economics

Cut and past two regressions results from Stata. The first one is a simple regression of your dependent variable on your key independent variable (use logs if you think it necessary). The second is a multiple variable regression, where you regress your dependent variable on your key independent variable and at least two more control variables (In this case, set manufacturing and GDP as two more control variables). Answer the following questions:

1) What does the simple regression suggest about the relationship between your x and y variables? Is the slope coefficient statistically significant? What is the size? How do you interpret it?

reg lnco2 lnPop

Source | SS df MS Number of obs = 12,248

-------------+---------------------------------- F(1, 12246) = 37067.11

Model | 103764.011 1 103764.011 Prob > F = 0.0000

Residual | 34280.9068 12,246 2.79935544 R-squared = 0.7517

-------------+---------------------------------- Adj R-squared = 0.7516

Total | 138044.917 12,247 11.2717333 Root MSE = 1.6731

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lnco2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

lnPop | .9880223 .0051318 192.53 0.000 .9779631 .9980815

_cons | -6.35125 .0833884 -76.16 0.000 -6.514704 -6.187796

2) Describe the additional control variables that you included and why you included them in your regression. Now explain the results: What does the table suggest about how they affect your dependent variable (or not) and how do you interpret the coefficients and the t-statistics?

reg lnco2 lnPop lnManu lnGDP

Source | SS df MS Number of obs = 7,365

-------------+---------------------------------- F(3, 7361) = 30919.34

Model | 62263.3061 3 20754.4354 Prob > F = 0.0000

Residual | 4941.02965 7,361 .671244349 R-squared = 0.9265

-------------+---------------------------------- Adj R-squared = 0.9264

Total | 67204.3358 7,364 9.12606407 Root MSE = .8193

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lnco2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

lnPop | .1871355 .0066558 28.12 0.000 .1740882 .2001827

lnManu | .418834 .016292 25.71 0.000 .386897 .450771

lnGDP | .3929029 .0179113 21.94 0.000 .3577917 .4280142

_cons | -11.79659 .107627 -109.61 0.000 -12.00757 -11.58561

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3) What happens to the size and significance of your key independent variable across the two regressions? Does it change in an appreciable manner when you include additional controls? If so, why do you think this is? If not, why might this be?