I develop three new types of vector autoregressions that use supervised
machine learning models to estimate coefficients in place of ordinary least
squares. I use these models to estimate the effects of monetary policy on the
real economy. Overall, I find that the machine learning vector autoregressions
produce impulse responses that are well behaved and similar to their ordinary
least squares counterparts. In practice, the machine learning vector autoregressions
produce more conservative estimates than the traditional ordinary
least squares vector autoregressions. Additionally, I establish a simulation
scheme to compare the relative efficiency of impulse responses generated from
machine learning and ordinary least squares vector autoregressions. To calculate
condence intervals, I use a bias corrected bootstrapping method from
Politis and Romano (1994) called the stationary bootstrap. In future work, I
intend to compare these impulse responses using simulated data from Killian
and Kim (2011)
