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From |
Kit Baum <[email protected]> |

To |
[email protected] |

Subject |
st: re: prais-winsten regression |

Date |
Thu, 11 Oct 2007 06:57:35 -0400 |

Evan said

I am regressing energy projection errors against GDP projection errors.

Both are measured as percentages and are pretty well bounded by +/- 0.2

(20%). We choose one prediction length and analyze a time-series of

such projections (1983 projection of 1988, 1984 projection of 1989, 1985

projection of 1990, etc.)

We are running Prais-Winsten regressions to address autocorrelation.

However, every so often we get very large (and completely absurd)

coefficients on either the variable or the constant term. These do not

arise when we use OLS, though we worry that those estimates would be

biased. These huge coefficients arise despite the fact that both Does

anyone know of any reason this might occur in a Prais-Winston

regression? Thanks.

Here is the output from one problematic regression:

tsset trend, yearly

prais pct_error gdp_nom_error, ssesearch

time variable: trend, . to 0103, but with gaps

Number of gaps in sample: 4

(note: computations for rho restarted at each gap)

Iteration 1: rho = 0.8944 , criterion = -.0259697

Iteration 2: rho = 0.8944 , criterion = -.0259697

Iteration 3: rho = 0.8944 , criterion = -.0259697

Iteration 4: rho = 0.9282 , criterion = -.02532865

Iteration 5: rho = 0.9806 , criterion = -.024677

Iteration 6: rho = 0.9806 , criterion = -.024677

Iteration 7: rho = 1.0005 , criterion = -.02401302

Iteration 8: rho = 1.0005 , criterion = -.02401302

Iteration 9: rho = 1.0005 , criterion = -.02401302

Iteration 10: rho = 1.0005 , criterion = -.02401302

Iteration 11: rho = 1.0005 , criterion = -.02401302

Iteration 12: rho = 1.0005 , criterion = -.02401302

Iteration 13: rho = 1.0005 , criterion = -.02401302

Iteration 14: rho = 1.0005 , criterion = -.02401302

Iteration 15: rho = 1.0001 , criterion = -.02400141

Iteration 16: rho = 1.0001 , criterion = -.02400141

Iteration 17: rho = 1.0001 , criterion = -.02400141

Iteration 18: rho = 1.0000 , criterion = -.02399868

Iteration 19: rho = 1.0000 , criterion = -.02399868

Iteration 20: rho = 1.0000 , criterion = -.02399868

Iteration 21: rho = 1.0000 , criterion = -.02399868

Iteration 22: rho = 1.0000 , criterion = -.02399868

Iteration 23: rho = 1.0000 , criterion = -.02399843

Iteration 24: rho = 1.0000 , criterion = -.02399843

Iteration 25: rho = 1.0000 , criterion = -.02399843

Iteration 26: rho = 1.0000 , criterion = -.02399843

Iteration 27: rho = 1.0000 , criterion = -.02399843

Iteration 28: rho = 1.0000 , criterion = -.02399841

Iteration 29: rho = 1.0000 , criterion = -.02399839

Can you say "unit root" ???

Why do you think that prais---which is basically an OLS technique coupled with an estimate of rho---is going to give a consistent estimate for an I(1) series? Use -dfgls- on the dependent and independent variables and you will likely find that you have a textbook example of what Granger & Engle call a spurious regression.

Kit Baum, Boston College Economics and DIW Berlin

http://ideas.repec.org/e/pba1.html

An Introduction to Modern Econometrics Using Stata:

http://www.stata-press.com/books/imeus.html

Begin forwarded message:

I am regressing energy projection errors against GDP projection errors.

Both are measured as percentages and are pretty well bounded by +/- 0.2

(20%). We choose one prediction length and analyze a time-series of

such projections (1983 projection of 1988, 1984 projection of 1989, 1985

projection of 1990, etc.)

We are running Prais-Winsten regressions to address autocorrelation.

However, every so often we get very large (and completely absurd)

coefficients on either the variable or the constant term. These do not

arise when we use OLS, though we worry that those estimates would be

biased. These huge coefficients arise despite the fact that both Does

anyone know of any reason this might occur in a Prais-Winston

regression? Thanks.

Here is the output from one problematic regression:

tsset trend, yearly

prais pct_error gdp_nom_error, ssesearch

time variable: trend, . to 0103, but with gaps

Number of gaps in sample: 4

(note: computations for rho restarted at each gap)

Iteration 1: rho = 0.8944 , criterion = -.0259697

Iteration 2: rho = 0.8944 , criterion = -.0259697

Iteration 3: rho = 0.8944 , criterion = -.0259697

Iteration 4: rho = 0.9282 , criterion = -.02532865

Iteration 5: rho = 0.9806 , criterion = -.024677

Iteration 6: rho = 0.9806 , criterion = -.024677

Iteration 7: rho = 1.0005 , criterion = -.02401302

Iteration 8: rho = 1.0005 , criterion = -.02401302

Iteration 9: rho = 1.0005 , criterion = -.02401302

Iteration 10: rho = 1.0005 , criterion = -.02401302

Iteration 11: rho = 1.0005 , criterion = -.02401302

Iteration 12: rho = 1.0005 , criterion = -.02401302

Iteration 13: rho = 1.0005 , criterion = -.02401302

Iteration 14: rho = 1.0005 , criterion = -.02401302

Iteration 15: rho = 1.0001 , criterion = -.02400141

Iteration 16: rho = 1.0001 , criterion = -.02400141

Iteration 17: rho = 1.0001 , criterion = -.02400141

Iteration 18: rho = 1.0000 , criterion = -.02399868

Iteration 19: rho = 1.0000 , criterion = -.02399868

Iteration 20: rho = 1.0000 , criterion = -.02399868

Iteration 21: rho = 1.0000 , criterion = -.02399868

Iteration 22: rho = 1.0000 , criterion = -.02399868

Iteration 23: rho = 1.0000 , criterion = -.02399843

Iteration 24: rho = 1.0000 , criterion = -.02399843

Iteration 25: rho = 1.0000 , criterion = -.02399843

Iteration 26: rho = 1.0000 , criterion = -.02399843

Iteration 27: rho = 1.0000 , criterion = -.02399843

Iteration 28: rho = 1.0000 , criterion = -.02399841

Iteration 29: rho = 1.0000 , criterion = -.02399839

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