MultiIndex with DatetimeIndex indexing loses nanosecond precision
####  ```MultiIndex``` loses nanosecond precision

I have a dataframe where one column is ```dtype: datetime64[ns]```. This column has nanosecond precision.
I want to make this column, ```'nanosecond_dt'``` the index of my dataframe. I can do this no problem for a simple index:
```
new_df = df.set_index('nanosecond_dt')
ns_part_of_index = new_df.index.nanosecond

```
inspect ```ns_part_of_index``` to check nanos are there
```
array([872, 128, 872, 128], dtype=int32)
```
Clearly the new DatetimeIndex still has nanosecond precision.

I actually want to make the column ```'nanosecond_dt'``` AND another string based column, ```'name'```, into a MultiIndex for my dataframe. 

Unfortunately this means the ```DatetimeIndex``` part of the MultiIndex does *not* have nanosecond resolution. Why? Is this a known problem? 
Or am I making a mistake somewhere?

```
new_multi_index_df = df.set_index(['nanosecond_dt','name'])
ns_part_of_index = new_multi_index_df.index.get_level_values('nanosecond_dt')
```

now inspect ```ns_part_of_index``` for this MultiIndex:
```
array([0, 0, 0, 0], dtype=int32)
```

Unfortunately, the new DatetimeIndex as part of the MultiIndex has *lost* nanosecond precision.


Pandas  0.18.0
