## Basic Syntax

1. The basic syntax of reghdfe is the same as areg. Thus,

. areg depvar indepvars, absorb(absvar)

Becomes:

. reghdfe depvar indepvars, absorb(absvar1 absvar2 …)

2. With IV/GMM regressions, use the ivregress and ivreg2 syntax:

. reghdfe depvar indepvars (endogvars=iv_vars), absorb(absvars)

3. Similarly, for robust standard errors:

. reghdfe depvar indepvars , absorb(absvars) vce(robust)

. reghdfe depvar indepvars , absorb(absvars) vce(cluster clustervars)

## Examples

### Estimate an OLS Regression with two levels of fixed effects

``````. sysuse auto
(1978 Automobile Data)

. reghdfe price weight length, absorb(turn trunk)
(dropped 9 singleton observations)
(converged in 12 iterations)

HDFE Linear regression                            Number of obs   =         65
Absorbing 2 HDFE groups                           F(   2,     38) =      12.85
Prob > F        =     0.0001
R-squared       =     0.6403
Within R-sq.    =     0.4035
Root MSE        =  2254.6288

------------------------------------------------------------------------------
price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight |   5.688144   1.302818     4.37   0.000     3.050727    8.325561
length |  -44.21191   57.60797    -0.77   0.448    -160.8332    72.40933
-------------+----------------------------------------------------------------
Absorbed |         F(24, 38) =      1.548   0.110             (Joint test)
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     |
-------------+-------------------------------------------------|
turn |           13              13              0     |
trunk |           12              13              1     |
---------------------------------------------------------------+
``````

### Estimate an OLS Regression with two-way clustering

``````. reghdfe price weight length, absorb(turn trunk) vce(cluster turn trunk)
(dropped 9 singleton observations)
(converged in 12 iterations)

HDFE Linear regression                            Number of obs   =         65
Absorbing 2 HDFE groups                           F(   2,     12) =      16.59
Statistics robust to heteroskedasticity           Prob > F        =     0.0004
R-squared       =     0.6403
Number of clusters (turn)    =         13         Within R-sq.    =     0.4035
Number of clusters (trunk)   =         13         Root MSE        =  2284.8937

(Std. Err. adjusted for 13 clusters in turn trunk)
------------------------------------------------------------------------------
|               Robust
price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight |   5.688144   2.837967     2.00   0.068    -.4952552    11.87154
length |  -44.21191   87.68177    -0.50   0.623    -235.2541    146.8302
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     |
-------------+-------------------------------------------------|
turn |            0              13             13 *   |
trunk |            0              13             13 *   |
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation
``````

### Estimate an IV regression

``````. reghdfe price weight (length = gear), absorb(turn trunk)
(dropped 9 singleton observations)
(converged in 12 iterations)

HDFE IV (2SLS) estimation
-------------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

Number of obs =       65
F(  2,    38) =    12.22
Prob > F      =   0.0001
Total (centered) SS     =  323844734.5                Centered R2   =   0.6293
Total (uncentered) SS   =  323844734.5                Uncentered R2 =        .
Residual SS             =    199090229                Root MSE      =     2289

------------------------------------------------------------------------------
price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
length |  -106.3953   407.0322    -0.26   0.795    -930.3889    717.5984
weight |   6.578179   5.915196     1.11   0.273    -5.396509    18.55287
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):           1.342
Chi-sq(1) P-val =    0.2467
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                0.801
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
15% maximal IV size              8.96
20% maximal IV size              6.66
25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
(equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         length
Included instruments: weight
Excluded instruments: gear_ratio
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     |
-------------+-------------------------------------------------|
turn |           13              13              0     |
trunk |           12              13              1     |
---------------------------------------------------------------+
``````

If you also want the first stage or the OLS version of this regression, check out the `stages(...)` option (which also supports the reduced form and the “acid” version).