Nowcasting

Domenico GiannoneUniversité Libre de Bruxelles and CEPR

3rd VALE-EPGE Global Economic ConferenceBusiness Cycles

Rio de Janeiro , May 2013

Nowcasting

Contraction of the terms Now and Forecasting

Meteorology Nowcastingforecasting up to 6-12 hours ahead (long tradition, since 1860)

Only recently introduced in economics:Evans, 2005 IJCB; Giannone, Reichlin and Small, JME 2008

Why? Key variables released at low frequency and with longpublication delays.• Example, US GDP: Advanced estimate 4 weeks after end of the reference quarter• Example, EA GDP: Flash estimate 6 weeks after end of the reference quarter

Economic Nowcasting:Forecasting the near future, the present and even recent past.

Nowcasting

Contraction of the terms Now and Forecasting

Meteorology Nowcastingforecasting up to 6-12 hours ahead (long tradition, since 1860)

Only recently introduced in economics:Evans, 2005 IJCB; Giannone, Reichlin and Small, JME 2008

Why? Key variables released at low frequency and with longpublication delays.• Example, US GDP: Advanced estimate 4 weeks after end of the reference quarter• Example, EA GDP: Flash estimate 6 weeks after end of the reference quarter

Economic Nowcasting:Forecasting the near future, the present and even recent past.

Nowcasting

Contraction of the terms Now and Forecasting

Meteorology Nowcastingforecasting up to 6-12 hours ahead (long tradition, since 1860)

Only recently introduced in economics:Evans, 2005 IJCB; Giannone, Reichlin and Small, JME 2008

Why? Key variables released at low frequency and with longpublication delays.• Example, US GDP: Advanced estimate 4 weeks after end of the reference quarter• Example, EA GDP: Flash estimate 6 weeks after end of the reference quarter

Economic Nowcasting:Forecasting the near future, the present and even recent past.

Nowcasting

Contraction of the terms Now and Forecasting

Meteorology Nowcastingforecasting up to 6-12 hours ahead (long tradition, since 1860)

Only recently introduced in economics:Evans, 2005 IJCB; Giannone, Reichlin and Small, JME 2008

Why? Key variables released at low frequency and with longpublication delays.• Example, US GDP: Advanced estimate 4 weeks after end of the reference quarter• Example, EA GDP: Flash estimate 6 weeks after end of the reference quarter

Economic Nowcasting:Forecasting the near future, the present and even recent past.

This Presetation

1 Nowcasting and the Real-Time Data-Flowwith M. Banbura, M. Modugno and L. ReichlinPrepared for the Handbook of Economic Forecasting, Volume 2,Elsevier-North Holland

2 Nowcasting Chinawith S. Miranda Agrippino and M. ModugnoIn progress

3 Nowcasting BrazilM. ModugnoIn progress

Macroeconomic Forecasting and Conjuctural Analysis

• predicting the future: formal economic modeling of therelations among key macroeconomic aggregates

• predicting the present: simplified heuristic scrutiny of avariety of conjunctural data

• forecasts are updated infrequently disregarding thehigh-frequency flow of conjectural informationquarterly updates (SPF and Central Banks), bi-annual updates (OECD, IMF)

Research questions

• How important is nowcasting relative to longer horizonforecasting?

• Can we predict the present? How relevant is informaljudgement?

• How relevant is the conjectural information? How oftenshould we update the predictions?

Macroeconomic Forecasting and Conjuctural Analysis

• predicting the future: formal economic modeling of therelations among key macroeconomic aggregates

• predicting the present: simplified heuristic scrutiny of avariety of conjunctural data

• forecasts are updated infrequently disregarding thehigh-frequency flow of conjectural informationquarterly updates (SPF and Central Banks), bi-annual updates (OECD, IMF)

Research questions

• How important is nowcasting relative to longer horizonforecasting?

• Can we predict the present? How relevant is informaljudgement?

• How relevant is the conjectural information? How oftenshould we update the predictions?

Macroeconomic Forecasting and Conjuctural Analysis

• predicting the future: formal economic modeling of therelations among key macroeconomic aggregates

• predicting the present: simplified heuristic scrutiny of avariety of conjunctural data

• forecasts are updated infrequently disregarding thehigh-frequency flow of conjectural informationquarterly updates (SPF and Central Banks), bi-annual updates (OECD, IMF)

Research questions

• How important is nowcasting relative to longer horizonforecasting?

• Can we predict the present? How relevant is informaljudgement?

• How relevant is the conjectural information? How oftenshould we update the predictions?

Macroeconomic Forecasting and Conjuctural Analysis

• predicting the future: formal economic modeling of therelations among key macroeconomic aggregates

• predicting the present: simplified heuristic scrutiny of avariety of conjunctural data

• forecasts are updated infrequently disregarding thehigh-frequency flow of conjectural informationquarterly updates (SPF and Central Banks), bi-annual updates (OECD, IMF)

Research questions

• How important is nowcasting relative to longer horizonforecasting?

• Can we predict the present? How relevant is informaljudgement?

• How relevant is the conjectural information? How oftenshould we update the predictions?

How important is nowcasting relative to longer horizonforecasting?

Very !!!!

Forecasting GDP in real timeMSFE relative to constant growthHorizon 0 1 2 3 4

GB 0.87 1.03 1.16 1.23 1.29SPF 0.85 1.03 1.00 1.06 1.06

Evaluation sample 1992Q1 through 2001Q4

• The present is the only horizon of predictability.• Unpredictability beyond current quarter• Accuracy of macroeconomic projections heavily rely on

starting conditions

How can we predict the present?Can a machine replicate expert judgement?

How important is nowcasting relative to longer horizonforecasting?

Very !!!!

Forecasting GDP in real timeMSFE relative to constant growthHorizon 0 1 2 3 4

GB 0.87 1.03 1.16 1.23 1.29SPF 0.85 1.03 1.00 1.06 1.06

Evaluation sample 1992Q1 through 2001Q4

• The present is the only horizon of predictability.• Unpredictability beyond current quarter• Accuracy of macroeconomic projections heavily rely on

starting conditions

How can we predict the present?Can a machine replicate expert judgement?

How important is nowcasting relative to longer horizonforecasting?

Very !!!!

Forecasting GDP in real timeMSFE relative to constant growthHorizon 0 1 2 3 4

GB 0.87 1.03 1.16 1.23 1.29SPF 0.85 1.03 1.00 1.06 1.06

Evaluation sample 1992Q1 through 2001Q4

• The present is the only horizon of predictability.• Unpredictability beyond current quarter• Accuracy of macroeconomic projections heavily rely on

starting conditions

How can we predict the present?Can a machine replicate expert judgement?

How important is expert Judgement?

The Experts!

The Computer Nerde

Now-Casting US GDP: 10 years of Experience

Now-Casting US GDP: 10 years of Experience

Learning from the Markets

Market participants can be viewed as now-casters

⇒ they obsessively monitor all macroeconomic data to get aview on current and future fundamentals and their effectson policy

• The relevant information on the state of the economy isconveyed to markets through the release ofmacroeconomic reports.

• Market expectation for the headlines of these reports arecollected up to the day before the actual release of theindicator and distributed by data providers (i.e. Bloomberg).

• When realizations are different than these expectations,that is when the news are sizeable, the view of the marketchanges and this leads to changes in asset pricessee (Boyd, Hu, and Jagannathan, 2005; Flannery and Protopapadakis, 2002)).

Learning from the Markets

Market participants can be viewed as now-casters

⇒ they obsessively monitor all macroeconomic data to get aview on current and future fundamentals and their effectson policy

• The relevant information on the state of the economy isconveyed to markets through the release ofmacroeconomic reports.

• Market expectation for the headlines of these reports arecollected up to the day before the actual release of theindicator and distributed by data providers (i.e. Bloomberg).

• When realizations are different than these expectations,that is when the news are sizeable, the view of the marketchanges and this leads to changes in asset pricessee (Boyd, Hu, and Jagannathan, 2005; Flannery and Protopapadakis, 2002)).

Learning from the Markets

Market participants can be viewed as now-casters

⇒ they obsessively monitor all macroeconomic data to get aview on current and future fundamentals and their effectson policy

• The relevant information on the state of the economy isconveyed to markets through the release ofmacroeconomic reports.

• Market expectation for the headlines of these reports arecollected up to the day before the actual release of theindicator and distributed by data providers (i.e. Bloomberg).

• When realizations are different than these expectations,that is when the news are sizeable, the view of the marketchanges and this leads to changes in asset pricessee (Boyd, Hu, and Jagannathan, 2005; Flannery and Protopapadakis, 2002)).

Learning from the markets: Bloomberg.com

Learning from the Markets

Market participants can be viewed as now-casters

⇒ they obsessively monitor all macroeconomic data to get aview on current and future fundamentals and their effectson policy

• The relevant information on the state of the economy isconveyed to markets through the release ofmacroeconomic reports.

• Market form expectations for the headlines of these reportsup to the day before the actual release of the indicator anddistributed by data providers (i.e. Bloomberg).

• When realizations are different than these expectations,that is when the news are sizeable, the view of the marketchanges and this leads to changes in asset pricessee (Boyd, Hu, and Jagannathan, 2005; Flannery and Protopapadakis, 2002)).

Learning from the Markets

Market participants can be viewed as now-casters

⇒ they obsessively monitor all macroeconomic data to get aview on current and future fundamentals and their effectson policy

• The relevant information on the state of the economy isconveyed to markets through the release ofmacroeconomic reports.

• Market form expectations for the headlines of these reportsup to the day before the actual release of the indicator anddistributed by data providers (i.e. Bloomberg).

• When realizations are different than these expectations,that is when the news are sizeable, the view of the marketchanges and this leads to changes in asset pricessee (Boyd, Hu, and Jagannathan, 2005; Flannery and Protopapadakis, 2002)).

The Real-Time Data-Flow: Last Week...

Learning from the Markets

Market participants can be viewed as now-casters

⇒ they obsessively monitor all macroeconomic data to get aview on current and future fundamentals and their effectson policy

• The relevant information on the state of the economy isconveyed to markets through the release ofmacroeconomic reports.

• Market expectation for the headlines of these reports arecollected up to the day before the actual release of theindicator and distributed by data providers (i.e. Bloomberg).

• When realizations are different than theseexpectations, that is when the news are sizeable, theview of the market changes and this leads to changesin asset pricessee (Boyd, Hu, and Jagannathan, 2005; Flannery and Protopapadakis, 2002)).

The Real-Time Data-Flow: News

The Real-Time Data-Flow: News

Employment report on March 10, 2012

From the Bloomberg News:

• "Employers in the U.S. took on more workers than forecastin February, [...]"

• "[...] Stocks rose, capping the fourth straight weekly rallyfor the Standard & Poor’ s 500 Index [...]"

• "[...] Yields on benchmark 10-year note climbed to thehighest in a week yesterday after the job report reducedspeculation Federal Reserve policy makers may hint atnext week’s meeting that they’re moving closer to moremonetary stimulus[...]"

Employment report on August 3, 2012

From Bloomberg News:

• "The U.S. economy generated more jobs than forecast inJuly [...]"

• "[...] The S&P 500 advanced 1.9 percent to 1,390.99 inNew York after dropping 1.5 percent in the previous fourdays [...]"

• "[...] The yield on the 10-year Treasury note climbed to1.57 percent from 1.48 percent late yesterday[...]"

Employment report on September 7, 2012

Weak jobs report fuels QE3 hopes"[...] U.S. stocks wavered Friday, as investors digested adisappointing August jobs report but hoped the weaknesswould prompt the Federal Reserve to jumpstart the slowingeconomy with stimulus when it meets next week. [...]"

• "[...] The Labor Department reported that 96,000 jobs wereadded in August, well below the 120,000 jobs that theeconomists surveyed by CNNMoney were looking for.[...]"

• "[...] The Dow Jones industrial average and the Nasdaqedged up 0.1%, while the S&P 500 gained 0.3%. [...]"

Employment report on December 7, 2012

Forbes"Private payroll jobs increased by 146,000 in November abovethe 93,000 economists expected and the 12-month average of157,000 per month.[. . . ] What does this report mean for tradersand investors?"

• "[...] an improving economy will put the pressure on theFed to launch another round of QE3. This is certainly badnews for precious metals. [...]"

• "[...]a stronger economy is bad news for Treasuries,especially at a time when they trade at record low yields.So, I will stay away from U.S. Treasuries [...]"

• "[...] investors may want to buy into economically sensitivesectors - like homebuilding - as higher employmentincreases the likelihood of people buying homes. [...]"

Mimicking Market behavior and Beyond

(a) Construct a joint model for all macroeconomic data releases(b) Update the model in real time, in accordance with thereal-time data flow

• Model based forecasts: free of judgement, mood, heading.• Translate the news in a common unit

What’s the impact of the news on GDP?

A model of Now-Casting

• yQt : GDP at time t .

• Ωv : vintage of data (quarterly, monthly, possibly daily)available at time v (date of a particular data release)

Nowcasting of yQt : orthogonal projection of yQ

t on the availableinformation:

E[yQ

t |Ωv

],

The information set Ωv has particular characteristics:

1 it has a “ragged” or “jagged edge” [publication lags differingacross series]

2 it contains mixed frequency series, in our case monthlyand quarterly

3 it could be large

A model of Now-Casting

• yQt : GDP at time t .

• Ωv : vintage of data (quarterly, monthly, possibly daily)available at time v (date of a particular data release)

Nowcasting of yQt : orthogonal projection of yQ

t on the availableinformation:

E[yQ

t |Ωv

],

The information set Ωv has particular characteristics:

1 it has a “ragged” or “jagged edge” [publication lags differingacross series]

2 it contains mixed frequency series, in our case monthlyand quarterly

3 it could be large

A model of Now-Casting

• yQt : GDP at time t .

• Ωv : vintage of data (quarterly, monthly, possibly daily)available at time v (date of a particular data release)

Nowcasting of yQt : orthogonal projection of yQ

t on the availableinformation:

E[yQ

t |Ωv

],

The information set Ωv has particular characteristics:

1 it has a “ragged” or “jagged edge” [publication lags differingacross series]

2 it contains mixed frequency series, in our case monthlyand quarterly

3 it could be large

Further features

• Projections need to be updated regularly

E[yQ

t |Ωv

], E

[yQ

t |Ωv+1

], ...

v , v + 1, ..., consecutive data releases

Typically the intervals between two consecutive data releasesare short (possible couple of days or less) and change over time.Consequently, v has high frequency and it is irregularly spaced.

News and nowcast revisions• New release⇒ the information set expands (new

releases): Ωv ⊆ Ωv+1 [we are abstracting from data revisions]

• Decompose new forecast in two orthogonal components:

E[yQ

t |Ωv+1

]︸ ︷︷ ︸

new forecast

= E[yQ

t |Ωv

]︸ ︷︷ ︸

old forecast

+E[yQ

t |Iv+1

]︸ ︷︷ ︸

revision

,

Iv+1 information in Ωv+1 “orthogonal” to Ωv

• If we have a model that can account for joint dynamics ofall variables, we can express the forecast revision as aweighted sum of news from the released variables:

E[yQ

t |Ωv+1

]− E

[yQ

t |Ωv

]︸ ︷︷ ︸

forecast revision

=∑

j∈Jv+1

bj,t,v+1(xj,Tj,v+1 − E

[xj,Tj,v+1 |Ωv

])︸ ︷︷ ︸news

.

For detailed derivation see Banubra and Modugno, 2008.

News and nowcast revisions• New release⇒ the information set expands (new

releases): Ωv ⊆ Ωv+1 [we are abstracting from data revisions]

• Decompose new forecast in two orthogonal components:

E[yQ

t |Ωv+1

]︸ ︷︷ ︸

new forecast

= E[yQ

t |Ωv

]︸ ︷︷ ︸

old forecast

+E[yQ

t |Iv+1

]︸ ︷︷ ︸

revision

,

Iv+1 information in Ωv+1 “orthogonal” to Ωv

• If we have a model that can account for joint dynamics ofall variables, we can express the forecast revision as aweighted sum of news from the released variables:

E[yQ

t |Ωv+1

]− E

[yQ

t |Ωv

]︸ ︷︷ ︸

forecast revision

=∑

j∈Jv+1

bj,t,v+1(xj,Tj,v+1 − E

[xj,Tj,v+1 |Ωv

])︸ ︷︷ ︸news

.

For detailed derivation see Banubra and Modugno, 2008.

News and nowcast revisions• New release⇒ the information set expands (new

releases): Ωv ⊆ Ωv+1 [we are abstracting from data revisions]

• Decompose new forecast in two orthogonal components:

E[yQ

t |Ωv+1

]︸ ︷︷ ︸

new forecast

= E[yQ

t |Ωv

]︸ ︷︷ ︸

old forecast

+E[yQ

t |Iv+1

]︸ ︷︷ ︸

revision

,

Iv+1 information in Ωv+1 “orthogonal” to Ωv

• If we have a model that can account for joint dynamics ofall variables, we can express the forecast revision as aweighted sum of news from the released variables:

E[yQ

t |Ωv+1

]− E

[yQ

t |Ωv

]︸ ︷︷ ︸

forecast revision

=∑

j∈Jv+1

bj,t,v+1(xj,Tj,v+1 − E

[xj,Tj,v+1 |Ωv

])︸ ︷︷ ︸news

.

For detailed derivation see Banubra and Modugno, 2008.

What kind of framework?

Three desiderata:

1 can capture joint dynamics of inputs and target2 can be estimated on many series while retaining parsimony3 can handle jagged edged data and mix frequency

Idea: use parsimonious model that can be cast in state spaceform and use Kalman filter to project and to handle jaggededged dataEvans 2005 IJCB; Giannone, Reichlin and Small, 2008 JME

Why Large?The Real-Time Data-Flow: This Week

Why Large?The Real-Time Data-Flow: Next Week

Computing projections. What kind of model?The dynamic factor model

xt = µ+ Λft + εt ,

• ft : (unobserved) common factors; εt : idiosyncratic components

• Λ factor loadings

• Factors are modelled as a VAR process:

ft = A1ft−1 + · · ·+ Apft−p + ut

Parsimonious and robust model for Big Data- Forni et al. (2000), Stock and Watson (2002, Bernanke and Boivin (2002), Bai (2003) , Giannone et al (2005)

Alternative: Mixed Frequency Vector Autoregression+ Use Shrinkage (BVAR) to make it work with Big Data.Schorfheide and Song (2011), McCracken, Owyang, Sekhposyan (2013), Ghysels (2012), Felsenstein, Funovits,

Deistler, Anderson, Zamani, Chen (2013)

Problems and solutions

• Missing data

Kalman filter and smoother can be used to obtain, in an efficientand automatic manner, the projection for any pattern of dataavailability in Ωv as well as the news Iv+1 and expectationsneeded to compute bj,t,v+1

• Mixed frequency

Consider lower frequency variables as being periodically missing

• Estimation: Quasi Maximum likelihood:- robust and feasible Doz, Giannone and Reichlin., 2008 REStat

- handling missing data Banbura and Modugno, 2010

State space representation with mixed frequenciesExample: Let Y Q

t denote the vector of (log of) the quarterly flowseries.We assume that Y Q

t is the sum of daily contributions Xt

Y Qt =

t∑s=t−k+1

Xs , t = k ,2k , . . . .

Hence we will have that the stationary series yQt = Y Q

t − Y Qt−k can be

written as:

yQt = k

t∑s=t−k+1

t + 1 − sk

xs +t−k∑

s=t−2∗(k−1)

s − t + 2 ∗ k − 1k

xs

, t = k , 2k , . . . ,

where xs = Xs − Xs−1 can be thought of as an unobserved dailygrowth rate (or difference).See also Modugno 2011: Nowcasting Inflation

The real-time data flow

No Name Frequency Publication delay(in days after reference period)

1 Real Gross Domestic Product quarterly 282 Industrial Production Index monthly 143 Purchasing Manager Index, Manufacturing monthly 34 Real Disposable Personal Income monthly 295 Unemployment Rate monthly 76 Employment, Non-farm Payrolls monthly 77 Personal Consumption Expenditure monthly 298 Housing Starts monthly 199 New Residential Sales monthly 2610 Manufacturers’ New Orders, Durable Goods monthly 2711 Producer Price Index, Finished Goods monthly 1312 Consumer Price Index, All Urban Consumers monthly 1413 Imports monthly 4314 Exports monthly 4315 Philadelphia Fed Survey, General Business Conditions monthly -1016 Retail and Food Services Sales monthly 1417 Conference Board Consumer Confidence monthly -518 Bloomberg Consumer Comfort Index weekly 419 Initial Jobless Claims weekly 420 S&P 500 Index daily 121 Crude Oil, West Texas Intermediate (WTI) daily 122 10-Year Treasury Constant Maturity Rate daily 123 3-Month Treasury Bill, Secondary Market Rate daily 124 Trade Weighted Exchange Index, Major Currencies daily 1

Daily factor, GDP and its common component

-3

-2

-1

0

1

2

3

03/0

1/19

83

03/0

1/19

84

03/0

1/19

85

03/0

1/19

86

03/0

1/19

87

03/0

1/19

88

03/0

1/19

89

03/0

1/19

90

03/0

1/19

91

03/0

1/19

92

03/0

1/19

93

03/0

1/19

94

03/0

1/19

95

03/0

1/19

96

03/0

1/19

97

03/0

1/19

98

03/0

1/19

99

03/0

1/20

00

03/0

1/20

01

03/0

1/20

02

03/0

1/20

03

03/0

1/20

04

03/0

1/20

05

03/0

1/20

06

03/0

1/20

07

03/0

1/20

08

03/0

1/20

09

03/0

1/20

10

Daily Factor GDP growth

Filter uncertainty, GDP

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

01/1

0/20

08

08/1

0/20

08

15/1

0/20

08

22/1

0/20

08

29/1

0/20

08

05/1

1/20

08

12/1

1/20

08

19/1

1/20

08

26/1

1/20

08

03/1

2/20

08

10/1

2/20

08

17/1

2/20

08

24/1

2/20

08

31/1

2/20

08

07/0

1/20

09

14/0

1/20

09

21/0

1/20

09

28/0

1/20

09

D W M Q Filter Uncertainty (rhs)

Forecasting the Great Recession

-2

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.2501

/10/

2008

08/1

0/20

08

15/1

0/20

08

22/1

0/20

08

29/1

0/20

08

05/1

1/20

08

12/1

1/20

08

19/1

1/20

08

26/1

1/20

08

03/1

2/20

08

10/1

2/20

08

17/1

2/20

08

24/1

2/20

08

31/1

2/20

08

07/0

1/20

09

14/0

1/20

09

21/0

1/20

09

28/0

1/20

09

D W M Q Fcst (rhs) Outturn (rhs)

Does information help improving forecastingaccuracy?Root Mean Squared Forecast Error (RMSFE)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

Q0 M

1 D7

Q0 M

1 D1

4

Q0 M

1 D2

1

Q0 M

1 D2

8

Q0 M

2 D7

Q0 M

2 D1

4

Q0 M

2 D2

1

Q0 M

2 D2

8

Q0 M

3 D7

Q0 M

3 D1

4

Q0 M

3 D2

1

Q0 M

3 D2

8

Q+1

M1

D7

Q+1

M1

D14

Q+1

M1

D21

Q+1

M1

D28

Benchmark Monthly BCDC Bridge STD SPF

S&P 500 and its common component at differentlevels of time aggregation

1985 1990 1995 2000 2005 201025

20

15

10

5

0

5

10

15Daily growth rates

1985 1990 1995 2000 2005 201030

25

20

15

10

5

0

5

10

15Month on month growth rates

1985 1990 1995 2000 2005 201035

30

25

20

15

10

5

0

5

10

15Quarter on quarter growth rates

1985 1990 1995 2000 2005 201050

40

30

20

10

0

10

20

30Year on year growth rates

Now-Casting and the Real-Time Data-FlowResearch questions

• How important is nowcasting relative to longer horizonforecasting?

• Can we predict the present? How relevant is informaljudgement?

• How relevant is the conjectural information? How oftenshould we update the predictions?

What have we learned

X Nowcasting is key! Little predictability beyond currentquarter!

X We can predict the present without the need of informaljudgement?.

X It is worth to obsessively monitor conjectural information!Accuracy improves significantly and continuously.

Now-Casting ChinaIn progress, with S. Miranda-Agrippino and M. Modugno

Now-Casting ChinaThe Data

Now-Casting China: The Common Factor

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China in Real-Time

Now-Casting China: What are the Most InformativeData Releases

Now-Casting ChinaIn progress, M. Modugno

Now-Casting in BrazilAn Award-Winning Central Bank

Now-Casting Brazil: The Data

Now-Casting Brazil: The Data

Now-Casting Brazil: The Data

Now-Casting China: What are the Most InformativeData Releases

Now-Casting Brazil: The Data