Hi all,

I am running a regression for my dissertation and was planning on including year dummies. The output I get is as follows:

Code:
 reg newenrol CPI newmarriage depend newedspend newmortality newfemteach urban lnGDP i.year

      Source |       SS           df       MS      Number of obs   =     1,017
-------------+----------------------------------   F(25, 991)      =    284.34
       Model |  575642.157        25  23025.6863   Prob > F        =    0.0000
    Residual |  80251.9092       991  80.9807358   R-squared       =    0.8776
-------------+----------------------------------   Adj R-squared   =    0.8746
       Total |  655894.067     1,016  645.565026   Root MSE        =    8.9989

------------------------------------------------------------------------------
    newenrol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         CPI |   .0111325   .0296963     0.37   0.708    -.0471423    .0694074
 newmarriage |   .0644005   .0702137     0.92   0.359     -.073384     .202185
      depend |  -.2866934   .0331114    -8.66   0.000      -.35167   -.2217168
  newedspend |   1.322696   .2283656     5.79   0.000     .8745602    1.770832
newmortality |   -.567749   .0362517   -15.66   0.000    -.6388879     -.49661
 newfemteach |   .2620551   .0224073    11.70   0.000     .2180839    .3060264
       urban |   .0612197   .0238815     2.56   0.011     .0143556    .1080839
       lnGDP |   1.565205   .6738355     2.32   0.020     .2428971    2.887514
             |
        year |
       2001  |   .2016182   1.725029     0.12   0.907    -3.183511    3.586748
       2002  |  -.2950407   1.717641    -0.17   0.864    -3.665671     3.07559
       2003  |   .1552302   1.709801     0.09   0.928    -3.200016    3.510476
       2004  |   .0421186   1.710136     0.02   0.980    -3.313785    3.398022
       2005  |    .010656    1.70356     0.01   0.995    -3.332343    3.353655
       2006  |  -.1910212   1.703714    -0.11   0.911    -3.534322     3.15228
       2007  |  -.3678111   1.704711    -0.22   0.829    -3.713068    2.977446
       2008  |  -.7939854   1.705902    -0.47   0.642    -4.141581     2.55361
       2009  |  -1.582709   1.711864    -0.92   0.355    -4.942003    1.776585
       2010  |  -1.762984   1.711649    -1.03   0.303    -5.121857    1.595889
       2011  |  -2.007432   1.713243    -1.17   0.242    -5.369433    1.354569
       2012  |  -1.745967   1.712873    -1.02   0.308    -5.107242    1.615308
       2013  |  -.9419901   1.715142    -0.55   0.583    -4.307717    2.423737
       2014  |   -.540971   1.716269    -0.32   0.753     -3.90891    2.826968
       2015  |  -.1426261   1.717701    -0.08   0.934    -3.513374    3.228122
       2016  |   .3639826   1.718525     0.21   0.832    -3.008383    3.736349
       2017  |   .2700282   1.719346     0.16   0.875    -3.103948    3.644005
             |
       _cons |   60.52972    5.88975    10.28   0.000     48.97191    72.08754
All of the year dummies are insignificant! When I run the regression without the year dummies I get similar results for the other variables, the output is as follows:

Code:
reg newenrol CPI newmarriage depend newedspend newmortality newfemteach urban lnGDP

      Source |       SS           df       MS      Number of obs   =     1,017
-------------+----------------------------------   F(8, 1008)      =    896.51
       Model |  575070.577         8  71883.8221   Prob > F        =    0.0000
    Residual |  80823.4898     1,008  80.1820335   R-squared       =    0.8768
-------------+----------------------------------   Adj R-squared   =    0.8758
       Total |  655894.067     1,016  645.565026   Root MSE        =    8.9544

------------------------------------------------------------------------------
    newenrol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         CPI |   .0136373   .0294484     0.46   0.643    -.0441498    .0714244
 newmarriage |   .0686673   .0697638     0.98   0.325    -.0682315    .2055662
      depend |  -.2826268   .0328785    -8.60   0.000    -.3471448   -.2181087
  newedspend |   1.273113   .2259444     5.63   0.000     .8297378    1.716488
newmortality |  -.5659455   .0353495   -16.01   0.000    -.6353126   -.4965785
 newfemteach |   .2636893   .0222034    11.88   0.000     .2201191    .3072595
       urban |   .0624549   .0237473     2.63   0.009      .015855    .1090548
       lnGDP |   1.567741   .6653732     2.36   0.019     .2620653    2.873416
       _cons |   59.56112   5.612467    10.61   0.000     48.54767    70.57458

Should I leave the year dummies in, or just say I tried doing the regression with them and none were significant so I removed them? I'm not sure if there is a standard practise for this kind of thing?

Thanks very much