*! version 0.0 2may2020

Title

manyweakiv --
implements the weak-identification robust jackknife AR test from Mikusheva and Sun (2022).
The companying command manyweakivpretest implements a new pre-test that is
analogous to analogous to that of Stock and Yogo (2005) first stage F test,
but robust to many instruments and heteroscedasticity.

Syntax

manyweakivtest

(x =
instr)
[covariates] [if] [in]
[weight] ,
[options noconstant ]

manyweakivpretest

(x =
instr)
[covariates] [if] [in]
[weight] ,
[options noconstant ]

where x is a scalar endogeneous variable.

options

Must specify
instr specifies the list of (excluded) instruments.

Optional
noconstant specifies whether an intercept is included (default includes an intercept).

covariates specifies the list of controls, i.e., included instruments.

Saved Output

manyweakivtest reports the jackknife AR confidence interval via analytical test inversion.
manyweakivpretest reports the many-instruments F test.
In addition, it stores the following in e():

Scalar
r(F) the many-instruments F statistic

Description

In empirical applications using instrumental variables, the current consensus
practice is to report the first stage F statistic and as long as it is above 10,
researchers are allowed to rely on standard t-statistics inferences.
This practice has foundations in Stock and Yogo (2005) which showed that the
concentration parameter fully characterizes the size distortion of the TSLS-Wald test,
and empirically the concentration parameter can be judged based on the first stage F statistics.
This result has been obtained under the assumptions of homoscedasticity and
for a fixed number of instruments.

Mikusheva and Sun (2022) introduces a new F test that is valid under heteroscedasticity and many instruments. Based on the result of this new F test (implemented in manyweakivpretest), applied researchers can switch between the 5% JIVE t-statistic or 5% jackknife AR test (implemented in manyweakivtest) with the caveats analogous to Stock and Yogo (2005): Namely, the size of the two-step procedure are bounded within 15%.

Examples

Simulate group instruments.

. clear all

. set obs 100

. gen random = uniform()

. gen group = 0

. local k = 10

forval j = 1/`k' {
replace group = `j' if random > (`j'-1)/`k' & random < (`j')/`k'
}

. tab group, gen(g_)

. gen v = rnormal()

. gen w = rnormal()

. gen x = 1 + w + v

. gen y = 1*x + w + 0.5*v

We use many-instrument F test to assess instruments' strength. As expected we have weak instruments.

. manyweakivpretest y (x = g_*) w

Simulate the outcome for illustrating the jackknife AR test, which as expected return unbounded confidence interval.

. manyweakivtest y (x = g_*) w

Acknowledgements

Thank you to the users of early versions of the program who devoted time to reporting
the bugs that they encountered.

References

Anna Mikusheva, Liyang Sun, Inference with Many Weak Instruments, The Review of Economic Studies, Volume 89, Issue 5, October 2022, Pages 2663–2686, Preprint.

Mikusheva, A. and Sun, L. 2023.
Weak Identification with Many Instruments. arXiv:2308.09535 [econ.EM]

Citation and Installation of manyweakiv

manyweakiv is not an official Stata command. It is a free contribution
to the research community, like a paper. Please cite it as such:

Sun, L., 2023.
manyweakiv: weak-instruments robust test for linear IV regressions with many instruments.
https://github.com/lsun20/manyweakiv.

manyweakiv can be installed easily via the github package, which is available on
https://github.com/haghish/github.

. github install lsun20/manyweakiv

. github update manyweakiv

Author

Liyang Sun

liyang.sun@ucl.ac.uk