My coefficient for my Construction costs index is too high I have correct construction costs index by dividing it by the cpi index and multiplied by 100 to get the real construction cost variable afterwards I have taken the log of real construction cost to get log real construction cost index. However when I run my regression I get a very high coefficient and I was wondering how I can solve this problem?
Code:
* Example generated by -dataex-. For more info, type help dataex clear input double ConsCost_index float(real_consCost log_real_consCost) 100 100 4.6051702 105.2 102.73438 4.632147 109.3 102.53437 4.630198 111.5 101.25672 4.617659 113.7 101.13087 4.6164155 115.9 101.76472 4.6226635 119.3 102.99907 4.63472 124.1 105.97746 4.6632266 129.8 109.09948 4.6922603 130 106.60252 4.669107 130.8 105.9867 4.6633134 133.4 106.70628 4.67008 135.7 106.10562 4.664435 136 103.74653 4.6419506 137.2 102.1092 4.626043 139.8 103.01408 4.6348658 142.6 104.4506 4.648714 145.9 106.54813 4.6685967 149.6 107.74178 4.6797376 153.8 108.91506 4.6905684 157.2 108.50176 4.6867666 100 100 4.6051702 105.2 102.73438 4.632147 109.3 102.53437 4.630198 111.5 101.25672 4.617659 113.7 101.13087 4.6164155 115.9 101.76472 4.6226635 119.3 102.99907 4.63472 124.1 105.97746 4.6632266 129.8 109.09948 4.6922603 130 106.60252 4.669107 130.8 105.9867 4.6633134 133.4 106.70628 4.67008 135.7 106.10562 4.664435 136 103.74653 4.6419506 137.2 102.1092 4.626043 139.8 103.01408 4.6348658 142.6 104.4506 4.648714 145.9 106.54813 4.6685967 149.6 107.74178 4.6797376 153.8 108.91506 4.6905684 157.2 108.50176 4.6867666 100 100 4.6051702 105.2 102.73438 4.632147 109.3 102.53437 4.630198 111.5 101.25672 4.617659 113.7 101.13087 4.6164155 115.9 101.76472 4.6226635 119.3 102.99907 4.63472 124.1 105.97746 4.6632266 129.8 109.09948 4.6922603 130 106.60252 4.669107 130.8 105.9867 4.6633134 133.4 106.70628 4.67008 135.7 106.10562 4.664435 136 103.74653 4.6419506 137.2 102.1092 4.626043 139.8 103.01408 4.6348658 142.6 104.4506 4.648714 145.9 106.54813 4.6685967 149.6 107.74178 4.6797376 153.8 108.91506 4.6905684 157.2 108.50176 4.6867666 100 100 4.6051702 105.2 102.73438 4.632147 109.3 102.53437 4.630198 111.5 101.25672 4.617659 113.7 101.13087 4.6164155 115.9 101.76472 4.6226635 119.3 102.99907 4.63472 124.1 105.97746 4.6632266 129.8 109.09948 4.6922603 130 106.60252 4.669107 130.8 105.9867 4.6633134 133.4 106.70628 4.67008 135.7 106.10562 4.664435 136 103.74653 4.6419506 137.2 102.1092 4.626043 139.8 103.01408 4.6348658 142.6 104.4506 4.648714 145.9 106.54813 4.6685967 149.6 107.74178 4.6797376 153.8 108.91506 4.6905684 157.2 108.50176 4.6867666 100 100 4.6051702 105.2 102.73438 4.632147 109.3 102.53437 4.630198 111.5 101.25672 4.617659 113.7 101.13087 4.6164155 115.9 101.76472 4.6226635 119.3 102.99907 4.63472 124.1 105.97746 4.6632266 129.8 109.09948 4.6922603 130 106.60252 4.669107 130.8 105.9867 4.6633134 133.4 106.70628 4.67008 135.7 106.10562 4.664435 136 103.74653 4.6419506 137.2 102.1092 4.626043 139.8 103.01408 4.6348658 end
This is my regression command and results:
Code:
. asdoc xtreg log_realHP log_PopulationDensity log_Population Unemployment_rate log_
> real_consCost logReal_income real_interest i.low_dev i.Year,fe vce(robust) replace
> cnames(low development) save(PanelData_regression) add(Low dev Dummy,YES, Year Du
> mmy,YES) dec(3)
note: 2018.Year omitted because of collinearity
note: 2019.Year omitted because of collinearity
Fixed-effects (within) regression Number of obs = 2,532
Group variable: GM_code Number of groups = 282
R-sq: Obs per group:
within = 0.8875 min = 8
between = 0.0581 avg = 9.0
overall = 0.0023 max = 9
F(13,281) = 946.78
corr(u_i, Xb) = -0.5914 Prob > F = 0.0000
(Std. Err. adjusted for 282 clusters in GM_code)
-----------------------------------------------------------------------------------
| Robust
log_realHP | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------+----------------------------------------------------------------
log_PopulationD~y | .0364162 .0302895 1.20 0.230 -.0232068 .0960393
log_Population | .1433754 .076217 1.88 0.061 -.0066533 .2934041
Unemployment_rate | .03056 .0082342 3.71 0.000 .0143513 .0467686
log_real_consCost | 78.75831 1.275555 61.74 0.000 76.24746 81.26917
logReal_income | .2192286 .1029772 2.13 0.034 .0165239 .4219334
real_interest | .5209448 .0086732 60.06 0.000 .5038721 .5380175
1.low_dev | .0109145 .0026326 4.15 0.000 .0057325 .0160966
|
Year |
2012 | 1.041003 .0177587 58.62 0.000 1.006046 1.07596
2013 | 2.727895 .0451684 60.39 0.000 2.638983 2.816806
2014 | 3.375704 .0558562 60.44 0.000 3.265754 3.485653
2015 | 2.838823 .0477437 59.46 0.000 2.744843 2.932804
2016 | 1.79892 .0302964 59.38 0.000 1.739283 1.858557
2017 | .7132875 .0122971 58.00 0.000 .6890815 .7374935
2018 | 0 (omitted)
2019 | 0 (omitted)
|
_cons | -359.9836 6.54667 -54.99 0.000 -372.8704 -347.0969
------------------+----------------------------------------------------------------
sigma_u | .29981225
sigma_e | .02925436
rho | .99056879 (fraction of variance due to u_i)
----------------------------------------------------------------------------------
0 Response to regression coefficient calculation not correct
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