Hi all,

I am conducting some research into impact of the World Cup on the growth rate of host countries. I have a panel of 160 countries which includes observations of variables such as GDP, GDP Growth for the period 1990-2016. An example of the data is shown below. This data has been xtset.

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str30 CountryName double(GDP GDPGROWTH GDPCAP) float(Pre4 Pre3 Pre2 Pre1 t Post1 Post2 Post3 Post4)
"Albania"  6178834824.387625  -9.575640169486448 1880.0413396170275 0 0 0 0 0 0 0 0 0
"Albania"  4350579530.232994  -29.58899770129115 1331.7597795490356 0 0 0 0 0 0 0 0 0
"Albania" 4037337804.6581616  -7.199999986164059  1243.390610540299 0 0 0 0 0 0 0 0 0
"Albania"  4424922235.738512   9.600000045405338 1371.0966008720366 0 0 0 0 0 0 0 0 0
"Albania"  4792190780.412167    8.29999997982695 1494.0411519659224 0 0 0 0 0 0 0 0 0
"Albania"  5429552153.841033  13.299999992363581   1703.23715591804 0 0 0 0 0 0 0 0 0
"Albania"  5923641400.259521   9.100000007716176 1869.8168233283936 0 0 0 0 0 0 0 0 0
"Albania"   5276779759.35118 -10.920000000000016  1676.082839921589 0 0 0 0 0 0 0 0 0
"Albania"  5742719412.101891   8.830000000000027  1835.596721815642 0 0 0 0 0 0 0 0 0
"Albania"  6482955944.321828  12.890000000000072 2085.3711472230657 0 0 0 0 0 0 0 0 0
"Albania" 6933521382.4521885   6.949999999999903 2244.5648362582097 0 0 0 0 0 0 0 0 0
"Albania"  7508310305.057478   8.290000000000049  2453.557463926869 0 0 0 0 0 0 0 0 0
"Albania"  7849187592.907091   4.540000000000049 2572.6522013717067 0 0 0 0 0 0 0 0 0
"Albania"  8283247666.794856    5.53000000000003 2725.0967447186936 0 0 0 0 0 0 0 0 0
"Albania"   8739654613.23524   5.509999999999863  2887.291291048561 0 0 0 0 0 0 0 0 0
"Albania"  9222957513.347157   5.530000000000086 3062.5925044163087 0 0 0 0 0 0 0 0 0
"Albania"  9767112006.634628   5.899999999999878 3263.8124001509846 0 0 0 0 0 0 0 0 0
"Albania"  10351185304.63138   5.980000000000004 3485.2276282025928 0 0 0 0 0 0 0 0 0
"Albania"  11127524202.47875   7.500000000000156  3775.479708805628 0 0 0 0 0 0 0 0 0
"Albania" 11500296263.261787    3.34999999999998  3928.342143385504 0 0 0 0 0 0 0 0 0
"Albania" 11926957254.628792  3.7099999999999227 4094.3602035923504 0 0 0 0 0 0 0 0 0
"Albania" 12231094664.621826  2.5500000000000114   4210.07700502783 0 0 0 0 0 0 0 0 0
"Albania" 12404776208.859451   1.419999999999959  4276.917643063649 0 0 0 0 0 0 0 0 0
"Albania" 12528823970.948051  1.0000000000000426  4327.608231775726 0 0 0 0 0 0 0 0 0
"Albania" 12750584155.233839  1.7700000000000529  4413.335122319529 0 0 0 0 0 0 0 0 0
"Albania" 13033647123.480019  2.2199999999999136  4524.467507924287 0 0 0 0 0 0 0 0 0
"Albania" 13470274302.116606   3.350000000000051  4683.519216507559 0 0 0 0 0 0 0 0 0
"Albania" 13986932578.969046  3.8355438446510135  4867.632464647651 0 0 0 0 0 0 0 0 0
"Algeria"  91989749446.97946   .8000005799814147  3550.032671541718 0 0 0 0 0 0 0 0 0
"Algeria"  90885871916.03632  -1.200000584390537 3422.6386182093443 0 0 0 0 0 0 0 0 0
"Algeria"  92521819701.82526  1.8000023010180257 3403.9034522240077 0 0 0 0 0 0 0 0 0
"Algeria"   90578860785.6483  -2.100000759214012 3259.8436797716563 0 0 0 0 0 0 0 0 0
"Algeria"  89763654164.59029  -.8999965488494865 3164.8985771543003 0 0 0 0 0 0 0 0 0
"Algeria"  93174668346.04738  3.7999947898763793 3223.5575604032097 0 0 0 0 0 0 0 0 0
"Algeria"  96994828322.43536   4.099998469755789  3297.863374558326 0 0 0 0 0 0 0 0 0
"Algeria"   98061771373.7004   1.099999937850555  3281.102139095419 0 0 0 0 0 0 0 0 0
"Algeria" 103062925254.28166   5.100003610502341 3397.4101977918867 0 0 0 0 0 0 0 0 0
"Algeria"  106360940460.5076  3.2000015505953456 3457.1370464975817 0 0 0 0 0 0 0 0 0
"Algeria" 110423586430.99423  3.8196784955987937 3541.0720367972917 0 0 0 0 0 0 0 0 0
"Algeria" 113745564598.73032  3.0083954661371592 3600.4372541095986 0 0 0 0 0 0 0 0 0
"Algeria" 120125920931.17308   5.609323189832736 3754.5162751500056 0 0 0 0 0 0 0 0 0
"Algeria" 128777236288.58391  7.2018722440160445  3974.175032022265 0 0 0 0 0 0 0 0 0
"Algeria" 134316749131.75137   4.301624264364293 4091.1442350797965 0 0 0 0 0 0 0 0 0
"Algeria" 142251902308.52768   5.907791268081326  4273.312751467654 0 0 0 0 0 0 0 0 0
"Algeria" 144648118986.03094  1.6844883186912796  4282.328230917478 0 0 0 0 0 0 0 0 0
"Algeria"  149526919452.5767  3.3728751543716413  4359.375747522446 0 0 0 0 0 0 0 0 0
"Algeria"   153055956405.236   2.360134861053268  4390.499632759569 0 0 0 0 0 0 0 0 0
"Algeria"  155554202821.6213  1.6322438375223243  4386.038895588909 0 0 0 0 0 0 0 0 0
"Algeria" 161207268655.39215  3.6341453533424612  4463.394674889505 0 0 0 0 0 0 0 0 0
"Algeria" 165869166838.53906  2.8918659946484837   4504.92009813206 0 0 0 0 0 0 0 0 0
"Algeria"  171466867482.1632   3.374768650687841  4564.435016789671 0 0 0 0 0 0 0 0 0
"Algeria" 176212451150.23447  2.7676388667711223  4596.219627388071 0 0 0 0 0 0 0 0 0
"Algeria" 182889354514.36853   3.789121211668231  4675.885024476667 0 0 0 0 0 0 0 0 0
"Algeria" 189772334940.90765  3.7634669578312554  4759.595241519403 0 0 0 0 0 0 0 0 0
"Algeria"  196034821993.4945  3.2999999997559684  4827.724251387318 0 0 0 0 0 0 0 0 0
"Algeria" 199171379146.41522  1.6000000005227548  4820.434063719884 0 0 0 0 0 0 0 0 0
"Andorra" 1950360346.8336878   3.781387589629162   35780.5196725988 0 0 0 0 0 0 0 0 0
"Andorra" 2000016577.2760122  2.5460028718764107 35291.711409292446 0 0 0 0 0 0 0 0 0
"Andorra"  2018600970.439821   .9292119562888956 34278.647100255075 0 0 0 0 0 0 0 0 0
"Andorra" 1997779424.1846933 -1.0314840109578967  32766.05967074008 0 0 0 0 0 0 0 0 0
"Andorra" 2045390257.5149548   2.383187690988038  32633.82512747826 0 0 0 0 0 0 0 0 0
"Andorra" 2101791877.2436512   2.757499187329728  32917.64882135711 0 0 0 0 0 0 0 0 0
"Andorra"  2199519738.003028   4.649740148750595  34175.26006841249 0 0 0 0 0 0 0 0 0
"Andorra" 2398964977.7997584   9.067672199105132  37293.28241329082 0 0 0 0 0 0 0 0 0
"Andorra" 2475606878.1163473   3.194790296058514 38595.723209696414 0 0 0 0 0 0 0 0 0
"Andorra" 2577084005.2476096   4.099080836634087 40035.482449085124 0 0 0 0 0 0 0 0 0
"Andorra"  2668012839.882807    3.52836129711109  40801.54213003222 0 0 0 0 0 0 0 0 0
"Andorra"  2789321202.452289   4.546768319706089 41420.846177696934 0 0 0 0 0 0 0 0 0
"Andorra"   2969818586.67949   6.471014670827913  42396.30239802838 0 0 0 0 0 0 0 0 0
"Andorra" 3331207491.6417465  12.168719886904597 45519.492383943405 0 0 0 0 0 0 0 0 0
"Andorra"  3585973903.044556   7.647869790219858  47032.86688847065 0 0 0 0 0 0 0 0 0
"Andorra"  3851227772.830262   7.396982715365013 48831.929359938404 0 0 0 0 0 0 0 0 0
"Andorra" 4025933064.0290008   4.536353119159912  49708.40048930129 0 0 0 0 0 0 0 0 0
"Andorra"   4027543882.80023  .04001106689084111 48710.664620299576 0 0 0 0 0 0 0 0 0
"Andorra"  3681577710.753998  -8.590003786766744  43900.95170286543 0 0 0 0 0 0 0 0 0
"Andorra" 3545703433.7638264  -3.690653509588529  41979.86590139739 0 0 0 0 0 0 0 0 0
"Andorra" 3355695364.2384105  -5.358825775333358   39736.3540626699 0 0 0 0 0 0 0 0 0
"Andorra" 3199771534.4574075  -4.646543051633373 38205.771088791866 0 0 0 0 0 0 0 0 0
"Andorra" 3148088242.8188205  -1.615218182986652  38190.58658537226 0 0 0 0 0 0 0 0 0
"Andorra" 3159158337.7785444  .35164500185076975  39104.30184901897 0 0 0 0 0 0 0 0 0
"Andorra" 3231113955.0167494  2.2776831530642028 40785.049228339616 0 0 0 0 0 0 0 0 0
"Andorra" 3258326510.4285784   .8422035183741343  41765.92035312352 0 0 0 0 0 0 0 0 0
"Andorra"  3319880351.133421   1.889124386639395  42958.55839253401 0 0 0 0 0 0 0 0 0
"Andorra" 3382068237.5248113  1.8731966159611488 43942.938186510895 0 0 0 0 0 0 0 0 0
"Angola"  31822141256.216652 -3.4500986836048355 2614.4925039045625 0 0 0 0 0 0 0 0 0
"Angola"   32137613010.45088   .9913592919288448  2560.063030537661 0 0 0 0 0 0 0 0 0
"Angola"  30261328943.397697  -5.838280728699502 2333.4765495055613 0 0 0 0 0 0 0 0 0
"Angola"  23003628103.413578 -23.983417428756297 1716.2104308705007 0 0 0 0 0 0 0 0 0
"Angola"  23311730287.409233  1.3393634369786014 1684.2152545782533 0 0 0 0 0 0 0 0 0
"Angola"  26808489836.790936  15.000000026897695 1878.7932657895108 0 0 0 0 0 0 0 0 0
"Angola"  30439530825.347652  13.544369752501368 2073.2149592902338 0 0 0 0 0 0 0 0 0
"Angola"  32653786720.212154   7.274277345367764  2164.081638131306 0 0 0 0 0 0 0 0 0
"Angola"  34185623674.956352   4.691146444574585  2204.909862849585 0 0 0 0 0 0 0 0 0
"Angola"   34931379545.11695   2.181489731623415  2190.087274328473 0 0 0 0 0 0 0 0 0
"Angola"  35998401929.875435   3.054624233721796 2189.5607527822303 0 0 0 0 0 0 0 0 0
"Angola"  37512494194.487854   4.205998553940972 2208.7915360030192 0 0 0 0 0 0 0 0 0
"Angola"   42638834053.56884  13.665686511013874  2426.431783481753 0 0 0 0 0 0 0 0 0
"Angola"   43913671240.49369  2.9898500163565274 2412.3925214334604 0 0 0 0 0 0 0 0 0
"Angola"   48723474948.03264  10.952861766437167   2582.64647618106 0 0 0 0 0 0 0 0 0
"Angola"   56046084738.63072  15.028915319377802  2866.434693689993 0 0 0 0 0 0 0 0 0
end

I have 9 created dummies which each correspond to a year in a country's World Cup cycle. The dummy named t refers to the year of the World Cup, whilst the dummies Pre4, Pre3, Pre2, and Pre1 refer to the years preceding the World Cup. The dummies Post1, Post2, Post3, and Post4 refer to the years following the World Cup.

I am looking to test the exogeneity of the dummies I use. Specifically, I want to establish whether countries with high growth in the years before a World Cup host is selected are more likely to be selected as a hosts.

I have seen a similar approach taken in Sterken (2006), which is described as:

"In order to test this selection bias hypothesis we estimated a binary choice (logit) model with the event dummy variables as dependent variables (taking the value 1 if a country organized an event and 0 in other cases) and lagged GDP per capita growth as determinants. There is no endogeneity of the events found for lags up to eight years."


I have tried to implement this using a logit model to regress my dummy for the World Cup year (t) on up to 12 lags of GDP growth, as below.

Code:
*1 Lag
gen lagOwnGDPGROWTH=OwnGDPGROWTH[_n-1]
*2 lags 
gen lagOwnGDPGROWTH2=OwnGDPGROWTH[_n-2]
*3 lags 
gen lagOwnGDPGROWTH3=OwnGDPGROWTH[_n-3]
*4 Lags
gen lagOwnGDPGROWTH4=OwnGDPGROWTH[_n-4]
*5 Lags
gen lagOwnGDPGROWTH5=OwnGDPGROWTH[_n-5]
*6 Lags
gen lagOwnGDPGROWTH6=OwnGDPGROWTH[_n-6]
*7 Lags
gen lagOwnGDPGROWTH7=OwnGDPGROWTH[_n-7]
*8 Lags
gen lagOwnGDPGROWTH8=OwnGDPGROWTH[_n-8]
*9 Lags
gen lagOwnGDPGROWTH9=OwnGDPGROWTH[_n-9]
*10 Lags
gen lagOwnGDPGROWTH10=OwnGDPGROWTH[_n-10]
*11 Lags
gen lagOwnGDPGROWTH11=OwnGDPGROWTH[_n-11]
*12 Lags
gen lagOwnGDPGROWTH12=OwnGDPGROWTH[_n-12]


logistic t lagGDPGROWTH lagGDPROWTH2 lagGDPGROWTH3 lagGDPGROWTH4 lagGDPGROWTH5 lagGDPGROWTH6 lagGDPGROWTH7 lagGDPGROWTH8 lagGDPGROWTH9 lagGDPGROWTH10 lagGDPGROWTH11 lagGDPGROWTH12
This gives the output:

Code:
Logistic regression                             Number of obs     =        286
                                                LR chi2(12)       =      11.27
                                                Prob > chi2       =     0.5057
Log likelihood =  -27.24784                     Pseudo R2         =     0.1714

-----------------------------------------------------------------------------------
                t | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
  lagOwnGDPGROWTH |   .7803537   .1386373    -1.40   0.163     .5508932     1.10539
 lagOwnGDPGROWTH2 |   1.123929   .1439277     0.91   0.362      .874452     1.44458
 lagOwnGDPGROWTH3 |   1.009245   .1552665     0.06   0.952     .7465252    1.364423
 lagOwnGDPGROWTH4 |    .831256    .105247    -1.46   0.144     .6485784    1.065386
 lagOwnGDPGROWTH5 |   .6763069   .1177278    -2.25   0.025     .4808075    .9512975
 lagOwnGDPGROWTH6 |   1.157211   .1726232     0.98   0.328     .8638482      1.5502
 lagOwnGDPGROWTH7 |   1.207327   .1974243     1.15   0.249     .8762637     1.66347
 lagOwnGDPGROWTH8 |   1.110775   .1947707     0.60   0.549     .7877176    1.566323
 lagOwnGDPGROWTH9 |   .9037453    .163654    -0.56   0.576     .6337339    1.288799
lagOwnGDPGROWTH10 |   .9988944   .1671454    -0.01   0.995     .7195942    1.386601
lagOwnGDPGROWTH11 |   1.169153   .1938346     0.94   0.346     .8447922    1.618052
lagOwnGDPGROWTH12 |   .8571109   .1352739    -0.98   0.329     .6290653    1.167826
            _cons |   .0276128   .0234739    -4.22   0.000     .0052179    .1461254
-----------------------------------------------------------------------------------
I am unsure if this is the correct approach or how to interpret the odds ratios. I also have the logit coefficients which appear when I use outreg2 to convert the tables to word.

I hope I have been as clear as I can with my issue. Any help is much appreciated.

Nic