Help with Gauss output please

Good day,

I have tried to replicate Table 8 of the paper "Dynamic panels with threshold effect and endogeneity" by Seo and Shin (2016) Journal of Econometrics Volume 195, Issue 2, December 2016, Pages 169-186.

I obtained the results below but could not match them with those reported in the paper. I am particularly interested in the estimates reported in Table 8 of their paper.

Any help, please?  I do have the code and the data if that helps.

Thanks,

ozey

x = c~Tq~d;    @ set regressors (c=cash flow, Tq=Tobin, d=debt)@

/------ output based on analytic variance formula -----/
proportion of upper regime:   0.1958

estimates and s.e. (threshold~lower regime (lag y,x)~delta (1,lag y,x)

0.3590   0.3875   0.0519  -0.0038  -0.1350   0.4409  -0.0594  -0.0039   0.6580
0.0396   0.0919   0.0387   0.0023   0.0466   0.2601   0.0521   0.0019   0.1322

upper regime estimates and s.e. (lag y,x)

0.8284  -0.0075  -0.0077   0.5229
0.2291   0.0457   0.0033   0.1311

 Long-run parameter estimates and s.e.

0.0519  -0.0038  -0.1350  -0.0075  -0.0077   0.5229
0.0696   0.0033   0.1239   1.3404   0.2641   2.3207

Overidentification J-statistic (p-value)
45.0039   0.0162

/------ in case of averaging -----/
proportion of upper regime:   0.1940

estimates and s.e. (threshold~lower regime (lag y,x)~delta (1,lag y,x)

0.3601   0.3941   0.0592  -0.0034  -0.1215   0.4091  -0.0770  -0.0035   0.6133
0.0426   0.0869   0.0416   0.0021   0.0441   0.2417   0.0491   0.0018   0.1224

 upper regime estimates and s.e. (lag y,x)

0.8032  -0.0179  -0.0069   0.4918
0.2127   0.0440   0.0032   0.1234

 Long-run parameter estimates and s.e.

0.0592  -0.0034  -0.1215  -0.0179  -0.0069   0.4918
0.0721   0.0031   0.1118   1.0912   0.2213   1.5592

Overidentification J-statistic (p-value)
64.0851   0.0001

elapsed time : 15 minutes  56.92 seconds

2 Answers



0



There will be some variation in the output of the code because it uses random numbers in the computations. I ran the code in GAUSS 18 on a Mac and GAUSS 10 on a Linux server and got slightly different results each time.

Then I made two changes which gave me reproducible results.

First change

I set the random number seed, using the rndseed keyword towards the top of the code, like this

new;  format /rd /m1 8,4; output file= invest.out on;
tstart = date;   
load invest;  // Load data and define variables

// Set seed for repeatable random numbers
rndseed 23423;

Second change

When running the code with GAUSS 18, I changed the function rndn to _rndng10 so that GAUSS 10 and GAUSS 18 would use the same random number generator. (Note that GAUSS 18 uses a higher quality random number generator than GAUSS 10 and _rndng10 should be used only for comparing results between versions.)

With these changes, I get this output

no constant
threshold variable = c
qn~ns~jm~jn~t0 : 200.0000 200.0000   1.0000   1.0000   4.0000
 number of moments:  36.0000

 linearity test from analytic      :   0.0000
 linearity test from averaging:   0.0000

/------ output based on analytic variance formula -----/
 proportion of upper regime:   0.1958

 estimates and s.e. (threshold~lower regime (lag y,x)~delta (1,lag y,x)

  0.3590   0.3875   0.0519  -0.0038  -0.1350   0.4409  -0.0594  -0.0039   0.6580
  0.0396   0.0919   0.0387   0.0023   0.0466   0.2601   0.0521   0.0019   0.1322

 upper regime estimates and s.e. (lag y,x)

  0.8284  -0.0075  -0.0077   0.5229
  0.2291   0.0457   0.0033   0.1311

 Long-run parameter estimates and s.e.

  0.0519  -0.0038  -0.1350  -0.0075  -0.0077   0.5229
  0.0696   0.0033   0.1239   1.3404   0.2641   2.3207

 Overidentification J-statistic (p-value)
 45.0039   0.0162

/------ in case of averaging -----/
 proportion of upper regime:   0.1928

 estimates and s.e. (threshold~lower regime (lag y,x)~delta (1,lag y,x)

  0.3610   0.3862   0.0570  -0.0035  -0.1214   0.4407  -0.0773  -0.0032   0.6044
  0.0420   0.0870   0.0407   0.0022   0.0434   0.2432   0.0494   0.0017   0.1207

 upper regime estimates and s.e. (lag y,x)

  0.8269  -0.0203  -0.0067   0.4830
  0.2148   0.0439   0.0031   0.1214

 Long-run parameter estimates and s.e.

  0.0570  -0.0035  -0.1214  -0.0203  -0.0067   0.4830
  0.0701   0.0032   0.1079   1.2555   0.2512   1.9488

 Overidentification J-statistic (p-value)
 62.2397   0.0001

 elapsed time : 11 minutes  27.62 seconds

Based on this, it appears that to reproduce the results of the paper exactly, you would need to use the same starting random seed.

aptech

1,323


0



Hi,

Have also computed similar results but don't know how to interpret them.

Any help please?

davide

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