nowcasting german gdp growth and the real time newsflow
TRANSCRIPT
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RESEARCH & DEVELOPMENT STATISTICS (NBB)
Jean PalateDavid de Antonio Liedo
Christian-Albrechts-Universität zu KielInstitut für Statistik und Ökonometrie
July 2015
Nowcasting German GDPwith
Licensed under the EUPL (http://ec.europa.eu/idabc/eupl). The last updated version of the software can be downloaded herehttps://github.com/jdemetra/jdemetra-app/releases/tag/v2.0.0
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- Humans have limited capacity to process information and interprete it.
- Confirmation bias , wishful thinking, and group think: pervasive in macroeconomic forecasting.
2000
Q1
2000
Q4
2001
Q3
2002
Q2
2003
Q1
2003
Q4
2004
Q3
2005
Q2
2006
Q1
2006
Q4
2007
Q3
2008
Q2
2009
Q1
2009
Q4
2010
Q3
2011
Q2
2012
Q1
2012
Q4
2013
Q3
2014
Q2
2,000,000
2,050,000
2,100,000
2,150,000
2,200,000
2,250,000
2,300,000
2,350,000
2,400,000
2,450,000
2011Q3
EA12 GDP Chain linked volumes (2010), million
euro
LINKING TECHNOLOGY IN A REAL-TIME FORECASTING ENVIRONMENT
Monitoring the macro economy in real time and detecting turning points requires certain skills and intuition
Technology can help …
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LINKING TECHNOLOGY IN A REAL-TIME FORECASTING ENVIRONMENT
Monitoring the macro economy in real time and detecting turning points requires certain skills and intuition
Technology can help …
Red Bull Racing Chief Technical Officer Adrian Newey Source: Mark Thompson/Getty Images AsiaPac
Sebastian Vettel driving for Red Bull Racing in 2010.Photographer: Andrew Hoskins at British Grand Prix
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LINKING TECHNOLOGY IN A REAL-TIME FORECASTING ENVIRONMENT
Monitoring the macro economy in real time and detecting turning points requires certain skills and intuition
Technology can help … TODAY: real-time simulation
Sebastian Vettel driving for Red Bull Racing in 2010.Photographer: Andrew Hoskins at British Grand Prix
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Simulate real-time forecasts
Forecasting uncertainty as a function of the news-flow
REAL-TIME FORECASTING EVALUATION PLUG-IN
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Simulate real-time forecasts
Forecasting uncertainty as a function of the news-flow
Replace the concept of “forecast horizon” by “information set”
REAL-TIME FORECASTING EVALUATION PLUG-IN
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1. WHAT IS JDEMETRA (JD) +
A Real-Time Forecasting Evaluation Library
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1. WHAT IS JDEMETRA (JD) +
2. MODELING THE REAL-TIME NEWSFLOW
A Real-Time Forecasting Evaluation Library
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1. WHAT IS JDEMETRA (JD) +
2. MODELING THE REAL-TIME
NEWSFLOW
3. NEXT STEPS
A Real-Time Forecasting Evaluation Library
GERMAN GDP Defining the calendar Estimation In Sample analysis Out-of-Sample (Real Time simulation) News Analysis
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JDEMETRA+ is Pure Java software• Mainly (>95%) based on libraries written by Research &
Development (NBB)• Complete control• High-performance (compared to Matlab…)• No economic cost for the user: Open Access software licensed under
the EUPL (http://ec.europa.eu/idabc/eupl)• It has been designed for extension (today you will see the proof)
JDEMETRA+ provides many useful services Primary goal remains seasonal adjustment (TRAMO-SEATS and X12). Externalities: temporal disaggregation (Chow-Lin, Fernandez,
Litterman), benchmarking (Denton, Cholette), Outliers detections, chain linking, etc…
On-going: Multivariate models (SUTSE, DFM, BVAR) Dynamic access to different sources: Excel, Txt, SAS, Databases… Rich graphical components Storage of current work through workspace… Graphical interface based on NetBeansInternational Cooperation Maintenance partly ensured by the Bundesbank (X11) Support of the SA Center of Excellence (INSEE, ONS, ISTAT, STATEC,
EUROSTAT…)
1.WHAT IS JD+
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2. MODELING THE NEWSFLOW
EXPECTATIONSformation and
updating
Econometric & Statistical
tools
JD+ defines nowcasting in terms of the dynamic interactions of real world “things”:
A. The newsflow (potentially “Big Data”- V3: Volume/Variety/Velocity)
B. Technologies for signal extraction (e.g. short-term forecasting methods)
C. Interpretation of changes in expectations in terms of the news
Nowcasting model
A
B
C
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2. MODELING THE NEWSFLOW
EXPECTATIONSformation and
updating
Econometric & Statistical
tools Rather than evaluating the properties of a given econometric tool ( ), our aim is to evaluate the “nowcasting model” as a whole.
Real-Time Forecasting Evaluation
A
B
C
B
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SOME DATA ISSUES BEFORE WE START….
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A quick look at Production Index Manufacturing (Germany)Before we start
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(includes manufacturers, mines, and utilities)
Production Index Manufacturing vs Industrial Output
Note 1: Industrial production in manufacturing looks very much like industrial output
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(includes manufacturers, mines, and utilities)
Production Index Manufacturing vs Industrial Output
Note 1: Industrial production in manufacturing looks very much like industrial outputNote 2: Industrial output “first release” is more volatile than the last available series
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Production Index Manufacturing vs Industrial Output(includes manufacturers, mines, and utilities)
Note 1: Industrial production in manufacturing looks very much like industrial outputNote 2: Industrial output “first release” is more volatile than the last available series
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Q1) Can the “prelim” data have a larger variance? It goes against the news hypothesis, but thera are ways to work out a rational explanationQ2) Does it imply that revisions are predictable? Not necesarilyQ3) Which series do we choose? I have no choice: only “first” is available in real-time
This periodogram of the revisions together with Note 2implies that a significant part of the variance of the “first release”seems to be removed in the revision process.
Production Index Manufacturing vs Industrial Output(includes manufacturers, mines, and utilities)
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Industrial Output (MoM% Germany)
Advanced Release calendar and seasonally adjusted
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Industrial Output (MoM% Germany)
Advanced Release calendar and seasonally adjusted
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Similar performance to the market, even if not attempt has been made to exploit residual seasonality/calendar, which could be (not necesarily) predictable
Industrial Output (MoM% Germany)
Advanced Release calendar and seasonally adjusted
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MODELING THE GERMAN REAL-TIME
DATAFLOW
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Some examples for GDP Real-time publication schedule
Real-time data (instead of revised)
Camacho M. and G. Pérez-Quirós (2010) Real-time Real-time
De Antonio Liedo (2014) «Nowcasting Belgium» Real-time Real-time
Banbura, Giannone, Modugno, Reichlin (2012) Real-time Real-timeGiannone, Reichlin and Simonelli (2009) Real-time Real-timeGDPnow Real-time Real-timeBarnett et al. (2014) « nowcasting Nominal GDP» Real-time Real-timeAngelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011)
Stylized Revided
Banbura and Modugno (2014) Stylized Revised
Kuzin, Marcelino and Schumacher (2011) Stylized Revised
Piette (2015) bridge with targeted predictors based on elastic-net
Stylized Revised
REAL-TIME FORCASTING EVALUATION“Small sample” of the literature: Simulating real-time
forecasts in macro since Giannone, Reichlin and Small (2008) and Evans (2005)
Analysis of data revisions:"A Real-Time Data Set for Macroeconomists," Dean Croushore and Tom Stark, Journal of Econometrics 105 (November 2001),
First real-time database for German GDP:Clausen and Meier (2003)
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Some examples for GDP Real-time publication schedule
Real-time data (instead of revised)
Camacho M. and G. Pérez-Quirós (2010) small model/calendar
Real-time Real-time
De Antonio Liedo (2014) «Nowcasting Belgium» Real-time Real-time
Banbura, Giannone, Modugno, Reichlin (2012) Real-time Real-timeGiannone, Reichlin and Simonelli (2009) Real-time Real-timeGDPnow Real-time Real-timeBarnett et al. (2014) « nowcasting Nominal GDP» Real-time Real-timeAngelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011)
Stylized Revided
Banbura and Modugno (2014) Stylized Revised
Kuzin, Marcelino and Schumacher (2011) Stylized Revised
Piette (2015) bridge with targeted predictors based on elastic-net
Stylized Revised
REAL-TIME FORCASTING EVALUATION“Small sample” of the literature: Simulating real-time
forecasts in macro since Giannone, Reichlin and Small (2008) and Evans (2005)
Surprisingly, it took time to formalize the other important dimension of real-time data. First papers to focus on the “real-time dataflow”:-Giannone, Reichlin and Small (2008) , Journal of Monetary Economics-Evans (2005), International Journal of Central Banking
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Some examples for GDP Real-time publication schedule
Real-time data (instead of revised)
Camacho M. and G. Pérez-Quirós (2010) small model/calendar
Real-time Real-time
De Antonio Liedo (2014) «Nowcasting Belgium» Real-time Real-time
Banbura, Giannone, Modugno, Reichlin (2012) Real-time Real-timeGiannone, Reichlin and Simonelli (2009) Real-time Real-timeGDPnow Real-time Real-timeBarnett et al. (2014) « nowcasting Nominal GDP» Real-time Real-timeAngelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011)
Stylized Revided
Banbura and Modugno (2014) Stylized Revised
Kuzin, Marcelino and Schumacher (2011) Stylized Revised
Piette (2015) bridge with targeted predictors based on elastic-net
Stylized Revised
The publication calendar is a key parameter in our forecasting evaluation set-up (GRS2008)
REAL-TIME FORCASTING EVALUATION“Small sample” of the literature: Simulating real-time
forecasts in macro since Giannone, Reichlin and Small (2008) and Evans (2005)
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Some examples for GDP Real-time publication schedule
Real-time data (instead of revised)
Camacho M. and G. Pérez-Quirós (2010) small model/calendar
Real-time Real-time
De Antonio Liedo (2014) «Nowcasting Belgium» Real-time Real-time
Banbura, Giannone, Modugno, Reichlin (2012) Real-time Real-timeGiannone, Reichlin and Simonelli (2009) Real-time Real-timeGDPnow Real-time Real-timeBarnett et al. (2014) « nowcasting Nominal GDP» Real-time Real-timeAngelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011)
Stylized Revided
Banbura and Modugno (2014) Stylized Revised
Kuzin, Marcelino and Schumacher (2011) Stylized Revised
Piette (2015) bridge with targeted predictors based on elastic-net
Stylized Revised
The publication calendar is a key parameter in our forecasting evaluation set-up (GRS2008)
Simplified “vintage-based estimation” only for key variables à la Jacobs and van Norden (2011) or Clements and Galvao (2013): “advanced” vs “last available”
REAL-TIME FORCASTING EVALUATION“Small sample” of the literature: Simulating real-time
forecasts in macro since Giannone, Reichlin and Small (2008) and Evans (2005)
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The publication calendar is a key parameter in our forecasting evaluation set-up (GRS2008)
Simplified “vintage-based estimation” only for key variables à la Jacobs and van Norden (2011) or Clements and Galvao (2013): “advanced” vs “last available”
Our tool will save you a lot of time
REAL-TIME FORCASTING EVALUATION“Small sample” of the literature: Simulating real-time
forecasts in macro since Giannone, Reichlin and Small (2008) and Evans (2005)Some examples for GDP Real-time
publication scheduleReal-time data (instead of revised)
Camacho M. and G. Pérez-Quirós (2010) small model/calendar
Real-time Real-time
De Antonio Liedo (2014) «Nowcasting Belgium» Real-time Real-time
Banbura, Giannone, Modugno, Reichlin (2012) Real-time Real-time
Giannone, Reichlin and Simonelli (2009) Real-time Real-time
GDPnow Real-time Real-time
Barnett et al. (2014) « nowcasting Nominal GDP» Real-time Real-time
Angelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011)
Stylized Revided
Banbura and Modugno (2014) Stylized Revised
Kuzin, Marcelino and Schumacher (2011) Stylized Revised
Piette (2015) bridge with targeted predictors based on elastic-net
Stylized Revised
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1) Just introduce the publication delay for each series ...
2) Decide when to update your forecasts (e.g. in this example, the days when GDP flash, IFO Surveys and industrial production are released)
“vintagebased”
IFOIFO
α t β t
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Q-ML under “weak” cross correlation patterns: Doz et al. (2012)
“vintagebased”
IFOIFO
α t β t
3) next, specify your state=space model: SUTSE, DFM, BVAR
Measurement Equation: y𝑡¿=Z α t+ Λ βt+ξ t ¿
(β tα t)=(T 111 T 12
1
T 211 T 22
1 )( β t−1
α t−1)+…+(T11
𝑝 T 12𝑝
T21𝑝 T 22
𝑝 )(β t −𝑝
αt −𝑝)+(uβ ,tuα ,t)State Equation:
Usual identification assumptions
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Q-ML under “weak” cross correlation patterns: Doz et al. (2012)
“vintagebased”
IFOIFO
α t β t
3) next, specify your state=space model: SUTSE, DFM, BVAR
Measurement Equation: y𝑡¿=Z α t+ Λ βt+ξ t ¿
(β tα t)=(T 111 T 12
1
T 211 T 22
1 )( β t−1
α t−1)+…+(T11
𝑝 T 12𝑝
T21𝑝 T 22
𝑝 )(β t −𝑝
αt −𝑝)+(uβ ,tuα ,t)State Equation:
Usual identification assumptions
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“vintagebased”
IFOIFO
α t β t
3) next, specify your state=space model: SUTSE, DFM, BVAR
Weighted averageof the MoM% factorsto approximate QoQ% rates
Cumulative sum over 12 months
Monthly growth rates
Data can be seasonallyadjusted in real-timeand transformed into growth rates
In this example, most data are alreadytransformed andsurveys can be leftuntransformed
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“vintagebased”
IFOIFO
α t β t
3) next, specify your state=space model: SUTSE, DFM, BVAR
Weighted averageof the MoM% factorsto approximate QoQ% rates
Camacho M. and G. Pérez-Quirós (2010) use «YoY» instead of «Q» to link the PMI, IFO and NBB monthly Surveys in their «eurosting» model for the euro area.Their link also applies to the measurement error (à la Mariano-Murasawa); not in our case.
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… and estimate it
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Principal Components
… and estimate it
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Principal Components
EM algorithmBanbura and Modugno (2010)
… and estimate it
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Principal Components
EM algorithmBanbura and Modugno (2010)
Numerical Optimization Uses EM to initialize. Algorithms:- Levenberg-Marquardt- Broyden–Fletcher–Goldfarb–Shanno Options: - Simplified iterations - Iterations by blocks
Final EM algorithm
… and estimate it
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Principal Components
EM algorithmBanbura and Modugno (2010)
Numerical Optimization Uses EM to initialize. Algorithms:- Levenberg-Marquardt- Broyden–Fletcher–Goldfarb–Shanno Options: - Simplified iterations - Iterations by blocks
Final EM algorithm
… and estimate it
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IN-SAMPLE
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Correlation of Measurement Errors
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Business Expectations IFO
Markit PMI (Manufactures)
Correlation of Measurement Errors
GDP final
GDP flash
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OUT-OF-SAMPLE
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4) Define evaluation sample and dates at which model parameters must be re-estimatedFor univariate models, recursive estimation every month, while multivariate models may be re-estimated once or twice per year, depending on the application
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5) Visualize results
Real GDP growth (flash)
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Real GDP growth (final)
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Real-time updates for GDP growth
Simulated release calendar
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Simulated release calendar
Real-time updates for GDP growth
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Simulated release calendar
Real-time updates for GDP growth
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Simulated release calendar
Real-time updates for GDP growth
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Theoretical Forecasting UncertaintyThe forthcoming (unpredictable) news flow determines the size ofthe RMSE as a function of the information set
Theoretical RMSE around nowcast for GDP (final)
days before (-)or after (+) the end of the quarter
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Theoretical Forecasting UncertaintyThe forthcoming (unpredictable) news flow determines the size ofthe RMSE as a function of the information set
Theoretical RMSE around nowcast for GDP (final)
Empirical(2005-2014)
days before (-)or after (+) the end of the quarter
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Theoretical Forecasting UncertaintyThe forthcoming (unpredictable) news flow determines the size ofthe RMSE as a function of the information set
Theoretical RMSE around nowcast for GDP (final)
Empirical(2005-2014)
!
days before (-)or after (+) the end of the Quarter(notice x-axis isnot scaled)
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Forecasting Uncertainty for Real GDP%
• Toy model with only Industrial Production (preliminary) and IFO Business Expectations performs only a bit worse than the model with ten variables (Camacho M. and G. Pérez-Quirós (2010) advocate for small models)
• Surprising that the introduction of export expectations (Kiel10 X) doesn’t have a larger impact
days before (-)or after (+) the end of the quarter
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Forecasting Uncertainty for Real GDP%
This makes sense(but revised IPI is notavailable in real-time)
• Toy model with only Industrial Production (preliminary) and IFO Business Expectations performs only a bit worse than the model with ten variables (Camacho M. and G. Pérez-Quirós (2010) advocate for small models)
• Surprising that the introduction of export expectations (Kiel10 X) doesn’t have a larger impact
days before (-)or after (+) the end of the quarter
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Forecasting Uncertainty for Real GDP%
Counter Intuitivethat using revised IPIworsens it
This makes sense(but revised IPI is notavailable in real-time)
• Toy model with only Industrial Production (preliminary) and IFO Business Expectations performs only a bit worse than the model with ten variables (Camacho M. and G. Pérez-Quirós (2010) advocate for small models)
• Surprising that the introduction of export expectations (Kiel10 X) doesn’t have a larger impact
days before (-)or after (+) the end of the quarter
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The paradox explainedDoes it make sense that the model is consistent with an inferior performancewhen the quality of the industrial production has improved?
Counter Intuitivethat using revised IPIworsens it
I believe it does
days before (-)or after (+) the end of the quarter
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Hyndman, R. J. and Koehler A. B. (2006). "Another look at measures of forecast accuracy." Diebold, F.X. and R.S. Mariano (1995)Diebold, F.X. (2013), “Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective …”
6) Quantify Results
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NEWS
Banbura and Modugno (2010)Journal of Applied Econometrics
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“News” in the real-time dataflow
Definition: unexpected component of a given data release or revision Mathematically, the vector of news
Synonyms: innovation, surprise, shock
Note 1: This definition implies that news cannot be read if we do not have a prior expectation
Note 2: The vector of news can be large, specially if a given release incorporates historical data revisions
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, 𝐼 𝑣+1 , 𝐼 𝑣+1
𝐼 𝑣+1𝐼 𝑣+1 𝐼 𝑣+1𝐼 𝑣+1
Updating the forecast on the basis of news
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Assume only one indicator is released
*
CES-IFO
“Quality” mattersDefinition: quality is defined here as the correlation between the factor and the news
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Assume only two indicators are released
CES-IFO
MARKIT
“Quality” mattersDefinition: quality is defined here as the correlation between the factor and the news
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Assume one indicator was earlier
*
Markit-PMI is
earliest
“Timeliness” also matters Definition: timeliness refers to the habit of being available at the forecaster’s information set earlier than other indicators
<Weight is higher
Once Markit-PMI is published, the news content would be smaller (because of the correlation with CES-IFO), so the impact“wx news” will be smaller for the subsequent CES-IFO release
1
2
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“Timeliness” also matters
• This simple mathematical expression has explained the importance of timeliness ( and )
• This larger “impact” coefficient is translated into tangible phenomena:
- more citations (FT, Bloomberg)- the ability to have an effect in market expectations- a higher economic value
• The obvious implication: survey data providers may have incetives to release their data as early as possible (without compromising on their quality, which can be objectively evaluated too)
1 2
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Updating the forecast today
Today: 9 july 2015
15 december 2014
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Updating the forecast today
Today: 9 july 2015
15 december 2014
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IPI june CES-IFO CES-IFO
Markit
IPI march
IPI aprilFlash
Markit
CES-IFO
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IPI june CES-IFO CES-IFO
Markit
IPI march
IPI aprilFlash
Markit
CES-IFO
Relative impacts can change if timeliness assumption is modified
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C Getty Images
Photo: Urban Events
You are the pilot
• Think about the most suitable forecasting model
• Understand the data and assess model fit
• Before using your model out-of-sample , use our “simulator” to become aware of the risks
SUMMARY
Features
• Simulates forecasting scenarios using real-time data availability (users can define the release calendar in a simple manner)
• Check whether a new model yields statistically significant gains in forecasting accuracy with respect to alternatives
• Robust quantification of forecast accuracy as a function of the information available (ongoing: test release impacts)
• Many measures of forecast accuracy and possibility to perform analysis by subsamples
(ongoing: Giacomini and Rossi, 2010)
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You are the pilot
• Think about the most suitable forecasting model
• Understand the data and assess model fit
• Before using your model out-of-sample , use our “simulator” to become aware of the risks
• Good luck!
SUMMARY
(α=5%)
Features
• Simulates forecasting scenarios using real-time data availability (users can define the release calendar in a simple manner)
• Check whether a new model yields statistically significant gains in forecasting accuracy with respect to alternatives
• Robust quantification of forecast accuracy as a function of the information available (ongoing: test release impacts)
• Many measures of forecast accuracy and possibility to perform analysis by subsamples
(ongoing: Giacomini and Rossi, 2010)
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Supplementary material
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PERIODOGRAMDecomposes the sum of squares of the
growth rates in terms of the Fourier coefficients
A quick look at Production Index Manufacturing (Germany)
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(includes manufacturers, mines, and utilities)
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(includes manufacturers, mines, and utilities)