MultiIndex slicing with .loc returns wrong results
#### A small, complete example of the issue

``` python
import pandas

idx = pandas.date_range("2012", "2016", freq="D")
df = pandas.concat([
                       pandas.DataFrame(1, index=idx, columns=[ctry]) for ctry in ["BE", "FR", "SP"]
                       ],
                   axis=1).stack().to_frame()

df.index = df.index.swaplevel(0, 1)
df = df.sortlevel()
df.info()
print()
df_ok = df.loc[pandas.IndexSlice[("SP", "FR"), "2013-01-01":"2014-01-01"], :]
print()
# here, the time indexation is not properly handled as dates < 2013-01-01 are kept
df_not_ok=df.loc[pandas.IndexSlice[("SP", "BE", "FR"), "2013-01-01":"2014-01-01"], :]
```
#### Expected Output

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 4386 entries, (BE, 2012-01-01 00:00:00) to (SP, 2016-01-01 00:00:00)
Data columns (total 1 columns):
0    4386 non-null int64
dtypes: int64(1)
memory usage: 51.4+ KB

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 732 entries, (FR, 2013-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0    732 non-null int64
dtypes: int64(1)
memory usage: 8.6+ KB

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 3290 entries, (BE, **2013**-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0    **2196** non-null int64
dtypes: int64(1)
memory usage: 38.6+ KB
#### Actual output

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 4386 entries, (BE, 2012-01-01 00:00:00) to (SP, 2016-01-01 00:00:00)
Data columns (total 1 columns):
0    4386 non-null int64
dtypes: int64(1)
memory usage: 51.4+ KB

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 732 entries, (FR, 2013-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0    732 non-null int64
dtypes: int64(1)
memory usage: 8.6+ KB

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 3290 entries, (BE, **2012**-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0    **3290** non-null int64
dtypes: int64(1)
memory usage: 38.6+ KB
#### Output of `pd.show_versions()`

<details>
# Paste the output here

INSTALLED VERSIONS

commit: None
python: 2.7.11.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
</details>
