StataReader selectively ignores format details
Code Sample, a copy-pastable example if possible

[link to current commit](https://github.com/pandas-dev/pandas/blob/22515f58c178cdb5cd38c4e56f26dc91c7053550/pandas/io/stata.py#L1032-L1034)

```python
    def _read_header(self):
        # ...
        # remove format details from %td
        self.fmtlist = ["%td" if x.startswith("%td") else x
                        for x in self.fmtlist]
```

which allows for this check to succeed when format details are present

```python
def _stata_elapsed_date_to_datetime_vec(dates, fmt):
    # ...
    elif fmt in ["%td", "td", "%d", "d"]:  # Delta days relative to base
        # ...
```

#### Problem description

It looks like `pandas` is attempting to _the right thing_ by dropping format codes from `%td` formats so that they will [match the correct format](https://github.com/pandas-dev/pandas/blob/22515f58c178cdb5cd38c4e56f26dc91c7053550/pandas/io/stata.py#L321) in `_stata_elapsed_date_to_datetime_vec`

I handle a large number of Stata files and arbitrarily supporting display formats with only `%td` is problematic.

#### Expected Output

Ideally none of these four assert statements would fail

```python
import datetime as dt
import pandas

# File attached below
d1, d2, d3, d4 = pandas.read_stata('STATA_DATES.DTA').iloc[0]

expected = dt.datetime(2006, 11, 21)

assert d1 == expected  # fails    - format is %dD_m_Y
assert d2 == expected  # succeeds - format is %tdD_m_Y
assert d3 == expected  # fails    - format is %tcD_m_Y
assert d4 == expected  # succeeds - format is %tc
````

#### Proposed Fixes

1. Strip formats from other types by adding extra list comprehensions in `_read_header`. My current workaround is to subclass `StataReader` and add this logic after `_read_header`.

1. Use `str.startswith`'s lesser-known tuple argument
   In `_stata_elapsed_date_to_datetime_vec` change lines like
  `elif fmt in ["%td", "td", "%d", "d"]:  # Delta days relative to base`
   to
   `elif fmt.startswith(("%td", "td", "%d", "d")):  # Delta days relative to base`
   this obviates the need for the list comprehensions in the first place.

#### Output of ``pd.show_versions()``

<details>
>>> pandas.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.12.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None

</details>

#### Sample Files
[STATA_DATES.DTA.zip](https://github.com/pandas-dev/pandas/files/1360608/STATA_DATES.DTA.zip) the DTA file used for the *Expected Output*. It's a single row with 4 values


![stata date types](https://user-images.githubusercontent.com/26555712/31249289-a4652626-a9cb-11e7-927c-b822edc723d0.png)

The file was generated from this CSV file with the following commands. 
[stata_dates.csv.zip](https://github.com/pandas-dev/pandas/files/1360647/stata_dates.csv.zip)
```
load ~/tmp/stata_dates.csv
format date_d %dD_m_Y
format date_td %tdD_m_Y


format datetime_format %tcD_m_Y
format datetime_noformat %tc
save ~/tmp/STATA_DATE_D_TD.DTA
```