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RETURN

RETURN

RETURN for Module 4 overall significance and predictability.

Download DTA

Structure

cross-sectional

File type

DTA

Module

Course catalog

Visibility

public

Preview table

Preview rows are shown when safely installed in the app. External or licensed files may need to be opened from the source link.

No local preview table is installed for this dataset. Use the download/source link, then load it with the Python example below.

Python loading code

import pandas as pd

# Download RETURN from the dataset link first.
data = pd.read_stata("RETURN.DTA")
print(data.head())

Check the variable dictionary and source documentation before dropping or recoding missing values.

Dataset AI assistant

Get help with variables, graphs, regression questions, limitations, and Python loading code.

Variables dictionary

returnnumeric

return used for Module 4 overall significance and predictability.

Missing values: Review source documentation before recoding missing values.

return_1numeric

return_1 used for Module 4 overall significance and predictability.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.RETURN is catalogued for Module 4 overall significance and predictability. Open the linked Drive source to install the file before validating output.
  2. 2.Create one summary table and one graph that support an econometric question.
  3. 3.Write one limitation about measurement, missing data, or omitted variables.