I am stuck in my model, it's been more than two years that I have not had any econometric courses and I have forgotten some parts...

So, here's the thing: I want to see the effect of the farmland values on the urban sprawl and as a proxy of sprawl, I am using the "land permits" data on county level for Kansas.

as explanatory variable, I have income, density of population, farmland price and a dummy variable, 1 for a county being adjacent to the metropolitan area and 0 for the opposite case.

When I run the model with the dummy, the dummy turns out to be insignificant and the model seems good without it, (Rsquared, pvalue and the signs are correct) BUT

1- My datas are not normally distributed (but quite large sample)

2- httest shows I have the heteroscedasity problem.

3- my residuals are not normally distributed.

4- ovtest shows that my model has some omitted variables (I tried to add some others but they are not statically significant! )

I know its awful :shakehead but I have no idea WHERE I should start, I need some help,

here is the result of regression:

****

reg permits nilv income density dummy

Source | SS df MS Number of obs = 105

-------------+------------------------------ F( 4, 100) = 45.51

Model | 1642023.55 4 410505.888 Prob > F = 0.0000

Residual | 901959.21 100 9019.5921 R-squared = 0.6455

-------------+------------------------------ Adj R-squared = 0.6313

Total | 2543982.76 104 24461.3727 Root MSE = 94.972

------------------------------------------------------------------------------

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

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

nilv | -.0356831 .0147927 -2.41 0.018 -.0650313 -.0063349

income | .0052909 .0016778 3.15 0.002 .0019622 .0086197

density | .958824 .127327 7.53 0.000 .7062109 1.211437

dummy | 31.06607 20.52525 1.51 0.133 -9.655439 71.78757

_cons | -186.5026 70.08598 -2.66 0.009 -325.5512 -47.45402

------------------------------------------------------------------------------

and here it is without dummy:

****

reg permits nilv income density

Source | SS df MS Number of obs = 105

-------------+------------------------------ F( 3, 101) = 59.16

Model | 1621361.06 3 540453.687 Prob > F = 0.0000

Residual | 922621.701 101 9134.86832 R-squared = 0.6373

-------------+------------------------------ Adj R-squared = 0.6266

Total | 2543982.76 104 24461.3727 Root MSE = 95.577

------------------------------------------------------------------------------

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

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

nilv | -.0343146 .0148591 -2.31 0.023 -.063791 -.0048382

income | .0052326 .0016881 3.10 0.003 .0018839 .0085813

density | .9651885 .1280682 7.54 0.000 .7111357 1.219241

_cons | -176.8083 70.23728 -2.52 0.013 -316.1401 -37.47641

------------------------------------------------------------------------------

** ovtest

Ramsey RESET test using powers of the fitted values of permits

Ho: model has no omitted variables

F(3, 98) = 22.88

Prob > F = 0.0000

** hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Ho: Constant variance

Variables: fitted values of permits

chi2(1) = 533.52

Prob > chi2 = 0.0000

* sktest r, noadj

Skewness/Kurtosis tests for Normality

------- joint ------

Variable | Obs Pr(Skewness) Pr(Kurtosis) chi2(2) Prob>chi2

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

r | 105 0.0000 0.0000 65.22 0.0000

Any idea where I should start?