Radar Embedding Example

The IZA/Fable Data Consumption Indicator: Germany is based on consumer and small business transactions from Fable Data, a leading provider of anonymized, pan-European spending data was developed collaboratively by IDSC and Fable Data. Fable Data sources bank and credit/debit card operators and compiles a dataset of expenditures of distinct consumers. 

The index presented here is constructed using high frequency daily data with a lag of 3-4 days. The method used to construct it is the so called “one year look back rolling panel” which is found to correlate best with official data from Eurostat and others. This method provides monthly percentage difference YoY and solves the problem of a dynamic composition of the panel structure.

Since consumers are joining and leaving the panel we proceed as follows: for each month in each year we aggregate the expenditures in that month in that year as well as those in the same month of the previous year using the consumers that are active in both months. We then take percentage difference. This way the index gets better as the panel gets richer and more representative and at each point in time we have the best information available. Since we look at percentage differences the numbers remain comparable over the entire time coverage.

Toll Index June 2024

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.
While in the long run this is bound to make the Toll Index more accurate this introduced a discontinuity. The BAG even had difficulty producing the numbers timely for about year. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):


Focus Magazin,
Tim Harford – The undercover economist,
Financial Times,
MoneyWeek,
WirtschaftsWoche,
CNN International,
DRS3 Swiss public radio,
Deutsche Welle.

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).

Toll Index May 2024

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.
While in the long run this is bound to make the Toll Index more accurate this introduced a discontinuity. The BAG even had difficulty producing the numbers timely for about year. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):


Focus Magazin,
Tim Harford – The undercover economist,
Financial Times,
MoneyWeek,
WirtschaftsWoche,
CNN International,
DRS3 Swiss public radio,
Deutsche Welle.

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).

Toll Index April 2024

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.
While in the long run this is bound to make the Toll Index more accurate this introduced a discontinuity. The BAG even had difficulty producing the numbers timely for about year. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):


Focus Magazin,
Tim Harford – The undercover economist,
Financial Times,
MoneyWeek,
WirtschaftsWoche,
CNN International,
DRS3 Swiss public radio,
Deutsche Welle.

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).

Toll Index March 2024

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.
While in the long run this is bound to make the Toll Index more accurate this introduced a discontinuity. The BAG even had difficulty producing the numbers timely for about year. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):


Focus Magazin,
Tim Harford – The undercover economist,
Financial Times,
MoneyWeek,
WirtschaftsWoche,
CNN International,
DRS3 Swiss public radio,
Deutsche Welle.

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).

Toll Index February 2024

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.
While in the long run this is bound to make the Toll Index more accurate this introduced a discontinuity. The BAG even had difficulty producing the numbers timely for about year. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):


Focus Magazin,
Tim Harford – The undercover economist,
Financial Times,
MoneyWeek,
WirtschaftsWoche,
CNN International,
DRS3 Swiss public radio,
Deutsche Welle.

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).

Toll Index January 2024

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.
While in the long run this is bound to make the Toll Index more accurate this introduced a discontinuity. The BAG even had difficulty producing the numbers timely for about year. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):


Focus Magazin,
Tim Harford – The undercover economist,
Financial Times,
MoneyWeek,
WirtschaftsWoche,
CNN International,
DRS3 Swiss public radio,
Deutsche Welle.

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).