[Guest]

Hi Clyde,

i have removed the "/" in the date variables so they are now ddmmyyyy, which is a string variable which has the type:str8 format: %9s, is there a command which can turn this format into a date format readable for STATA. i tried format name_of_date_variable %td and it returns "string %fmt required for string variables
r(120);"

thanks

[Clyde Schechter]

-help date()-

More generally, people who use Stata regularly need to familiarize themselves with Stata's approach to handling dates and times and the many function used to create and transform these variables. So, more broadly, read the Date and time functions chapter in the [FN] volume of the PDF manuals that come with your Stata installation. It's a long read, and you won't remember it all. But it will give you a comprehensive tour of this terrain and you will know what functions to look for in future situations. Then the help files will fill in the forgotten details.

[Guest]

Hi Clyde, i have run the regression for the same property repeat sales model (LogPrice(After)-LogPrice(Before) = a + b*days_between_sales) could you help me interpret the results, including the Sum of squares and mean squares results?

thank you

. by HOUSE_ID (sold_after_farm_operational_dumm), sort: keep if sold_after_farm_operational_dumm[_N] =
> = 1 & sold_after_farm_operational_dumm[1] == 0 & _N == 2
(2643 observations deleted)

. by HOUSE_ID (sold_after_farm_operational_dumm): gen delta_log_price = log(Deflated_house_price[_N]/D
> eflated_house_price[1])

. by HOUSE_ID (sold_after_farm_operational_dumm): gen days_between_sales = Date[_N] - Date[1]

. reg delta_log_price i. days_between_sales

      Source |       SS       df       MS              Number of obs =     182
-------------+------------------------------           F( 89,    92) =   35.21
       Model |  17.5061425    89   .19669823           Prob > F      =  0.0000
    Residual |  .513900881    92  .005585879           R-squared     =  0.9715
-------------+------------------------------           Adj R-squared =  0.9439
       Total |  18.0200433   181   .09955825           Root MSE      =  .07474

------------------------------------------------------------------------------------
   delta_log_price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
days_between_sales |
              697  |  -.3941674   .0747387    -5.27   0.000     -.542605   -.2457299
              700  |   -.359604   .0747387    -4.81   0.000    -.5080416   -.2111664
              746  |  -.1334763   .0747387    -1.79   0.077    -.2819139    .0149613
              769  |   -.142736   .0647256    -2.21   0.030    -.2712867   -.0141853
              820  |  -.1741309   .0747387    -2.33   0.022    -.3225684   -.0256933
              882  |  -.3458721   .0747387    -4.63   0.000    -.4943097   -.1974345
             1302  |  -.2153475   .0747387    -2.88   0.005    -.3637851   -.0669099
             1381  |  -.4622693   .0747387    -6.19   0.000    -.6107069   -.3138317
             1464  |  -.2882997   .0747387    -3.86   0.000    -.4367373   -.1398622
             1737  |  -.3824733   .0747387    -5.12   0.000    -.5309108   -.2340357
             1816  |   .2762742   .0747387     3.70   0.000     .1278366    .4247118
             1940  |   -.501379   .0747387    -6.71   0.000    -.6498166   -.3529414
             1942  |  -.1619683   .0747387    -2.17   0.033    -.3104059   -.0135307
             2005  |   -.048905   .0747387    -0.65   0.515    -.1973426    .0995326
             2463  |   .0721482   .0747387     0.97   0.337    -.0762894    .2205858
             2541  |  -.3064635   .0747387    -4.10   0.000    -.4549011   -.1580259
             2646  |  -.3789136   .0747387    -5.07   0.000    -.5273512    -.230476
             2653  |   .6935585   .0747387     9.28   0.000      .545121    .8419961
             2695  |   .1127382   .0747387     1.51   0.135    -.0356994    .2611757
             2696  |  -.3584019   .0747387    -4.80   0.000    -.5068395   -.2099643
             2716  |   .0458851   .0747387     0.61   0.541    -.1025524    .1943227
             2751  |  -.3500189   .0747387    -4.68   0.000    -.4984565   -.2015813
             2759  |  -.3834961   .0747387    -5.13   0.000    -.5319337   -.2350586
             2765  |  -.6331525   .0747387    -8.47   0.000    -.7815901    -.484715
             2766  |  -.3862727   .0747387    -5.17   0.000    -.5347103   -.2378351
             2812  |  -.3920212   .0747387    -5.25   0.000    -.5404588   -.2435837
             2863  |  -.5079789   .0747387    -6.80   0.000    -.6564165   -.3595413
             3064  |  -.0702462   .0747387    -0.94   0.350    -.2186838    .0781914
             3408  |  -.3161484   .0747387    -4.23   0.000    -.4645859   -.1677108
             3447  |  -.0309224   .0747387    -0.41   0.680      -.17936    .1175152
             3461  |  -.2400258   .0747387    -3.21   0.002    -.3884633   -.0915882
             3510  |   .9880758   .0747387    13.22   0.000     .8396382    1.136513
             3591  |  -.6031023   .0747387    -8.07   0.000    -.7515399   -.4546647
             3598  |  -.2959608   .0747387    -3.96   0.000    -.4443984   -.1475233
             3675  |  -.3309831   .0747387    -4.43   0.000    -.4794207   -.1825455
             3797  |  -.2950603   .0747387    -3.95   0.000    -.4434979   -.1466227
             3857  |  -.2572535   .0747387    -3.44   0.001    -.4056911   -.1088159
             3997  |  -.6548469   .0747387    -8.76   0.000    -.8032845   -.5064093
             4027  |  -.3631889   .0747387    -4.86   0.000    -.5116265   -.2147513
             4130  |   -.323994   .0747387    -4.34   0.000    -.4724316   -.1755564
             4169  |   .2220552   .0747387     2.97   0.004     .0736176    .3704928
             4346  |  -.3002426   .0747387    -4.02   0.000    -.4486802    -.151805
             4463  |  -.3508526   .0747387    -4.69   0.000    -.4992902    -.202415
             4526  |  -.5285985   .0747387    -7.07   0.000    -.6770361   -.3801609
             4702  |  -.2785239   .0747387    -3.73   0.000    -.4269615   -.1300863
             4729  |   .2274483   .0747387     3.04   0.003     .0790107    .3758859
             4793  |  -.8981839   .0747387   -12.02   0.000    -1.046621   -.7497463
             4809  |   .0233454   .0747387     0.31   0.755    -.1250921     .171783
             4876  |   .9356782   .0747387    12.52   0.000     .7872406    1.084116
             4885  |  -.2644164   .0747387    -3.54   0.001     -.412854   -.1159788
             4962  |  -.5614433   .0747387    -7.51   0.000    -.7098809   -.4130057
             5106  |  -.4286973   .0747387    -5.74   0.000    -.5771349   -.2802597
             5124  |   -.324702   .0747387    -4.34   0.000    -.4731396   -.1762645
             5152  |  -.2739211   .0747387    -3.67   0.000    -.4223587   -.1254835
             5253  |  -.3397644   .0747387    -4.55   0.000     -.488202   -.1913268
             5271  |  -.2804312   .0747387    -3.75   0.000    -.4288688   -.1319937
             5281  |  -.3116242   .0747387    -4.17   0.000    -.4600618   -.1631867
             5282  |  -.5408064   .0747387    -7.24   0.000     -.689244   -.3923688
             5307  |  -.8391265   .0747387   -11.23   0.000    -.9875641   -.6906889
             5363  |   -.454757   .0747387    -6.08   0.000    -.6031946   -.3063195
             5495  |   .3650371   .0747387     4.88   0.000     .2165995    .5134747
             5518  |  -.6497876   .0747387    -8.69   0.000    -.7982252     -.50135
             5653  |  -.2650597   .0747387    -3.55   0.001    -.4134972   -.1166221
             5907  |  -.6053464   .0747387    -8.10   0.000     -.753784   -.4569088
             5946  |  -.2824016   .0747387    -3.78   0.000    -.4308392    -.133964
             6011  |  -.2313315   .0747387    -3.10   0.003    -.3797691   -.0828939
             6035  |   -.022574   .0747387    -0.30   0.763    -.1710116    .1258636
             6102  |    -.26037   .0747387    -3.48   0.001    -.4088076   -.1119324
             6183  |  -.5272745   .0747387    -7.05   0.000    -.6757121   -.3788369
             6199  |  -.1299688   .0747387    -1.74   0.085    -.2784064    .0184688
             6314  |  -.1947172   .0747387    -2.61   0.011    -.3431548   -.0462796
             6331  |  -.4574492   .0747387    -6.12   0.000    -.6058868   -.3090117
             6429  |  -.3294341   .0747387    -4.41   0.000    -.4778717   -.1809965
             6450  |  -.3470768   .0747387    -4.64   0.000    -.4955144   -.1986393
             6484  |  -.1840058   .0747387    -2.46   0.016    -.3324434   -.0355682
             6668  |   -.480616   .0747387    -6.43   0.000    -.6290536   -.3321784
             6687  |  -.2195136   .0747387    -2.94   0.004    -.3679512    -.071076
             6759  |   -.534215   .0747387    -7.15   0.000    -.6826526   -.3857774
             6808  |  -.3382743   .0747387    -4.53   0.000    -.4867119   -.1898367
             6816  |  -.5136693   .0747387    -6.87   0.000    -.6621069   -.3652317
             6825  |  -.4847557   .0747387    -6.49   0.000    -.6331933   -.3363181
             7098  |   .0711586   .0747387     0.95   0.344     -.077279    .2195962
             7168  |  -.3845569   .0747387    -5.15   0.000    -.5329945   -.2361194
             7175  |  -.1488839   .0747387    -1.99   0.049    -.2973215   -.0004464
             7351  |  -.3287382   .0747387    -4.40   0.000    -.4771758   -.1803006
             7381  |  -.6923947   .0747387    -9.26   0.000    -.8408323   -.5439571
             7411  |  -.0169957   .0747387    -0.23   0.821    -.1654333    .1314419
             7739  |  -.3678238   .0747387    -4.92   0.000    -.5162614   -.2193862
             7848  |   -.557927   .0747387    -7.47   0.000    -.7063646   -.4094894
                   |
             _cons |   .3300108   .0528483     6.24   0.000     .2250496     .434972

[Clyde Schechter]

I would just advise you to scrap this model. You have 89 predictors and 192 observations It's useless.

Even if you had a much larger corpus of data, it doesn't really make sense to model time as 89 discrete time points. No one will ever be able to make any sense out of the results. Better is to treat time as a continuous variable. Now, you need to pick an appropriate way to do that. Is your outcome likely to be linearly related to the time interval? Or some curvilinear relationship? U-shaped? Undulating with several peaks and valleys? Piecewise linear?

If there is no theory to guide this, then I would start with -lowess delta_log_price days_between_sales- and see what the smoothed relationship looks like, and then pick a specification for your regression that would reasonably capture that.

There is another problem here. You have reduced the data to matched pairs (same house before and after), but you are including both members of each matched pair in the analysis even though the variables are the same for both members! So all of your data is double counted. So you need to do:

[code]
egen flag = tag(HOUSE_ID)
regress delta_log_price days_between_sales_or_whatever_transform(s)_you_se ttle_on if flag
[code]
so as to only represent each pair once.

Once you get a simpler model and correct the estimation sample in these ways, I think you will have a much more tractable set of results to try to interpret.

I would ignore the sums of squares and mean squares. They are of interest in ANOVA, but this is a regression model with continuous variables; the SS and MS statistics don't really help understand what's going on in this context and are primarily of historical interest.

[Guest]

Thanks, i managed to get the time format fixed by saving my excel file as an older version of excel, which state then accepted.
Thanks for all your help Clyde, it has been very insightful!
You can now close the thread.