DatetimeIndex construction with periods and closed
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

dti1 = pd.DatetimeIndex(start="2018-10-16", periods=12, freq="H", closed="right")
print(dti1)

dti2 = pd.DatetimeIndex(end="2018-10-16", periods=12, freq="H", closed="left")
print(dti2)
```
Outputs:
```python
DatetimeIndex(['2018-10-16 01:00:00', '2018-10-16 02:00:00',
               '2018-10-16 03:00:00', '2018-10-16 04:00:00',
               '2018-10-16 05:00:00', '2018-10-16 06:00:00',
               '2018-10-16 07:00:00', '2018-10-16 08:00:00',
               '2018-10-16 09:00:00', '2018-10-16 10:00:00',
               '2018-10-16 11:00:00'],
              dtype='datetime64[ns]', freq='H')
DatetimeIndex(['2018-10-15 13:00:00', '2018-10-15 14:00:00',
               '2018-10-15 15:00:00', '2018-10-15 16:00:00',
               '2018-10-15 17:00:00', '2018-10-15 18:00:00',
               '2018-10-15 19:00:00', '2018-10-15 20:00:00',
               '2018-10-15 21:00:00', '2018-10-15 22:00:00',
               '2018-10-15 23:00:00'],
              dtype='datetime64[ns]', freq='H')
```
#### Problem description

When explicitly asking for a number of periods, an index of that size is expected. By using `closed` with the matching boundary specified (`start` for `closed="right"` and `end` for `closed="left"`) we get one less than the requested periods.

Probably this also happens with other functions/methods that use the `closed` argument and specify the number of items desired.

#### Expected Output
```python
DatetimeIndex(['2018-10-16 01:00:00', '2018-10-16 02:00:00',
               '2018-10-16 03:00:00', '2018-10-16 04:00:00',
               '2018-10-16 05:00:00', '2018-10-16 06:00:00',
               '2018-10-16 07:00:00', '2018-10-16 08:00:00',
               '2018-10-16 09:00:00', '2018-10-16 10:00:00',
               '2018-10-16 11:00:00', '2018-10-16 12:00:00'],
              dtype='datetime64[ns]', freq='H')
DatetimeIndex(['2018-10-15 12:00:00', '2018-10-15 13:00:00',
               '2018-10-15 14:00:00', '2018-10-15 15:00:00',
               '2018-10-15 16:00:00', '2018-10-15 17:00:00',
               '2018-10-15 18:00:00', '2018-10-15 19:00:00',
               '2018-10-15 20:00:00', '2018-10-15 21:00:00',
               '2018-10-15 22:00:00', '2018-10-15 23:00:00'],
              dtype='datetime64[ns]', freq='H')
```

#### Output of ``pd.show_versions()`` (short version)
```
python: 3.6.4.final.0

pandas: 0.23.4
```