Development Tools & Resources

The example code demonstrating the use case is based on the following development tools and resources:

• Refinitiv's DataStream Web Services (DSWS): Access to DataStream data. A DataStream or Refinitiv Workspace IDentification (ID) will be needed to run the code bellow.
• Python Environment: Tested with Python 3.7
• Packages: DatastreamDSWS, Numpy, Pandas, statsmodel, scipy and Matplotlib. The Python built in modules warnings, statistics, datetime and dateutil are also required.

Tutorial Video Part 1 - Introduction to the Theory, Codebook and Python Libraries

Import libraries

This code runs on Python version 3.7.4

We need to gather our data. Since Refinitiv's DataStream Web Services (DSWS) allows for access to the most accurate and wholesome end-of-day (EOD) economic database (DB), naturally it is more than appropriate. We can access DSWS via the Python library "DatastreamDSWS" that can be installed simply by using pip install.

We use Refinitiv's Eikon Python Application Programming Interface (API) to access financial data. We can access it via the Python library "eikon" that can be installed simply by using pip install.

The following are Python-built-in module/library, therefore it does not have a specific version number.

statistics, numpy, scipy and statsmodels are needed for datasets' statistical and mathematical manipulations

The numpy library imported in this code is version: 1.16.5
The statsmodels library imported in this code is version: 0.10.1
The scipy library imported in this code is version: 1.3.1

pandas will be needed to manipulate data sets

The pandas library imported in this code is version: 0.25.1

matplotlib is needed to plot graphs of all kinds

The matplotlib library imported in this code is version: 3.1.1

Setup Original Functions

Tutorial Video Part 2 - Introduction to Some Python Fundamentals and Our First Set of Functions

Setup data sets

The cell below defines a function that translates DataStream data into a shape we can work with: a Pandas Dafaframe normalised to the 1st of the month that it was collected in.

The cell below defines a function that appends our monthly data-frame with chosen data

Setup plot functions

The cell below defines a function to plot data on one y axis.

The cell below defines a function to plot data on two y axis.

The cell below defines a function to plot data regressed on time.

Setup Statistics Tools

The cell below defines a function that creates and displays a table of statistics for all vectors (columns) within any two specified datasets.

Monthly Data

U.S.A.'s Consumption Data

Tutorial Video Part 3 - Collect Data and Our Second Set of Functions

Quarterly Data

U.S.A.'s Investment Data

U.S.A.'s Government Investment

U.S.A.'s Net Exports of Goods and Services

Tutorial Video Part 4 - Dataframe manipulations and Plots

G.D.P. Component Sum

U.S.A.'s G.D.P.

To check our model, we can use 'United States, National Product Account, Gross Domestic Product, Overall, Total, Current Prices, AR, SA, USD'.

Data Résumé

Note that the GDP and GDP Component Sum datapoints align so well that they are indistinguishable in the graph above

Tutorial Video Part 5 - The Holt Winters Model for Positive Values

Tutorial Video Part 6 - The Holt Winters Model for Negative Values

Holt-Winters (HW) method

Creating a function to streamline use of Holt-Winters model

U.S.A.'s Investments' Estimates using the HW method

Investment forecasts:

2020-05-01 3.666572e+12
2020-06-01 3.664588e+12
2020-07-01 3.662814e+12
Freq: MS, dtype: float64

U.S.A.'s Government Investment using the HW method

Government Investment forecasts:

2020-05-01 3.866815e+12
2020-06-01 3.876213e+12
2020-07-01 3.885344e+12
Freq: MS, dtype: float64

U.S.A.'s Net Exports of Goods and Services using the HW method

df with Holt-Winters estimates

Tutorial Video Part 7 - Backtesting our Method

The Setting for this video has changed, I do apologize for the lighting!

Backtesting our Method

The Python function defined in the cell below returns the In-Sample Recursive Prediction values of ' data ' using the Holt-Winters exponential smoother Plus it then provides forecasts for One Out-Of-Sample period (thus the acronym 'ISRPHWPOOOF'):

We will back-test our models from January 1990

Backtesting our Method with Investment data

In the graph above, we put ourselves in the shoes of an analyst who only has quarterly Investment data. She/He uses the latest quarterly releases of Investment data which spans from 1950 (in our example) to last quarter (last month at the earliest). She/He then uses the HW model to compute a forecast for the current period (month) - in yellow in the graph above.

Backtesting our Method with Government Investment data

Backtesting our Method with Net Exports data

G.D.P. Montly Estimates

Conclusion

It is possible to estimate monthly US GDP figures using the HW model to a good degree of accuracy. The final forecast even anticipated the fall in GDP caused by COVID-19 (in 2020) before official figures were released. Such estimates can not only be done via an Expenditure Approach but also an Income Approach; a mixture of the two would be an interesting study.

Note that it is possible to use this method to compute future monthly GDP forecasts.

References:

Mitchell, Smith, Weale, Wright, and Salazar (2005): James Mitchell, Richard J. Smith, Martin R. Weale, Stephen Wright, Eduardo L. Salazar, An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth, The Economic Journal, Volume 115, Issue 501, February 2005, Pages F108–F129, https://doi.org/10.1111/j.0013-0133.2005.00974.x

C. C. Holt (1957) Forecasting seasonals and trends by exponentially weighted moving averages, ONR Research Memorandum, Carnegie Institute of Technology.

P. R. Winters (1960). Forecasting sales by exponentially weighted moving averages. Management Science

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