yukai-yang / pstr Goto Github PK

Function
ret = plot_response(obj=pstr, vars=1:4, log_scale = c(F,T), length.out=100)
in example of using return error:

Error in if (varname != tvarname) { :
missing value where TRUE/FALSE needed

I've tried R versions down to 3.4.0 and the installation does not work.

Hello!
I have problems running the package in R version 4.0.3.
Will they have an updated version of the code?

Thanks!

Hi Yukai,
Just out of curiosity, does this package allow for endogenous regressors? Let's say for example in the investment-cash flow volatility regression, what if investment level somehow reversely affecting cash flow vol? (e.g., low investment could lead to lower stock prices, causing financial constrains etc. and thus making cash flow more volatile in the future).
A possible solution is adopting an instrumental variable approach for the PSTR model, as outlined in section 6 in the paper by Fouquau et al. here: http://www.univ-orleans.fr/deg/masters/ESA/CH/FH.pdf (the published version is at https://www.sciencedirect.com/science/article/pii/S0264999307000831).

Regards,
Long

Error in if (class(invS1) == "try-error") { : the condition has length > 1
R:4.2.0.

Error output
Error: The PSTR model is not estimated yet.

To Reproduce
I'll make this relevant to the handbook, so please run:

1. pstr = NewPSTR(Hansen99, dep='inva', indep=4:20, indep_k=c('vala','debta','cfa','sales'), tvars=c('vala'), iT=14)
2. pstr = EstPSTR(use=pstr)
3. plot_coefficients(pstr)

-> Error comes out.

Additional information

1. When estimating, this warning pops up:

Warning message:
In if (class(ttmp) == "try-error") { :
  the condition has length > 1 and only the first element will be used

2. The same error comes up for plot_coefficients(pstr) and other pertinent commands, but not for print(pstr, "estimates")

Software:

• R version: 4.0.3
• PSTR version: 1.2.4
• Windows 10

Hi Yukai,
First of all thank you for the hard work on this package. I've been looking for a way to perform PSTR in R since forever. Recently I came across Gongalez refined paper mentioning your package and am so glad to find this.
I want to suggest a new feature for the package. When employing the two regimes, single transition function PSTR model with two variables (from here, p.7):

with a logistic transition function with a single location parameter (m=1):
,
it can be shown that the elasticity of I w.r.t S is both cross-sectional and time-variant (you show this when you compute your "response" measures):
.
And I think it would be intuitive to extract these varying coefficients, for all individuals over time, because they show the dynamic of the interested relationships and complement the plot of transition functions.

Let say we applies this to the case of Hansen's data (4 vars instead of 2, but the above formulae applies). We would like to investigate the impact of debt level on investment, conditional on the transition variable selected to be Tobin's Q. Let's fit the model first:

pstr = NewPSTR(Hansen99, dep='inva', indep=4:20, indep_k=c('vala','debta','cfa','sales'),tvars=c('vala'), iT=14); model = EstPSTR(use=pstr, im=1, iq=1, useDelta=TRUE, par=c(1.6,.5), method='CG'); model$est[14:17] ; model$mbeta

Then compute the time varying coefficients, as well as extract Tobin's Q levels for each of the first three firms in the sample

id<-unique(Hansen99$cusip); tvc<-NULL; vala<-NULL; for (i in 1:3){ vala_i<-Hansen99$vala[Hansen99$cusip==id[i]] ; g<-(1+exp(-model$gamma*(vala_i-model$c)))^(-1) ; tvc_i<-model$est[14:17][2] + model$mbeta[2]*g ; tvc<-cbind(tvc,tvc_i); vala<-cbind(vala,vala_i); colnames(tvc)[i]=colnames(vala)[i]=id[i] }

Finally plot these times series side by side:

par(mfcol=c(1,2)); matplot(tvc, type = 'l', lwd=2,col = 1:3, xaxt='n', xlab='year',ylab='coefficient',main='Time varying impact of debt level on investment'); axis(1, at=1:nrow(tvc), labels=c(1974:1987)); legend("topleft", legend = paste('firm_',colnames(tvc),sep=''), col=1:3, lwd=2, cex=1.1); matplot(vala, type = 'l', lwd=2,col = 1:3, xaxt = 'n', xlab='year',ylab="Tobin's Q",main="Tobin's Q level"); axis(1, at=1:nrow(tvc), labels=c(1974:1987)); legend("topleft", legend = paste('firm_',colnames(vala),sep=''), col=1:3, lwd=2, cex=1.1)

We can see that the elasticity of investment w.r.t debt changes over time, and depends on the level of Q: The higher the Q (firms with more investment opportunities), the stronger the impact. In particular, the firm with highest Q (2824) (green curve, right plot) exhibits the most stable the relationship (green curve, left plot). These are the conclusions you made at the end of the package introduction.

There is a caveat though: If the transition variable is the same as the independent variable (or a function of it), the computation of the elasticity gets a bit more complicated. In general, for a model with r transition functions (r+1 regimes) we have:
.
That means the elasticity of investment w.r.t Tobin's Q need to be computed differently. But I think this could also be incorporated into the new feature.

Hopefully my suggestions make sense!
Thanks,

Long

hello,
I just come across a problem when installing the package of version 1.2.4.

• installing source package 'PSTR' ...
  ** R
  Error : (由警告转换成)无法重新编码'PSTR.R'行41
  ERROR: unable to collate and parse R files for package 'PSTR'
• removing 'D:/R/R-3.5.3/library/PSTR'
  In R CMD INSTALL
  Error in i.p(...) :
  (converted from warning) installation of package ‘C:/Users/Xiangtuo/AppData/Local/Temp/RtmpyaO9AV/file3d10d956938/PSTR_1.2.4.tar.gz’ had non-zero exit status

could you help me to have a look?

best regards