0

I am working on a time series-based study on the Czech Republic. I have macroeconomic data from 1993 to 2021. I tested my time series for stationarity using both R (function adfTest from package fUnitRoots) and Gretl. The results are significantly different to the point that for example the differences of GDP are strongly stationary according to Gretl, but nonstationary according to R. Both the test statistics and p-values are different. Do you have any idea why is that and which result is correct?

The test statistic for differences (I used the "constant" version and 3 lags as recommended by R)

  • According to R: -1.8587
  • According to Gretl: -4.27469 The p-values:
  • According to R: 0.3727
  • According to Gretl: 0.0004865

I am also enclosing the data

Year;GDP_(CZKm)
1993;1 205 330
1994;1 375 851
1995;1 596 306
1996;1 829 255
1997;1 971 024
1998;2 156 624
1999;2 252 983
2000;2 386 289
2001;2 579 126
2002;2 690 982
2003;2 823 452
2004;3 079 207
2005;3 285 601
2006;3 530 881
2007;3 859 533
2008;4 042 860
2009;3 954 320
2010;3 992 870
2011;4 062 323
2012;4 088 912
2013;4 142 811
2014;4 345 766
2015;4 625 378
2016;4 796 873
2017;5 110 743
2018;5 410 761
2019;5 791 498
2020;5 709 131
2021;6 108 717
Ben Bolker
  • 211,554
  • 25
  • 370
  • 453
Martina Smrčková
  • 1
  • 1
    They are likely just fitting different models. But talking about why the models are different is more if a statistical question than a programming question. You are likely to get better help tracking down the differences by asking for help at [stats.se] – MrFlick Feb 03 '23 at 14:55

0 Answers0