nparvar,unemp_it,smooth=FALSE)

rominsal/sptpsar documentation built on June 1, 2022, 2:03 a.m.