BUG: Promote discrete covariance inputs before products - #942

The kernel covariance estimators, cov_nw and add_trend accept complex data. The
estimators are Hermitian: every product uses the conjugate transpose, so that the lag-j
autocovariance is the sum of x[t] times the conjugate of x[t - j], the long-run
covariance is Gamma_0 + sum of w_j (Gamma_j + Gamma_j^H) and the estimates are
complex128 matrices that are exactly Hermitian, with a real diagonal.

* The automatic bandwidth combines the columns with the (real) weights and uses the real
  part of the autocovariance of the complex series, since the sums over j and -j add a
  value and its conjugate. The product v[t] * v[t - j] of the earlier code is the
  pseudo-covariance, which is not what the bandwidth formula needs.
* cov_nw returns a real float for a 1-d complex series, and a complex Hermitian matrix
  otherwise.
* add_trend adds the real trend columns to complex data and returns complex128. Constant
  columns of complex data are detected.
* PreWhitenedRecolored raises a TypeError for complex data. A VAR for complex data is
  not one model: a VAR with complex coefficients cannot represent real and imaginary
  parts that depend on each other in a way that is not a complex multiplication, the VAR
  of the stacked real and imaginary parts is a different model with more parameters,
  and the order that the information criteria select depends on which is used. The
  Notes say to use the stacked data or a kernel estimator.
* The weights of the bandwidth are real.

The estimates for float32 data are float64, and for complex64 data they are complex128.
The tests of antonsoo's last commit (bashtage#942) expected float32 to be preserved, which the
estimators did not do consistently (short_run was float32 and long_run float64) and the
float32 estimates are not accurate enough, and expected the complex estimates to be the
products without the conjugate. test_preserve_floating_kernel_dtype and
test_preserve_inexact_newey_west are replaced by tests of the same values with float64
and complex128 dtypes and the conjugate transpose.

The reference for the tests is independent of the complex arithmetic. For z = a + ib the
Hermitian long-run covariance of the estimators equals S_aa + S_bb + i (S_ba - S_ab),
where S is the long-run covariance of the real data [a, b] with the same kernel,
bandwidth, centering and degrees of freedom, and the automatic bandwidth equals the one
computed from the sums of the autocovariances of the real and imaginary parts. The
tests cover all ten kernels, every estimate (long run, short run, one sided, one sided
strict), centering and degrees of freedom, a rotation of the data (Hermitian estimates
are invariant to it, the pseudo-covariance is not), real data stored as complex, labels
of a DataFrame, and an exact rational reference for float32 data with a large mean. They
pass with and without numba.

contributing.rst: rule 5 of the input validation section points to a new "Data types"
section, which has the table of data types by the default and by allow_complex, why
float32 is promoted, why float16 is rejected, where complex data are accepted and why
the others are not, and how to add complex support to a function and to test it against
the real and imaginary parts.

9.0.rst: the entry about the conversion of integer, unsigned integer and Boolean data
states the rules for all data types and credits antonsoo for the first float32 and
complex support of the covariance estimators (bashtage#942, bashtage#943), and a new entry in the new
features describes the Hermitian covariance estimators, add_trend and the bootstraps.