econkit 0.4.1.9.1

Advanced Econometric Analysis Tools

econkit

econkit is a Python library that provides various statistical and econometric analysis tools, including descriptive statistics, correlation matrices, and tests for stationarity and autocorrelation.

Installation

Ensure you have the required packages installed:

pip install pandas numpy scipy statsmodels tabulate yfinance

Functions

Descriptive Statistics

descriptives(data)

Computes descriptive statistics for each numeric column in a DataFrame.

Parameters:

• data: pandas.DataFrame containing the data to be analyzed.

Returns:

• None. Prints a summary table of the descriptive statistics.

Example Usage:

import pandas as pd
from econkit import econometrics

df = pd.read_csv('your_data.csv')
econometrics.descriptives(df)

Correlation Matrix

correlation(df, method='Pearson', p=False)

Calculates and prints the correlation matrix and p-values for numeric columns in the provided DataFrame. Supports Pearson, Spearman, and Kendall correlation methods.

Parameters:

• df: pandas.DataFrame containing the data to be analyzed.
• method: str (optional). Method of correlation ('Pearson', 'Spearman', or 'Kendall'). Default is 'Pearson'.
• p: bool (optional). If True, p-value matrix is also printed; if False, only the correlation matrix is printed. Default is False.

Returns:

• None. Prints the correlation matrix and optionally the p-value matrix.

Example Usage:

import pandas as pd
from econkit import econometrics

df = pd.read_csv('your_data.csv')
econometrics.correlation(df, method='Spearman', p=True)

Augmented Dickey-Fuller (ADF) Test

adf(dataframe, maxlag=None, regression='c', autolag='AIC', handle_na='drop')

Performs the ADF test on each column in the DataFrame and returns a summary table.

Parameters:

• dataframe: pandas.DataFrame containing the data to be tested.
• maxlag: int (optional). Maximum number of lags to use. Default is None.
• regression: str {'c', 'ct', 'ctt', 'nc'} (optional). Type of regression trend. Default is 'c'.
• autolag: str (optional). Method to use when automatically determining the lag length ('AIC', 'BIC', 't-stat'). Default is 'AIC'.
• handle_na: str {'drop', 'fill'} (optional). How to handle missing values. Default is 'drop'.

Returns:

• None. Prints a summary table of the ADF test results.

Example Usage:

import pandas as pd
from econkit import econometrics

df = pd.read_csv('your_data.csv')
econometrics.adf(df, regression='ct', autolag='BIC')

KPSS Test

kpss_test(dataframe, regression='c', nlags='auto', handle_na='drop')

Performs the KPSS test on each column in the DataFrame and returns a summary table.

Parameters:

• dataframe: pandas.DataFrame containing the data to be tested.
• regression: str {'c', 'ct'} (optional). Type of regression trend. Default is 'c'.
• nlags: str or int (optional). Number of lags to use. Default is 'auto'.
• handle_na: str {'drop', 'fill'} (optional). How to handle missing values. Default is 'drop'.

Returns:

• None. Prints a summary table of the KPSS test results.

Example Usage:

import pandas as pd
from econkit import econometrics

df = pd.read_csv('your_data.csv')
econometrics.kpss_test(df, regression='ct', nlags='auto')

Durbin-Watson Test

dw(data)

Performs the Durbin-Watson autocorrelation test and Ljung-Box test for each column of the dataset.

Parameters:

• data: pandas.DataFrame where each column is a time series.

Returns:

• None. Prints a summary table of the Durbin-Watson test results.

Example Usage:

import pandas as pd
from econkit import econometrics

df = pd.read_csv('your_data.csv')
econometrics.dw(df)

Financial Data Retrieval

data(ticker_symbol, start_date, end_date, interval)

Downloads stock data from Yahoo Finance and calculates daily returns.

Parameters:

• ticker_symbol: str. The stock ticker symbol.
• start_date: str. Start date in 'dd-mm-yyyy' format.
• end_date: str. End date in 'dd-mm-yyyy' format.
• interval: str. Data interval (e.g., '1d', '1wk', '1mo').

Returns:

• pandas.DataFrame containing the stock data and calculated returns.

Example Usage:

from econkit import finance

data = finance.data('AAPL', '01-01-2020', '31-12-2020', '1d')
print(data)

Usage Notes

• Ensure your data is clean and properly formatted before using these functions.
• Some functions handle missing values; specify your preferred method using the handle_na parameter.
• For time series analysis, ensure your data is indexed by date.

For more details, refer to the function docstrings or the examples provided above.