Now the uSEM model result is in the object “model.fit”, including beta matrix, psi matrix, and fit statistics. # lhs op rhs mi epc sepc.lv sepc.all sepc.nox lhs.number
#EVIEWS 9 BOOTSTRAP IMPULSE RESPONSE CODE#
Var.number <- p # number of variables lag.order <- 1 # lag order of the model model.fit <- uSEM(var.number, ries, lag.order, # verbose = FALSE, #published code verbose = TRUE, #test trim = TRUE) # "Iteration: 1"
![eviews 9 bootstrap impulse response eviews 9 bootstrap impulse response](https://3.bp.blogspot.com/-9AqwC0O0FGo/V2wvWjpVYfI/AAAAAAAAALI/Q7bsZn47g-kJvSKGrPlcokGlmrhfh3ffQCKgB/s1600/localirfs05.png)
# Warning: Removed 1 row(s) containing missing values (geom_path). ries $time <- seq( 1, length(ries), 1) <- melt(ries, id= "time") # convert to long format ggplot( data=, aes( x=time, y=value, colour=variable)) + geom_line() + facet_wrap( ~ variable, ncol = 1) + scale_y_continuous( breaks = seq( 0, 100, by = 50)) + theme( strip.background = element_blank(), panel.background = element_blank(), legend.title = element_blank(), # = element_blank(), # = element_blank(), # = element_text(), # = element_blank() legend.key = element_blank(), legend.position = "none", # legend.title = element_blank(), # panel.background = element_blank(), = element_text( color= "black", size= 12), = element_text( color= "black", size= 12), = element_text( color= "black", size= 12), = element_text( color= "black", size= 12), axis.line = element_line( color = 'black') ) + ylim( - 1, 1) + xlab( "Time") + ylab( "Value") # Scale for 'y' is already present.