Macrobond 1.0

Macrobond is happy to announce that we today released a new version of the Macrobond application. Our goal is to continue to rapidly expand and develop our application much based on client feedback. A selection of news relating to data and functionality is included if you read on.

You upgrade in the application by clicking the yellow line that appears or by Help > Check for Update. A description about how to upgrade if you need help from IT-department is given on https://redir.macrobond.com/go/installation where you click Upgrade for further instructions.

Selection of new functionality:

Chart style sheet editor – Creating and saving style sheets is now a separate function which you will find among the Action tabs to the left in the application.
Storage of style sheets – Style Sheets can now be stored locally or on our servers (in either your personal or company account) and accessed from there.
Printer selection – Added choices for print properties with Landscape / Portrait and also the ability to choose your printer in the Macrobond application instead of automatically going to the Windows Default Printer.
Simple formula language – In the series list you can now use +, -, *, /, parenthesis and numerical constants.
Chart annotations – using text boxes and drawing
The Real Time Data (RTD) function which retrieves data directly from the Macrobond database in real time in Microsoft Excel.

Selection of data added recently:

MSCI Index Module – The Index module includes Price and Total Return (Gross and Net) for Developed, Emerging and Asia Pacific markets. They are available with a daily and monthly frequency and with full history.
Flow of Funds Accounts of the United States – Quarterly statistics from the Federal Reserve with history back to 1945.
Quarterly statistics from Federal Reserve with history back to 1945
Commitment of Traders Report – The report is published by the CFTC, which is a US government entity. CFTC data includes the COT report, CIT report and disaggregated report.
Hours worked per sector for Sweden – Hours worked (ESA95), working day and seasonally adjusted by industrial classification published by SCB.
Danish house price data – Sourced from Association of Danish Mortgage Banks.
Private Equity index series from LPX – The LPX indices are widely used to track the performance of listed alternative asset classes and for benchmarking actively managed portfolios.
Euro area bank lending survey – The survey addresses issues such as credit standards for approving loans as well as credit terms and conditions applied to enterprise and households.

Macrobond application 1.30.112 maintenance release

This update contains fixes and tweaks.

You can begin installation directly from within the application. Either by clicking on the yellow banner that appears in the application, or by going to Help|Check for update in the menu of the Macrobond application.

If updating requires involvement from your IT department, please see the instructions provided here.

If you have asked us to contact your IT department directly for these upgrades, an email with information about upgrading to the latest version will be sent to your IT contact person. You are always welcome to contact the Macrobond team if you have any questions.

Macrobond application 1.30.109 maintenance release

This update contains fixes and tweaks.

If updating requires involvement from your IT department, please see the instructions provided here.

Macrobond application 1.30.107 maintenance release

This update contains fixes and tweaks.

If updating requires involvement from your IT department, please see the instructions provided here.

Release 3: Feeds and Integrations

Feed Services

Databricks Integration

Databricks is an ecosystem where users can analyse data, and build data products in an agnostic cloud-hosted environment. Databricks is also renowned for the analytics and AI tools they provide.

The specific area we are focusing on here is the Databricks Marketplace.

Macrobond customers can now ask to receive their bespoke data feed directly into Databricks.

The main advantage of doing so is to offer an additional way of consuming the data via the Macrobond Data web API or FTP service for instance, and reduce the ETL ('Extract, Transform, and Load'). Macrobond data would be readily available for consumption directly into the customers' Databricks instance.

The publicly available Macrobond Pay-As-You-Go data feed on Databricks is available here.

Key highlights:

- Reduced ETL

- Bespoke data feed

- Minimum latency

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Content

Macrobond ONE: Now-Casting Economics

The term now-casting is a contraction for now and forecasting and it is defined as the prediction of the present, the very near future and the very recent past. Now-casting is relevant in economics because key statistics on the present state of the economy are available with a significant delay. This is particularly true for those collected on a quarterly basis, with Gross Domestic Product (GDP) being a prominent example.

Now-Casting Economics delivers frequently updated near term predictions of key macroeconomic variables. These predictions are generated by a set of statistical models, which run continuously on the Now-casting’s proprietary platform. As soon as a new value for any of the input data series is published, the relevant Now-Casting models process the new information and update their predictions.

The dataset currently covers 33 countries, additional key macroeconomic statistics which include proprietary indices such as the Now-Casting Surprise Index.

Data navigation improvements

1. From International to National Source

Some sources are both international and national sources. When we treat these sources as international only, users can only find the data within Source & Release.

We will now treat the below sources as national sources, so that we can expose structure within Country & Region, allowing users to navigate thematically within the tree to find the concepts they are after. First release includes ECB and Eurostat.

2. US Labor Market

Some of the steps involve:

• Removing discontinued nodes

• Moving wages & earnings from Labor Market to Income & Earnings

• Taxonomy harmonization e.g. ‘underemployment’

• Deeper tree-nodes to denote better specific concepts

3. Customer-driven fixes, involving custom tree creation to ensure thematic navigation (after) vs. flat or no hierarchy (before)

Including, but not limited to:

• US Dallas Fed Texas Manufacturing Outlook Survey

• US Retail Trade

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Revision History expansion

Users will now be able to benefit from Revision History (access to vintages and associated time stamps) on International sources.

We are turning the feature on for Eurostat first; ECB will be next.

As a reminder, in the last delivery cycle, we also turned on Revision History for daily time series to capture the vintageTimeStamps, to be used as publication dates.

Facilitating cross-regional comparison - International & Subnational RegionKeys

1. International sources’ RegionKeys

RegionKeys allow users to change the region of analysis in their documents, or to link together similar time series programmatically, around a unique concept.

We are starting with Eurostat and will prioritize expansion based on customer requirements.

2. US subnational RegionKeys

Macrobond will now allow users to flip the Region being analysed at the sub-regional level e.g. state, MSA, city etc.

This enhancement covers 132 releases in the US

3. Chinese subnational RegionKeys

Similar to above, this enhancement covers 56 releases in China.

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Macrobond 1.30

Enhanced data exploration workflow

The new “Quick views” provides superior dataset exploration. You can now select ready-made templates combining charts and tables in one screen, via the mouse or through keyboard shortcuts.

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With a time series selected, the Presentation Actions box now allows for several operations to open up pre-defined templates in a “Quick View” tab:

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A screenshot of a computerDescription automatically generated

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By popular request, this Quick View tab includes the option to open a chart and table in the same view:

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And, even more conveniently, these functions can be assigned keyboard shortcuts in the Quick View settings:

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Control the graph size to fit to your publication requirements

We’ve made several changes to give you more flexibility to control the size of the graph area, making it easier to prepare charts for publication purposes.

The main feature now available to you is the “Uniform pane size” setting. This will make all panes in a chart the same size, regardless of how many panes you use, and the size of titles and footers.

This is available in the settings for the Graph area (also in Style sheets) as well as in the Graph layout dialog.

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Additionally, you now have full control over margin indentations from top, left, right, and bottom under Properties for Chart.

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Margin controls now make it possible for you to control the positioning of the edge of the chart, regardless of the size and position of other elements like titles.

These two features combined empower you to control the spatial formatting of your charts like never before.

Additional ways to analyse rates and % data

You can now choose to use additive rebase in slice analytics. Each value of the timeseries in the underlying segment will be subtracted with the first value of the segment minus the value you input. This is particularly useful when you want to have result of each segment starting at 0.

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Improved readability for charts with multiple panes

You can now choose to display the legend text per chart pane, so that each chart pane will have a floating legend text flagging series displayed in their corresponding pane.

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A graph of stock marketDescription automatically generated

Making bar charts more readable

Now the column headers and footers will wrap the text for column width adjustment without having to hard code the line break in the text box.

Backfill history using discontinued series

Macrobond may be required to create a new time series with a recent historical start point if there is a change made to the methodology of the time series or any other change.

Users seeking to extend the historical data of their time series can now easily locate related discontinued time series by right-clicking within the contextual menu. This functionality allows for seamless addition of these series to documents, enabling the extension of historical context for the main time series they are using.

Other use cases than backfilling history can be achieved using this feature.
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Support for Chinese language

The general release of the application version in Chinese is now available to all users running v1.29 and above.

You can now opt for Macrobond in Chinese by changing your database language in your application settings:

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If your application version does not allow you to switch the database language yourself, ask your Macrobond representative to activate the Chinese application for you.

The tree structure and time series descriptions are now available in Chinese:

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Macrobond application 1.29.115 maintenance release

This update contains fixes and tweaks.

If updating requires involvement from your IT department, please see the instructions provided here.

Databricks feed

Introduction
Data Discovery
Set up your data feed in the Macrobond management panel
Use your Snowflake data feed

Introduction

You can access any of Macrobond’s time series data into your Databricks environment. The data feed is intended for production purposes with near real-time updates.

Here’s how it works:

Data discovery

The Databricks data feed is used for production purposes only. Macrobond delivers the time series chosen by the consumer into their Databricks environment. There are various options to explore the Macrobond database and find the time series to be delivered into Databricks:

Using Macrobond.net data catalogue. The Macrobond.net is a read only web portal that offers access to the tree structure and time series associated with every source included in the Macrobond base package.. Users can use it to explore the coverage and start documenting the primnames (unique identifiers) of the time series they would like to consume via Databricks.
Using the Macrobond application. If you already subscribe to Core or Data+, the principles are the same as 1. above. If you subscribe to the desktop application, you have access to many more features including the analytics and sharing capabilities. Documenting a universe of time series via the Macrobond application can also be easier as it allows you to download the data in Excel or via API (Data+).
Using the Macrobond Data Web API. Should you need to evaluate programmatically the time series data points, the Data Web API can also be used as a data feed product instead of being only an enabler to using the Databricks data feed.
Using Macrobond’s data team. You can express various parameters that fit into your requirements so that Macrobond’s data team can generate a list of time series by converting these parameters into Macrobond-defined metadata fields. This allows you to evaluate the full database at scale. To ensure you access your definite universe, our team will discuss with you your exact requirements.

Set up your data feed in the Macrobond management portal

Once you know which time series you would like to receive in Databricks, or with the initial data set you are interested in, you can request the primnames through a dedicated portal provided by Macrobond.

The Macrobond team will provide access to this URL: https://feed.macrobond.com/login and send via email your Username and Password.

Here is an overview of the key features:

Monitor your data feed properties

Your contractual allowance appears under “Unique Time Series limit”. You can use your feed during your subscription term (annual basis). You have an allowance of how many time series you can add or remove from your feed via the “Changes Per Month” counter, which corresponds to 10% of your total allowance.
Access your existing Subscription List and visualize or download the list of primnames you are subscribed to, including a flag denoting whether the series are feedable or not to be delivered via the feed (compliance module).
Add time series to your data feed. You can add the primnames with a line break or decide to upload a list from a .csv file.
Remove time series from your data feed.

Use your Databricks data feed

Once the time series are added into the Macrobond data feed management portal, the series are immediately delivered into your Databricks environment. Note that depending on the volume of time series, this operation can take some time to load.

Time series updates are also provided into Databricks with minimal latency as they are automatically pushed by Macrobond as soon as an entity received a complete update transaction in the backend.

The Pay-As-You-Go Data feed in Databricks provides access to a few tables and views. A data dictionary and sample notebook are available on the Databricks listing.

Here is an overview of the schema. Columns that are repeated across multiple tables or views will be skipped once described in a table definition.

MAIN_ENTITY [Table]

It delivers the full dataset but should not be used by users because it is redundant with the following tables or views. Instead, you should use either LATEST_ENTITY [View], derived from the MAIN_ENTITY table to obtain the latest version of your time series, or REVISION [Table] to obtain the vintages.

PRIMNAME: unique identifier provided by Macrobond to identify a time series.
METADATA: nest list of metadata fields for each time series containing lists or dictionaries.
DATES: list of observation periods for the time series. Note that Macrobond uses the beginning of a period to denote the reference date. For instance, 2024-04-01 means “Q2 2024” in the case of a quarterly time series.
OBSERVATION_VALUES: list of data points for the time series.
ULID: a column intended to be used to join to REVISION (in conjunction with a PRIMNAME). This is a unique identifier for simultaneously transmitted time series (belonging to the same batch when loaded into Databricks). A newer batch of uploads always increments this value. In a given batch of uploads, a given time series can appear only once.
NUMBER_OF_REVISIONS: number of unique vintages for the selected time series. The underlying data can be consumed in the REVISION table.
LAST_UPDATE: time stamp when the time series was fully delivered and accessible in Databricks
FEED_QUEUE_INSERT_TIMESTAMP: time stamp when Macrobond-designed Databricks worker added the time series to the insert queue as part of a loading batch.

REVISION [Table]

Some columns are already described in the previous table definition. New columns are:

VINTAGETIMESTAMP: time stamp as to when this version of the time series containing data that could have been revision afterward it was made available in Macrobond.

Note that some time series have received a backfill to extend the point-in-time history directly from the source. As such, most of the time stamps prior to 2018 are arbitrary (as opposed to the date stamps).

To understand whether a time series received a backfill or not, you can refer to the time series metadata and look for fields: “RevisionHistorySource” and “RevisionHistorySourceCutOffDate”. When both fields exist, the time series was backfilled. The spread between the cut off date and the “FirstRevisionTimeStamp” provides the magnitude of the backfill. Revisions post cut off date are all captured and stored by Macrobond, not by the source.

When VINTAGETIMESTAMP = null, it corresponds to the first unrevised version of a time series as Macrobond knows it. We do not know at which point in time this version was published, until there is a first revision, captured by the subsequent VINTAGETIMESTAMP = FirstRevisionTimeStamp in the metadata from when the information is known.

In summary, every time series capturing Revision History in Macrobond starts with a first vintage time stamps = null.

VINTAGEDATES: observation dates available as of the vintage time stamp.
VINTAGEVALUES: observation points available as of the vintage time stamp.

SUBSCRIPTION [View]

You see the same information as you do in your data feed management portal. This is explained in 2. above. The table is updated once a day.

PRIMNAME: requested primname. It can be an alias code.
ACTUAL_CODE: primname in the Macrobond database.
STATE: denotes the entity state. 0: active | 4: discontinued | -1: deleted
AVAILABLE_FOR_FEED: 1: compliant | 0: non feedable. In case of non feedable time series, you can prove to Macrobond you have permission or a data license with the source and Macrobond can create a feedability exception on your account.
LAST_UPDATE_IN_DATABRICKS: upload time stamp in Databricks.

LATEST_ENTITY [View]

Derived from the MAIN_ENTITY table, this view provides the latest version of your time series. It is the equivalent to the latest vintage available for time series storing Revision History.

Snowflake data feed

Introduction
Data Discovery
Set up your data feed in the Macrobond management panel
Use your Snowflake data feed
Data Dictionary

Introduction

You can access any of Macrobond’s time series data into your Snowflake environment. The data feed is intended for production purposes with near real-time updates.

Here’s how it works:

Data Discovery

The Snowflake data feed is used for production purposes only. Macrobond delivers the time series chosen by the consumer into their Snowflake environment. There are various options to explore the Macrobond database and find the time series to be delivered into Snowflake:

Using Macrobond.net data catalogue. The Macrobond.net is a read only web portal that offers access to the tree structure and time series associated with every source included in the Macrobond base package. Users can use it to explore the coverage and start documenting the primnames (unique identifiers) of the time series they would like to consume via Snowflake.
Using the Macrobond application. If you already subscribe to Core or Data+, the principles are the same as 1. above. If you subscribe to the desktop application, you have access to many more features including the analytics and sharing capabilities. Documenting a universe of time series via the Macrobond application can also be easier as it allows you to download the data in Excel or via API (Data+).
Using the Macrobond Data Web API. Should you need to evaluate programmatically the time series data points, the Data Web API can also be used as a data feed product instead of being only an enabler to using the Snowflake data feed.
Using Macrobond’s data team. You can express various parameters that fit into your requirements so that Macrobond’s data team can generate a list of time series by converting these parameters into Macrobond-defined metadata fields. This allows you to evaluate the full database at scale. To ensure you access your definite universe, our team will discuss with you your exact requirements.

Set up your data feed in the Macrobond management portal

Once you know which time series you would like to receive in Snowflake, or with the initial data set you are interested in, you can request the primnames through a dedicated portal provided by Macrobond.

The Macrobond team will provide access to this URL: https://feed.macrobond.com/login and send you via email your Username and Password.

Note that you can access the same portal through multiple team members to adjust the settings of your data feed in Snowflake.

Here is an overview of the key functions:

Monitor your data feed properties

Your contractual allowance appears under “Unique Time Series limit”. You can use your feed during your subscription term (annual basis). You have an allowance of how many time series you can add or remove from your feed via the “Changes Per Month” counter, which corresponds to 10% of your total allowance.
Access your existing Subscription List and visualize or download the list of primnames you are subscribed to, including a flag denoting whether the series are feedable or not (compliance checks).
Add time series to your data feed. You can add the primnames with a line break or decide to upload a list previously stored in a csv format.
Remove time series from your data feed.

Use your Snowflake data feed

Once the time series are added into the Macrobond data feed management portal, the series are immediately delivered into your Snowflake environment. Note that depending on the volume of time series, this operation can take some time to load.

Time series updates are also provided into Snowflake with minimal latency as they are automatically pushed by Macrobond as soon as an entity received a complete update transaction in our backend system.

The Pay-As-You-Go Data feed in Snowflake provides access to a few tables and views. Data dictionary is available on the Snowflake listing: https://app.snowflake.com/marketplace/listing/GZSYZQKOHJ/macrobond-financial-macrobond-pay-as-you-go-data

Here is an overview of the schema. Columns that are repeated across multiple tables or views will be skipped once described in a table definition.

Data Dictionary

MAIN_ENTITY [Table]

It delivers the full dataset but should not be used straight away by the users. Instead, you should use either LATEST_ENTITY [View], derived from the MAIN_ENTITY table to obtain the latest version of your time series, or REVISION [Table] to obtain the vintages.

PRIMNAME: unique identifier provided by Macrobond to identify a time series.
METADATA: nest list of metadata fields for each time series containing lists or dictionaries.
DATES: list of observation periods for the time series. Note that Macrobond uses the beginning of a period to denote the reference date. For instance, 2024-04-01 means “Q2 2024” in the case of a quarterly time series.
OBSERVATION_VALUES: list of data points for the time series.
ULID: a column intended to be used to join to REVISION (in conjunction with a PRIMNAME). This is a unique identifier for simultaneously transmitted time series (belonging to the same batch when loaded into Snowflake). A newer batch of uploads always increments this value. In a given batch of uploads, a given time series can appear only once.
NUMBER_OF_REVISIONS: number of unique vintages for the selected time series. The underlying data can be consumed in the REVISION table.
LAST_UPDATE: time stamp when the time series was fully delivered and accessible in Snowflake.
FEED_QUEUE_INSERT_TIMESTAMP: time stamp when Macrobond-designed Snowflake worker added the time series to the insert queue as part of a loading batch.

REVISION [Table]

Some columns are already described in the previous table definition. New columns are:

VINTAGETIMESTAMP: time stamp as to when this version of the time series containing data that could have been revision afterward it was made available in Macrobond.
Note that some time series have received a backfill to extend the point-in-time history directly from the source. As such, most of the time stamps prior to 2018 are arbitrary (as opposed to the date stamps).
To understand whether a time series received a backfill or not, you can refer to the time series metadata and look for fields: “RevisionHistorySource” and “RevisionHistorySourceCutOffDate”.
When both fields exist, the time series was backfilled. The spread between the cut off date and the “FirstRevisionTimeStamp” provides the magnitude of the backfill. Revisions post cut off date are all captured and stored by us, not by the source.
When VINTAGETIMESTAMP = null, it corresponds to the first unrevised version of a time series as Macrobond knows it. We do not know at which point in time this version was published, until there is a first revision, captured by the subsequent VINTAGETIMESTAMP = FirstRevisionTimeStamp in the metadata from when the information is known.
In summary, every time series capturing Revision History in Macrobond starts with a first vintage time stamps = null.
VINTAGEDATES: observation dates available as of the vintage time stamp.
VINTAGEVALUES: observation points available as of the vintage time stamp.

SUBSCRIPTION [View]

You see the same information as you do in your data feed management portal. This is explained in 2. above. The table is updated once a day.

PRIMNAME: requested primname. It can be an alias code.
ACTUAL_CODE: primname in the Macrobond database.
STATE: denotes the entity state. 0: active | 4: discontinued | -1: deleted
AVAILABLE_FOR_FEED: 1: compliant | 0: non feedable. In case of non feedable time series, you can prove to Macrobond you have permission or a data license with the source and Macrobond can create a feedability exception on your account.
LAST_UPDATE_IN_SNOWFLAKE: upload time stamp in Snowflake.

LATEST_ENTITY [View]

Derived from the MAIN_ENTITY table, this view provides the latest version of your time series. It is the equivalent to the latest vintage available for time series storing Revision History.

Macrobond application 1.29.113 maintenance release

This update contains fixes and tweaks.

If updating requires involvement from your IT department, please see the instructions provided here.