Hi everyone,
Thanks in advance for the help.
I have the following case of 32 countries and 256 observations. When I run the commands
tsset CountryID Year, yearly
estat dwatson
The following error is shown: "sample may not include multiple panels"
However, when I run xtserial SDG6_Indx LogBTCUSD logGold .. I get result of [Prob > F = 0.9389]
My questions, please:
What is the difference between xtserial and estat dwatson?
Can I rely on xtserial to test autocorrelation ?
Thanks in advance for the help.
I have the following case of 32 countries and 256 observations. When I run the commands
tsset CountryID Year, yearly
estat dwatson
The following error is shown: "sample may not include multiple panels"
However, when I run xtserial SDG6_Indx LogBTCUSD logGold .. I get result of [Prob > F = 0.9389]
My questions, please:
What is the difference between xtserial and estat dwatson?
Can I rely on xtserial to test autocorrelation ?
| SEQ | Country | Country ID | Year | SDG6_Index | Log BTC | Stocks traded, total value (current US$) | Market capitalization of listed domestic companies (current US$) | Gold reserves (current US$) |
| 1 | Argentina | 1 | 2013 | 0.559971 | 2.24E+09 | 9.349726164 | 10.72513 | 10.48478 |
| 2 | Argentina | 1 | 2014 | 0.489503 | 3.52E+09 | 9.546853468 | 10.77918 | 10.49708 |
| 3 | Argentina | 1 | 2015 | 0.584097 | 2.7E+09 | 9.43133481 | 10.74923 | 10.40689 |
| 4 | Argentina | 1 | 2016 | 0.584817 | 4.36E+09 | 9.639560751 | 10.80346 | 10.5845 |
| 5 | Argentina | 1 | 2017 | 0.555282 | 6.59E+09 | 9.819068432 | 11.03639 | 10.74284 |
| 6 | Argentina | 1 | 2018 | 0.4673 | 4.57E+09 | 9.660027701 | 10.66263 | 10.821 |
| 7 | Argentina | 1 | 2019 | 0.4665 | 2.98E+09 | 9.474701295 | 10.59543 | 10.65206 |
| 8 | Argentina | 1 | 2020 | 0.4712 | 2.18E+09 | 9.339061693 | #VALUE! | 10.59554 |
| 9 | Australia | 2 | 2013 | 0.83165 | 7.88E+11 | 11.8964608 | 12.13544 | 10.72294 |
| 10 | Australia | 2 | 2014 | 0.8368 | 7.03E+11 | 11.84725534 | 12.11015 | 10.73167 |
| 11 | Australia | 2 | 2015 | 0.8419 | 7.51E+11 | 11.87542959 | 12.07448 | 10.65711 |
| 12 | Australia | 2 | 2016 | 0.84675 | 7.95E+11 | 11.90035465 | 12.10329 | 10.72 |
| 13 | Australia | 2 | 2017 | 0.779163 | 8.43E+11 | 11.92570995 | 12.17853 | 10.81726 |
| 14 | Australia | 2 | 2018 | 0.85645 | 7.74E+11 | 11.88875606 | 12.10133 | 10.73167 |
| 15 | Australia | 2 | 2019 | 0.86125 | 8.34E+11 | 11.92128373 | 12.17249 | 10.76339 |
| 16 | Australia | 2 | 2020 | 0.8661 | 1.23E+12 | 12.08854864 | 12.23567 | 10.62884 |
| 17 | Brazil | 3 | 2013 | 0.448291 | 7.4E+11 | 11.86904288 | 12.00879 | 11.55487 |
| 18 | Brazil | 3 | 2014 | 0.454423 | 6.44E+11 | 11.80900141 | 11.92629 | 11.56059 |
| 19 | Brazil | 3 | 2015 | 0.460613 | 4.2E+11 | 11.6232263 | 11.69067 | 11.55202 |
| 20 | Brazil | 3 | 2016 | 0.466159 | 5.61E+11 | 11.74902545 | 11.87999 | 11.56227 |
| 21 | Brazil | 3 | 2017 | 0.489158 | 6.43E+11 | 11.8078749 | 11.97987 | 11.57282 |
| 22 | Brazil | 3 | 2018 | 0.490319 | 7.69E+11 | 11.88603708 | 11.96229 | 11.57369 |
| 23 | Brazil | 3 | 2019 | 0.56775 | 1.04E+12 | 12.01650069 | 12.07458 | 11.55253 |
| 24 | Brazil | 3 | 2020 | 0.575133 | 1.37E+12 | 12.13785899 | 11.99492 | 11.55098 |

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