# asreg: Get standard errors of the first stage regression of the Fama and MacBeth (1973) Procedure in Stata

## asreg: Get standard errors of the first stage regression of the Fama and MacBeth (1973) Procedure in Stata

Category:Uncategorized

In the following example, we shall use asreg that can be installed from SSC by typing the following line in Stata command window

`ssc install asreg`

## The problem

Let’s say that we wish to report different regression statistics from Fama and MacBeth (1973) regression such the standard errors of variables. Using the fmb option, asreg can efficiently estimate FMB regression. Further, it reports the regression coefficients of the first stage regression when option first is used with the option fmb.  However, it does not report other regression statistics.

## The solution

The good news is that we can still find different regression components using asreg. Since the first stage regression of the FMB procedure is the cross-sectional regression, we can use the bysort period prefix with asreg.

## An example

Let us use the grunfeld data and estimate the FMB regression in the usual manner.

`webuse grunfeld, clearasreg invest mvalue kstock, fmb first`

#### First stage Fama-McBeth regression results

``````  +------------------------------------------------------------+
| _TimeVar   _obs       _R2   _b_mva~e   _b_kst~k      _Cons |
|------------------------------------------------------------|
|     1935     10   .865262    .102498   -.001995    .356033 |
|     1936     10   .696394    .083707   -.053641    15.2189 |
|     1937     10   .663763    .076514    .217722   -3.38647 |
|     1938     10   .705577    .068018    .269115   -17.5819 |
|     1939     10   .826602    .065522    .198665   -21.1542 |
|     1940     10   .839255    .095399    .202291   -27.0471 |
|     1941     10   .856215    .114764    .177465   -16.5195 |
|     1942     10   .857307    .142825    .071024   -17.6183 |
|     1943     10   .842064     .11861    .105412   -22.7638 |
|     1944     10   .875515    .118164    .072207   -15.8281 |
|     1945     10   .906797    .108471    .050221   -10.5197 |
|     1946     10   .894752    .137948    .005413   -5.99066 |
|     1947     10   .891239    .163927   -.003707   -3.73249 |
|     1948     10   .788823    .178667   -.042556    8.53881 |
|     1949     10   .863257    .161596   -.036965    5.17829 |
|     1950     10   .857714    .176217   -.022096   -12.1747 |
|     1951     10   .873773    .183141   -.112057    26.1382 |
|     1952     10   .846122    .198921   -.067495    7.29284 |
|     1953     10   .889261    .182674    .098753   -50.1525 |
|     1954     10    .89845    .134512    .331375   -133.393 |
|---------------------------------------------------------
Mean | 1944.5    10    .836907   .130605    .072958    -14.757 |
+------------------------------------------------------------+``````
```Fama-MacBeth (1973) Two-Step procedure           Number of obs     =       200
Num. time periods =        20
F(  2,    19)     =    195.04
Prob > F          =    0.0000
avg. R-squared    =    0.8369
------------------------------------------------------------------------------
|            Fama-MacBeth
invest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue |   .1306047   .0093422    13.98   0.000     .1110512    .1501581
kstock |   .0729575   .0277398     2.63   0.016     .0148975    .1310176
_cons |  -14.75697   7.287669    -2.02   0.057    -30.01024     .496295
------------------------------------------------------------------------------
```

## An alternate way to first-stage

`bys year: asreg invest mvalue kstock, sebys year: keep if _n == _Nlist _*`
`+-------------------------------------------------------------------------------------------------------------+| year   _Nobs         _R2      _adjR2   _b_mvalue    _b_kstock      _b_cons   _se_mv~e   _se_ks~k   _se_cons ||-------------------------------------------------------------------------------------------------------------|| 1935      10   .86526202   .82676546   .10249786   -.00199479    .35603339   .0157931   .2148591   23.82794 || 1936      10   .69639369   .60964903   .08370736   -.05364126    15.218946   .0211982   .4125528   49.72796 || 1937      10    .6637627   .56769491    .0765138    .21772236   -3.3864706   .0218952   .4745161   62.14382 || 1938      10   .70557727   .62145649   .06801777    .26911462   -17.581903   .0220019   .2076121   33.62243 || 1939      10   .82660153   .77705911   .06552194    .19866456   -21.154227   .0131751   .1563955   29.10151 ||-------------------------------------------------------------------------------------------------------------|| 1940      10   .83925512   .79332801     .095399    .20229056   -27.047068   .0171077   .2206074   42.49812 || 1941      10   .85621485   .81513338   .11476375    .17746501   -16.519486   .0197202   .2338307   47.43406 || 1942      10   .85730699   .81653756   .14282513    .07102405   -17.618283   .0246973   .1966943   43.85369 || 1943      10   .84206394   .79693935   .11860951    .10541193   -22.763795   .0207092   .1887016    46.8604 || 1944      10   .87551498   .83994783   .11816422    .07220719   -15.828145   .0169881   .1537212   41.84578 ||-------------------------------------------------------------------------------------------------------------|| 1945      10   .90679731   .88016797    .1084709    .05022083   -10.519677   .0133214   .1254533   35.10524 || 1946      10   .89475165    .8646807   .13794817    .00541339   -5.9906571    .018637   .1600683   45.73243 || 1947      10   .89123943   .86016498   .16392696   -.00370721   -3.7324894   .0280743   .1285463   37.80575 || 1948      10    .7888235   .72848735    .1786673   -.04255555    8.5388099   .0463983   .1661775   52.39133 || 1949      10   .86325678   .82418728   .16159617   -.03696511    5.1782863   .0346516   .1268614   41.07802 ||-------------------------------------------------------------------------------------------------------------|| 1950      10   .85771384   .81706065   .17621675   -.02209565    -12.17468   .0393216   .1361792    46.6222 || 1951      10   .87377295   .83770808   .18314051   -.11205694    26.138157   .0358898   .1486738   53.00348 || 1952      10   .84612242   .80215739   .19892081   -.06749499    7.2928402    .052286   .1906835   67.84544 || 1953      10   .88926056   .85762072   .18267385    .09875335   -50.152546    .058579   .2164437   77.91569 || 1954      10   .89845005   .86943578   .13451162    .33137459   -133.39308   .0704524   .1932826   76.18067 | +-------------------------------------------------------------------------------------------------------------+`

## Explanation

In the above lines of code, we estimated a yearly cross-sectional regression with the option se to report the standard errors. Then we retained just one observation per year and deleted duplicates. The results are the same as reported by the option first in the fmb regression, with the only difference that we have now additional regression statistics.