the use of big data in nowcasting and forecasting of ... · forecasting of macroeconomic variables...

The Use of Big Data in Nowcasting and Forecasting of Macroeconomic VariablesStefan Bender, Research Data and Service Center (RDSC), Deutsche Bundesbank

BCB's Workshop: Measuring and Analyzing the Economy using Big Data9– 10 November 2017, Brasilia

(The views expressed here do not necessarily reflect the opinion of the Deutsche Bundesbank or the Eurosystem.)

Arguments for Moving into Big Data

Use of big data for research and policy advices will increase.

Big data represent additional data sources we need.

Additional arguments (topics of the presentation) Nature of found data (big data). Data generating process. (“Paradigm shift” in research.)

9-10 November 2017, BrasiliaStefan Bender, Research Data and Service Center, Deutsche Bundesbank

Arguments against Moving into Big Data

Enough own data.

Too risky, too many uncertainties (data quality, ownership, ...).

Loss of reputation or trust.


Mobility

-Movement of people-From home to work

assume all go by car

THX to Piet Daas, Statistics Netherlands


4

Apresentador

Notas de apresentação

Especially for this seminar we produced some new results. With these results we would like to stress the power of new data sources, and new techniques to make new, relevant, realtime statistics. Here, we look at Highway A2, A76 and A79 here in Limburg. Nieuwe producten (dashboarding etc) At this moment, CBS produces statistics about commuting patterns. Where do people live, where are they working? This tells policy makers where economic activity takes place, how roads need to be designed and for employers where the necessary skills for their business originate. Bereikbaarheid, milieu, veiligheid We worked on a product that gives not only insights in streams, rather than individual counts, and also on a regional level. Niet sneller, regionaler en aanvullen (combineren), sensor data, andere bronnen, waarschijnlijkheid vergroten, Standdata te combineren (adres en WAARSCHIJNLIJKE werkplek) met route informatie Zelfde opstoppingspunten als in routeplanners – visuele vergelijking Levert een som van al die bewegingen op, met veel data Hier kunnen andere stromen aan worden toegevoegd zoals opleidingen en basisvoorzieningen. Doel is motieven van vervoer (dit als verrijking tov andere analyses) en wat veranderingen in achtergrondkenmerken voor effecten hebben op bereikbaarheid, milieu en veiligheid -> simulaties. We now can, by adding data from different sources like TomTom data, make these figures near real time and more detailed over a daytime, There is a huge amount of data involved, knowing polis, Google XXXX, This introduces the opportunity to Gedetailleerder (wijk niveau) Van tellingen op één punt naar stromen Meer beeld bij oorzaken (modaliteiten, woon/ studie, woon/ basisvoorzieningen, onderliggende factoren) Simulaties mogelijk (nieuw business model..) Hoewel nog maar een deel van de mobiliteit, komt het wel overeen met congestie punten uit Google Maps.

Overview

Big Data (but not the common definition)

Central Banks Policy Areas and the Use of Big Data

Two “new” Examples of Bundesbank research with Big Data

Google Data in Bridge Equation Models for German GDP by

Thomas Götz and Thomas Knetsch

Capturing depositors’ expectations with Google and Twitter Data

by Falko Fecht, Stefan Thum and Patrick Webe

Conclusion


Big Data are not just Data (AAPOR-definition)

Imprecise description of a rich and complicated set of

characteristics,

practices,

techniques,

ethical issues, and

outcomes

all associated with data.


Definition of Big Data – But (at least) one more V


One of my Preferred Definitions of Big Data


Aspirational Transactional

Big Data Generating Process

Big Data are often selective, incomplete and erroneous.

It makes a difference to use Big Data for forecast/nowcast or for detecting causality

Big Data are typically from disparate sources at various points in time and integrated into new data sets.

Thus in statistically valid ways, using Big Data is increasingly challenging.

Blending / Linking Big Data with Administrative Bundesbank Data


Available microdata at the RDSC


Households

CompaniesBanks

Securities

External Data

Bureau van Dijk (like Amadeus, Dafne)

Kantwert: contacts and business networks / balance information (in

real time)

Immobilienscout24 (real estate)

(Patent data)

Other data (big data) on the way


kantwert

Central Bank Policy Areas

Main Tasks of CBs: Preserve monetary and financial stability

This involves:

1.Monitoring of monetary and financial developments

2.Evaluation of monetary policy and financial regulations

3.Structural analysis/research to assess counterfactuals


THX to Falko Fecht

Big Data in Monitoring Macro Developments

Nowcasting and Forecasting

Google searches timely available Searches often in preparation for a certain event (jobloss, purchase

etc.)

Sentiments

Searches in the different categories can also captures sectoralconsumer sentiment

Index of searches for ’Recession’,’Unemployment’, ’Bankruptcy’ etc. can serve as a household sentiment indicator complementingconsumer surveys


THX to Falko Fecht

Big Data in Monitoring Financial Stability I

Household sentiment indices in predicting financial market developments: abnormal stock market returns increases in market volatility fund flows from equity to bond markets

(see Da/Engelberg/Goa RFS 2014)

Fears and Financial Institutions Financial institutions (in particular banks) susceptible to self-

enforcing liquidity crises Google searches (for banks stability related terms) capture

investors’ worries and help predict outflows of funds from financialinstitutions Twitter data allow text analysis to better grasp context Early warning indicator


THX to Falko Fecht

Big Data in Monitoring Financial Stability II

Housing bubbles

Searches and tweets of ’housing bubble’ useful indicator forinvestors’ worries

ImmoScout24 data offers comprise very detailedcharacteristics of the individual real estate (our hope)

Allows to use market microstructure ’indicators for anoverheating, such as market liquidity time on the market of average houses price adjustment etc.


THX to Falko Fecht

Big Data and Monetary Policy Evaluation

Timely availability of Big Data allows for a faster policyevaluation

More granular set of proxies for anticipation, expectations,worries etc. allows to better assess policy effects on attitudes

Examples: In assessing monetary policy effectiveness important to know if

policy measure was anticipated Event studies of news releases and bond yields Twitter data on unconventional policy measures allows to proxy

anticipation


THX to Falko Fecht

Google Data in Bridge Equation Models for German GDP Thomas Götz and Thomas Knetsch, Deutsche Bundesbank

Bundesbank Project Group Conference on “Big Data”

Frankfurt am Main: 24 October, 2017

Motivation

Increased interest in the use of Big Data in general, and Google Search data in

particular, for forecasting purposes.

Instead of a specific (often monthly) indicator, we consider aggregate GDP for

Germany, one of the variables predominantly focused on in our division

Instead of a tailored choice of (or method to combine) search terms, we analyze

the performance of a range of Google variable selection tools

We incorporate Google data into a set of Bridge Equations, one of our workhorse

models for short-term forecasts of German GDP

Appealing structure

Direct incorporation of Big Data into the current model suite (low

implementation costs, preservation of outcome communication, ...)


Our Example-BEM


ECB Google Data


Motivating Example I


Motivating Example II


Conclusion

“Big data” in the form of Google search data incorporated into an

example Bridge Equation Model

Treating them like survey indicators, they affected forecasts of GDP

growth and its components through the underlying monthly indicator

Potential for improving forecast accuracy for GDP growth detected,

mostly when added instead of survey indicators and for PLS and

Lasso

Transfers only partly to GDP components and monthly indicators:

some indicators do benefit quite a lot (e.g., Industrial Production),

others not (e.g., Production in Construction)


Capturing depositors’ expectations with Google and Twitter DataFalko Fecht, Stefan Thum and Patrick WeberBundesbank Conference on "Big Data", 25 October 2017

1. MotivationStrategic complementarities and financial crises

Motivation Can Google searches or Twitter messages be used as a predictor for a

deposit run on banks (time series and cross section)?

Many financial institutions exposed to self-fulfilling liquidity crises Financial institutions performing liquidity transformation are exposed to runs

by depositorsWorries that others excessively withdraw induce investors to withdraw

How to measure investors’ expectations?

Google searches might serve as a proxy for investors' worries Searches serve as an early warning indicator for liquidity crises


2. DataGoogle and Twitter data

We obtain Google Trends Data via www.google.com/trends

Relevant data: Relative search interest in search terms related to deposit insurance in Germany at the local level Transformations:

Data is winsorized and transformed into monthlyfrequency using different specifications: monthly average (avrg), first (fObs) and last (lObs) observation

Twitter Data

Relevant data:Tweets and Retweets containing terms related to deposit insurance


http://www.google.com/trends

2. DataAugment the Google data set with central bank data sources

We augment Google data with…

1. Bundesbank Balance Sheet Items statistics

Outstanding Euro amounts of overnight deposits at a monthly frequency at bank level (census approach)

2. Bundesbank MFI Interest Rate statistics

Interest rates on outstanding amounts of overnight deposits at a monthly frequency at bank level for roughly 230 German banks (sample approach)


3. Descriptive StatisticsGoogle search interest versus deposit shifts


-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

-6

-4

-2

0

2

4

6

8

10

12

14

200412 200511 200610 200709 200808 200907 201006 201105 201204 201303 201402 201501 201512

Google search for 'deposit insurance' (winsorized) (LHS) ∆(Savings Banks / Cooperatives - 1) (RHS)

3. Descriptive StatisticsGoogle search interest versus deposit shifts


-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

-6

-4

-2

0

2

4

6

8

10

12

14

200412 200511 200610 200709 200808 200907 201006 201105 201204 201303 201402 201501 201512

Google search for 'deposit insurance' (winsorized) (LHS) ∆(Savings Banks / Cooperatives - 1) (RHS)

Google searches time series shifted by six

months

4. SummaryGoogle/Twitter data are a valuable measure for depositors’ expectations

Google searches (and Twitter messages) can be used as a

measure for the concern of depositors

Indication of run-type phenomena in local deposit markets

Google/Twitter data capture expectations relevant to financial

stability analysis

Work shows the value added for central bankers to combine

proprietary central bank data with unconventional data


Conclusion

Policy advises and research is about answering questions.

There is a strong need for granular data.

Start by utilizing all of the information that is available, including surveys, admin data and big data

Big data offers new possibilities: we can take best of all worlds big data, surveys, admin data and combine them.

Big data is not only data, it is a new thinking with data.


Summing Up: New Challenges

Define a research question (what are we measuring?): Do not fall in love with the Data. Love the questions it can answer.

Think about what data are available (transactional versus aspirational) and the measurement error (how are we measuring it?): The size of the data reduces the estimation error, not its biases. Quality is what matters.

Link datasets (what are we missing?)

Statistical approaches (how can we draw inference?)

Address Privacy and Confidentiality/Ethics (are we protecting human subjects?)

Need for access and training


THX to Julia Laneand Roberto Rigobon

Thank you !

Website: www.bundesbank.de\fdsz Contact: [email protected]

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