bbva-google economic indicators · pdf filebbva-google economic indicators . ... people living...

BBVA-Google economic indicators

Madrid, 16 January 2012


Introduction

• Objective: to present BBVA-Google economic indicators, on this occasion relating to the Spanish Tourism sector

• These indicators improve the accuracy and speed at which tourist inflows and overnight stays are forecasted

• Searches allow new and extremely useful indicators to be obtained to estimate tourism trends in Spain

• High added value in social and economic terms, given their contribution to corporate decision-making

• Upholding excellence and technology in the pursuit of knowledge


Motive behind the project

We know past data in retrospect (backcasting). Examples: GDP, LFS, … human capital

We intuit the present (nowcasting)

We do not know future (forecasting)

Information is key to taking the right economic decisions


The usual approach to the problem

Univariate ARIMA models (Box and Jenkins, 1976)

Multivariate VARMA models

Long tradition since the 1970s, used widely nowadays

Extrapolating past trend observed to the future


The background in BBVA Research

Dynamic Factor Models: learning process (KF) that updates parameters

Estimates real time GDP growth (nowcasting) and evaluate indicators

Very good results in Spain, EMU, Mexico, USA …

In 2009 we embarked on a large-scale project to apply new methods

Spain: observed GDP growth and forecasts based on the MICA-BBVA model (%qoq)Source: BBVA ResearchCurrent forecast: 13 January 2012

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2Q0

9

3Q0

9

4Q

09

1Q10

2Q10

3Q10

4Q

10

1Q11

2Q11

3Q11

4Q

11

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

CI at 20% CI at 40% CI at 60%

Latest estimate Observed


The challenge of improving forecasts

Through indications of the future observed inthe present

Internet: a powerful tool that can be used to find information before taking decisions

Google is the leading Internet search engine

How can forecasts be improved?

Information

WorkConsumption

Investment

Economicactivity

Present Future


The background in Google

AR models with searches to predict the present beat those that do not include them

Examples: retail, vehicle and house sales, and tourism

Can searches help to forecast the future?

Choi and Varian (2009): Predicting the Present with Google Trends


The project

Filtered Google searches+ =Dynamic factor

models Indicators

This collaboration project was developed for application in various countries and sectors considered to be of strategic importance for BBVA and Google


Pilot project: Tourism in Spain

Accounting for more than 10% of GDP and employment,

and 27% of exports,

12% of revenue in the external Balance of Payments

Preliminary data make us optimistic about actual results

Tourism is a strategic sectorPercentage accounted for by tourism

2008 2009 2010

GDP 10.4 9.9 10.2

Exports 26.5 23.9 27.0

Balance of paymentrevenues

11.6 12.2 12.2

Employment 11.0

Source: INE, Cuenta Satélite del Turismo, and BdE


Variables forecastedForeign tourist inflows Overnight stays

People living abroad making one or more overnight stays

Every night spent by a Spanish or foreign visitor in a hotel establishment

Both published on around the 22nd or 23rd of the following month

Foreign tourist inflows Source: INE

Total overnight stays in hotels Source: INE

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

Jan

-08

May

-08

Sep

-08

Jan

-09

May

-09

Sep

-09

Jan

-10

May

-10

Sep

-10

Jan

-11

May

-11

Sep

-11

Foreign visitorsA

pr-

08

Oct

-08

Jan

-09

Ap

r-0

9

Oct

-09

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

45,000,000

Jan

-08

Jul-0

8

Jul-0

9

Jan

-10

Ap

r-10

Jul-1

0

Oct

-10

Jan

-11

Ap

r-11

Jul-1

1

Oct

-11

Total overnight stays


Google searches

Weekly data since 2006 in English, German, French and Italian (+ Spanish for overnight stays)

A similar seasonal pattern to tourist inflows but one month ahead

The correlation is significant and extremely high (0.6) even factoring in yoy growth rates

Foreign tourist inflows and Google searchesSource: INE and Google (Base=100, January 2006, RHS)

0

1000000

2000000

3000000

4000000

5000000

6000000

Jan

-06

Jul-0

6

0

100

200

300

400

500

600

700

800

900

Tourist inflows (lhs) Searches (rhs)

Jan

-07

Jul-0

7

Jan

-08

Jul-0

8

Jan

-09

Jul-0

9

Jan

-10

Jul-1

0

Jan

-11

Jul-1

1


Results: a common factor in these variables

The model allows the non-observable factor tobe forecasted …

clarifying the trend of tourist inflows and searches ….

using a recursive process where the model itself learns from its mistakes

Tourist inflows and BBVA-Google indicator searchesSource: INE, Google and BBVA Research

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

Jan

-07

Jul-0

7

-8

-6

-4

-2

0

2

4

6

8

Tourist inflows (lhs) Common factor (rhs)

Jan

-08

Jul-0

8

Jan

-09

Jul-0

9

Jan

-10

Jul-1

0

Jan

-11

Jul-1

1

Jan

-12


Results: improved forecasting capacity

Searches contain useful information that helps forecast the present and the future

Forecasting capacity improves by between 18% and 20% when searches are included

Improving between 24% and 31% when searches are included and dynamic factor models is

specified

Forecasting errors

Foreign tourist inflows Back-casting

Now-casting

Fore-casting

AR model 1.00 1.00 1.00

AR model incl. searches 0.98 0.90 0.82

FD model incl. searches 0.94 0.81 0.76

Mean squared error (AR=1.0, January-December 2011)


Results: forecasts at February 2012

Tourist inflows will reach 1.92 million, 4.4% more than in February 2011

39.8 million visitors are forecasted from February 2011, 10.7% more than in the previous

year

Overnight stays are expected to reach 14.3 million, 0.5% more than in February 2011

Tourist inflows and BBVA-Google indicator forecastsCurrent forecast: 16 January 2012Source: INE, Google and BBVA Research

-12-10-8-6-4-202468

1012141618

20

Jan

-10

Mar

-10

May

-10

Jul-1

0

Sep

-10

No

v-10

Jan

-11

Mar

-11

May

-11

Jul-1

1

Sep

-11

No

v-11

Jan

-12

CI at 20% CI at 40%CI at 60% Latest estimateObserved Cumulative annual figure yoy, %


Results: publications

Flash notes containing monthly forecasts Scientific publications describing technical details of the methodology used and results obtained

All forecasts will have free access to the public, for the benefit of society as a whole, sharing and disseminating the knowledge acquired through this collaboration


Working team contactsRafael Doménech

Chief Economist for Developed [email protected]

Miguel CardosoChief Economist [email protected]

José RuizPrincipal [email protected]

Juan Ramón GarcíaSenior [email protected]

Camilo Andrés UlloaEconomist [email protected]

SpainJuan Ruiz Chief Economist for Economic Scenarios

[email protected]

Jaime Martínez Senior [email protected]

Economic Scenarios Máximo Camacho Professor at the Universidad de Murcia

[email protected]

External partners

Olga San JacintoHead of

Google Spain and Portugal [email protected]

Javier LópezBusiness Analyst [email protected]

Álvaro FerrerTravel Analyst [email protected]

Google Spain


Madrid, 16 January 2012

bbva-google economic indicators · pdf filebbva-google economic indicators . ... people living...

Documents