a real-time forecasting evaluation library for jdemetra+

RESEARCH & DEVELOPMENT STATISTICS (NBB)

Free and Open Source Software, licensed under the EUPL (http://ec.europa.eu/idabc/eupl). The last updated version can be downloaded here https://github.com/jdemetra/jdemetra-app/releases

Philippe CharlesMatts Maggi

Jean PalateDavid de Antonio Liedo

CIRANO REALTIME 2015 WORKSHOP

Montreal, October 9, 2015

Macroeconomic Monitoring and

Visualizing News

http://ec.europa.eu/idabc/eupl

https://github.com/jdemetra/jdemetra-app/releases

http://www.ntts2015.eu/


Visualizing News

1. WHAT IS JDemetra+ ?

New tool for seasonal adjustment developed by the National Bank of Belgium in cooperation with the Deutsche Bundesbank and Eurostat in accordance with the European Statistical System (ESS) Guidelines

http://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-15-001


Visualizing News

Operationalize the nowcasting process

Visual analysis of news and updates

Real-time simulations

CONTRIBUTION

Specify DFM with complex loading

structures and estimate with ML

Visual decomposition of forecasting

updates in terms of news (big data)

Visual analysis of real-time forecasting

accuracy


New tool for seasonal adjustment developed by the National Bank of Belgium in cooperation with the Deutsche Bundesbank and Eurostat in accordance with the European Statistical System (ESS) Guidelines

http://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-15-001


2. BACKGROUND of JD+N Short-term forecasting Reading News

3. DFM EXAMPLE (A&D, 2010) SPECIFICATION & ESTIMATION VISUALIZATION

Estimation Results News decomposition Real-Time Simulations


Visualizing News




CONTRIBUTION






accuracy

DOWNLOAD




CONTRIBUTION






accuracy

DOWNLOAD NOWCASTING PLUGIN

DOWNLOAD JDemetra+

Have a look at our Wiki to assessthe time you will need to familiarizeyourself with the software

https://github.com/nbbrd/jdemetra-nowcasting


https://github.com/nbbrd/jdemetra-nowcasting/wiki

JDemetra+ is Pure Java software

Mainly (>95%) based on libraries written by R&D (NBB) Complete control of methods at the lowest level: high-performance Free and Open Source Software (FOOS) licensed under the EUPL

JDemetra+ provides many useful services

Primary goal remains seasonal adjustment (TRAMO-SEATS and X12). Temporal disaggregation (Chow-Lin, Fernandez, Litterman),

benchmarking (Denton, Cholette), Outliers detections, chain linking, … Dynamic access to different sources: Excel, Txt, SAS, Databases… On-going extensions: SUTSE, DFM (today) or BVAR Rich graphical components and interface based on NetBeans

International Cooperation Maintenance partly ensured by the Bundesbank (X11) Support of the SA Center of Excellence (INSEE, ONS, ISTAT, STATEC,

EUROSTAT…) A tool for the future: WEB service for computations on the cloud

1. WHAT IS JDemetra+

https://en.wikipedia.org/wiki/Free_and_open-source_software

http://ec.europa.eu/idabc/eupl.html


2. BACKGROUND OF JD+N Short-term forecasting Reading News




Visualizing News

The real-time newsflow: we follow Banbura, Giannone, Modugno and Reichlin (2013) and Banbura, et al. (2011) definition of nowcasting:

(a) “the prediction of the present, the very near future and the very recent past”(b) “the nowcasting process goes beyond the simple production of an early

estimate as it essentially requires the assessment of the impact of new data on the subsequent forecast revisions for the target variable”

2. JDemetra+ NOWCASTING

BACKGROUND The real-time dataflow: we consider the publication schedule as an

essential element, in line with the nowcasting literature (Giannone, Reichlin, Small (GRS), 2008 and Evans, 2005 )

We use a multivariate state-space framework: DFM by Banbura, et al. (2011) . Only a joint model for (X,Y) can be used for (a-b), while “partial models” such as bridge regressions are only valid for (a)

Real Time Data: Croushore and Tom Stark (2001), ALFRED

https://ideas.repec.org/p/eca/wpaper/2013-125192.html


http://www.sciencedirect.com/science/article/pii/S0304393208000652

http://www.ijcb.org/journal/ijcb05q3a4.pdf


https://www.philadelphiafed.org/research-and-data/real-time-center/real-time-data

Some of the literature since GRS (2008) Focus Release Schedule Revisions

Aruoba, Scotti, Diebold (2008) Daily Index Real-time* YES*

Camacho M. and G. Pérez-Quirós (2010) Small-sizedImpact of releases

Real-time YES

Banbura and Modugno (2010) ECB WP / Banbura et al. (2011) EM, News Stylized/R-t* NO/YES*

Giannone, Reichlin and Simonelli (2009) Impact of releases Real-time* YES*

Jacobs and Van Norden (2011) GDP Revisions GDP vintages YES

Angelini, Camba-Mendez, Giannone, Reichlin and Rünstler’11 Impact of releases Stylized NO

Kuzin, Marcelino and Schumacher (2011) MIDAS vsMF-VAR

Stylized NO

Baumeister and Kilian (2011) Oil Prices Real-time YES

Banbura, Giannone, Modugno, Reichlin (2013) Literature SurveyDaily News

Real-time YES

GDPnow (2014) GDP by components

Real-time YES

Aastveit, Ravazzolo, van Dijk (2014) Density Real-time YES

Giannone, Miranda-Agrippino, Modugno (2014) China Real-time YES

ML estimation of a DFMs to decompose forecast revisions in terms of News

[1/3] Reading the newsflow

http://amstat.tandfonline.com/doi/abs/10.1198/jbes.2009.07205

http://onlinelibrary.wiley.com/doi/10.1002/jae.1174/abstract



http://ner.sagepub.com/content/210/1/90


https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp949.pdf?71a8b89de33cbadd1dce25cf0bfa3d52

https://ideas.repec.org/a/eee/intfor/v27y2011i2p529-542.html

https://ideas.repec.org/a/taf/jnlbes/v30y2011i2p326-336.html


https://www.frbatlanta.org/cqer/research/gdpnow.aspx?panel=1

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2602446&download=yes




Real-time YES






Stylized NO



Real-time YES


Real-time YES



[2/3] Release Schedule as an essential element of nowcasting

ML estimation of a DFMs to decompose forecast revisions in terms of News The release schedule is a key parameter in our forecasting evaluation set-up
















Real-time YES






Stylized NO



Real-time YES


Real-time YES



[3/3] Data releases are subject to revisions

ML estimation of a DFMs to decompose forecast revisions in terms of News The release schedule is a key parameter in our forecasting evaluation set-up Following, Kishor & Koenig (2009), we include “advanced” and “final” separately

(but no detailed model for revisions as in Jacobs & Van Norden, 2011 )




















Visualizing News

3. EXAMPLEM1 M2

Real

Model

Inflation

Model

βt

αt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

Aruoba and Diebold (2010) propose two separate one-factor models to

discuss real activity and inflation interactions (M1, M2)

M1 M2

Real

Model

Inflation

Model

βt

αt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)



We add some more variables: Real GDI , Nominal GDI and GDP Advance releases separate from last available (Phil Fed RT Data, ALFRED)

For key variables such as IPI, Employment, real income, CPI, PPI: only first

Related examples: Banbura et al. (2011), Barnett et al. (2014) , Modugno

and Reichlin (2014)

3. EXAMPLE

M1 M2

Real

Model

Inflation

Model

βt

αt

M3

Joint Model

with 2 types

of factors

αt βt

αt

βt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

3. EXAMPLE



We add some more variables: Real GDI , Nominal GDI and GDP Advance releases separate from last available, as in Kishor & Koenig (2009)



and Reichlin (2014)

M1 M2

Real

Model

Inflation

Model

βt

αt

M3

Joint Model

with 2 types

of factors

αt βt

αt

βt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

M4

Joint Model

with 2 types

of factors

αt

αt βt

βt

3. EXAMPLE






and Reichlin (2014)

M1 M2

Real

Model

Inflation

Model

βt

αt

M3

Joint Model

with 2 types

of factors

αt βt

αt

βt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

M4

Joint Model

with 2 types

of factors

αt

αt βt

βt

*MULTIPLELOADINGSTRUCTURES

*MIXED FREQ.:PATTERNSIN LOADINGS

3. EXAMPLE






and Reichlin (2014)






Visualizing News

αtβt

Measurement Equation:

βtαt=T111 T121

T211 T221

βt−1αt−1+⋯+

T11𝑝T12𝑝

T21𝑝T22𝑝

βt−𝑝αt−𝑝+uβ,tuα,tState Equation:

Usual

identification

assumptions

Idiosyncratic terms ξt is iid ~ N 0, R with diagonal covariance

Q-ML under “weak” cross correlation patterns: Doz et al. (2012)

Idiosyncratic terms ξt uncorrelated with the factor innovationsuβ,tuα,t

y𝑟𝑒𝑎𝑙,t = Z αt − Λ βt + ξt M4

SPECIFICATION AND ESTIMATION IN JD+N

Principal Components



EM algorithmBanbura and Modugno (2014)




Numerical OptimizationUses EM to initialize.

Algorithms:

- Broyden–Fletcher–Goldfarb–Shanno

- Levenberg-Marquardt.

Options: - Simplified iterations

- Iterations by blocks

Final EM algorithm







Visualizing News


VISUALIZATION


Ad

van

ced

Re

alG

DP

gro

wth

rate

(de

mea

ne

d)

SHOCK DECOMPOSITION (M4)

VISUALIZATION


Ad

van

ced

No

m. G

DP

gro

wth

rate

(de

mea

ne

d)

SHOCK DECOMPOSITION (M4)

VISUALIZATION

GDP

(last available)

GDP

(final)

Estimation Results News decomposition Real-Time SimulationsCORRELATION

OVERVIEW (M4)

[rho=0.98]

VISUALIZATION

GDP [rho=0.5]

(advanced)

Nom GDP

(advanced)


GDI [rho= -0.54]

(last available)

GDP Deflator

(advanced)

[rho=0.2]

CORRELATIONRELATIVE VIEW (M4)

VISUALIZATION

2014Q3

old

New (27-oct 2014)

Advanced Deflator (log Δ)


VISUALIZATION

2014Q3

Advanced GDP 400xlog Δ New (27-oct 2014)

old


VISUALIZATION

RevisionsInsignificant


VISUALIZATION

News: unexpected component of agiven data release or revision

Mathematically, the vector of news

ℱ𝑣+1𝑟𝑒𝑓𝑟𝑒𝑠ℎ𝑒𝑑

− ℱ𝑣𝑎𝑟𝑐ℎ𝑖𝑒𝑣𝑒𝑑/𝑜𝑙𝑑

≡ 𝐼𝑣+1

=

y(𝑖,𝑡)1 − E y(𝑖,𝑡)1 ℱ𝑣…

y(𝑖,𝑡)J − E y(𝑖,𝑡)𝐽 ℱ𝑣

Weights of the news

E[y𝑘,𝑡 ℱ𝑟𝑒𝑓𝑟𝑒𝑠ℎ𝑒𝑑 − E[y𝑘,𝑡 ℱ𝑜𝑙𝑑

=

𝑗=1

𝐽

w𝑗𝑘,𝑡 y(𝑖,𝑡)𝑗 − E y(𝑖,𝑡)𝑗 ℱ𝑜𝑙𝑑

y𝑘,𝑡

w2𝑘,𝑡

w1𝑘,𝑡 w3𝑘,𝑡

w4𝑘,𝑡

w5𝑘,𝑡

5x1 5x55x1


UNDERSTANDING THE REAL-TIME NEWSFLOW

(𝑟𝑒𝑓𝑟𝑒𝑠ℎ𝑒𝑑)

VISUALIZATION

[w1𝑘,𝑡 , … , w5

𝑘,𝑡 ] = E[y𝑘,𝑡 I𝑣+1′ ]E I𝑣+1 I𝑣+1

′ −1

[w1𝑘,𝑡 , … , w5

𝑘,𝑡 ] = E[y𝑘,𝑡 I𝑣+1′ ]E I𝑣+1 I𝑣+1

′ −1

News: unexpected component of agiven data release or revision

Mathematically, the vector of news

ℱ𝑣+1𝑟𝑒𝑓𝑟𝑒𝑠ℎ𝑒𝑑

− ℱ𝑣𝑎𝑟𝑐ℎ𝑖𝑒𝑣𝑒𝑑/𝑜𝑙𝑑

≡ 𝐼𝑣+1

=

y(𝑖,𝑡)1 − E y(𝑖,𝑡)1 ℱ𝑣…

y(𝑖,𝑡)J − E y(𝑖,𝑡)𝐽 ℱ𝑣

Weights of the news

E[y𝑘,𝑡 ℱ𝑟𝑒𝑓𝑟𝑒𝑠ℎ𝑒𝑑 − E[y𝑘,𝑡 ℱ𝑜𝑙𝑑

=

𝑗=1

𝐽

w𝑗𝑘,𝑡 y(𝑖,𝑡)𝑗 − E y(𝑖,𝑡)𝑗 ℱ𝑜𝑙𝑑

y𝑘,𝑡

w2𝑘,𝑡

w1𝑘,𝑡

w4𝑘,𝑡

w5𝑘,𝑡

5x1 5x5

w3𝑘,𝑡

5x1


(𝑟𝑒𝑓𝑟𝑒𝑠ℎ𝑒𝑑)

UNDERSTANDING THE REAL-TIME NEWSFLOW

VISUALIZATION


DEFINING THE REAL-TIME SIMULATION

A. Define the publication delays for all data releases

B. Define the dates at which we want to calculate theforecasts (e.g. every time there is a new release)

C. Use A. and B. to determine the sequence of information sets ℱ1 ⊆ ℱ2 ⊆ ⋯ ⊆ ℱ𝑣

D. Re-estimate model parameters for each info set

and calculate E[ 𝒁−𝑖 ℱ𝑖 for 𝑖=1… v

𝒁−𝑖 = ℱ𝑣 - ℱ𝑖 represents our evaluation sample

VISUALIZATION


RESULTS OF THE REAL-TIME SIMULATION

Fixed horizons: Precision of the forecasts for each periodgiven an information assumption or horizon

Real-Time Perspective over the whole evaluation sample: display for each indicator the evolution of the point forecasts until the actual release date

RMSE graph: Average (over a subsample) precision of theforecasts for all variables as function of ℱ1 ⊆ ℱ2 ⊆ ⋯ ⊆ ℱ𝑣 . Is that function consistent with the one given by the theoreticalprecision (e.g. smoothed covariance)?

VISUALIZATION


RESULTS OF THE REAL-TIME SIMULATIONAdvanced RGDPFixed horizon

VISUALIZATION


RESULTS OF THE REAL-TIME SIMULATION Fixed horizonAdvanced RGDP

VISUALIZATION






VISUALIZATION


RESULTS OF THE REAL-TIME SIMULATION R-T Perspective

NBER Reference Dates

RGDP (M3)

VISUALIZATION


RESULTS OF THE REAL-TIME SIMULATION R-T Perspective

NBER Reference Dates

NGDP (M3)

RGDP (M3)

VISUALIZATION






VISUALIZATION



GDP final

GDP advanced

RMSE graph RGDP (last)

VISUALIZATION


RESULTS OF THE REAL-TIME SIMULATION RMSE graph RGDP (adv)

VISUALIZATION

I. TECHNOLOGY: A Web ServiceIt will allow for computations on the cloud

II. STATISTICS: Efficient and robust diffuse initializationUseful for SUTSE or BVAR models

III. ECONOMICS: From nowcasting to forecasting Incorporating data that refer to expecations about a distant future (quantitative and qualitative surveys, or financial variables)

4. CHALLENGES

DE JONG P. AND CHU-CHUN-LIN S. (2003): "Smoothing with an Unknown Initial Condition", Journal of Time Series Analysis, 24, 2, 141-148.DURBIN J. AND KOOPMAN S.J. (2012): "Time Series Analysis by State Space Methods", second edition. Oxford University Press.

Annex- Quick Start Guide- More material

NOWCASTING WITH JD+A QUICK START GUIDE




Nowcasting plug-in

ONLINE VERSION with examples for Belgium, euro area, Germany and UShttps://github.com/nbbrd/jdemetra-nowcasting/wiki

https://github.com/nbbrd/jdemetra-nowcasting/wiki

OVERVIEW

Download the executable. Make sure you have the last

verion of Java and open JD+

Install the nowcasting plug-in (+N ) and start using thesoftware

Create a new workspace and load the data or open anexisting one

Estimation

Reading News

Forecasting Simulation and Evaluation

10 MINUTES FOR INSTALLATION AND FIRST EXPLORATION

ADVANCED FUNCTIONALITIES

DOWNLOAD

Download the last release of JDemetra+

Save

jdemetra-vx.x.x.zip it in

destination folder, unzip

and install.

Navigate to the folder

nbdemetra/bin and

double click on

nbdemetra.exe (32-bit

system version) or

nbdemetra64.exe (64-bit

system version)

Important: All computers

have Java installed, but

you need version SE 7 to

be able to run JD+

Download Java here:https://java.com/en/download/

https://github.com/jdemetra/jdemetra-app

Download the executable. Install the nowcasting plug-in (+N ) and get started Create a new workspace and load the data Estimation Reading News Forecasting Simulation and Evaluation

DOWNLOAD JDemetra+


https://java.com/en/download/


INSTALL

The nowcasting plug-in is on of the many

elements of the JD+ software, but it is not installed

by default

The plug-ins needed for the nowcasting software are

nbdemetra-core2-2.0.0.nbm

nbdemetra-dfm-2.0.0.nbm

They are available

at https://github.com/jdemetra

You can create a folder

called plugins inside the parent

folder nbdemetra to store these

two files.

Open the JD+ software if you

have not done it yet: navigate

to the folder nbdemetra/bin

and double click on

nbdemetra(64).exe

Click on Tools/Plugins, and go

to the tab “downloaded”. Click

on “Add Plugins….”, go to the

folder where you have saved

the two plug-ins.


DOWNLOAD NOWCASTING PLUGIN

https://github.com/jdemetra

https://github.com/nbbrd/jdemetra-nowcasting

Explore the interface

1) Workspace



2) Data Providers



3) One tab for each model



4) Space for charts, which are generated using the Tools menu

(data can be dragged and dropped into this space)


Load Data

Before proceeding, make sure you have an excel

book with sheets containing dates in the first column and the name of

the variables in the first row

Right-click anywhere inside providers

(Spreadsheets, for data in excel)

Open and click on the square

marked in red for browsing:

look for the file with your data

After having successfully opened

all the data files that you need

you will have a tree structure

like the one shown below

The data frequency for each

series is automatically identified,

also when frequencies are mixed

within the same sheet


Create a new Workspace

Click on Statistical methods/Nowcasting/…

…/Dynamic factor model


… new Workspace

• A workspace containing only model DfmDoc-1, default name, is created.

• Drag and drop the data from the Providers tab to the workspace and your first model will be

automatically specified (with 2 factors, following a VAR(2), by default). Change default options

• Transform the data, remove seasonal factors, and decide how the factors are linked to the

observables. If you are planing to evaluate your model, parameterize the real time dataflow


• Workspaces may contain model specifications and data that have previously been

saved. They are accesible using File/Open workspace

Open existing Workspace


• Workspaces may contain model specifications and data that have previously been

saved. They are accesible using File/Open workspace

• Once the workspace is opened, it can be used in many possible ways: a model can be

evaluated or the data can be refreshed (DFM_r2p2/refresh) or archived

(DFM_r2p2/archive) in order to look at the news the next time new data are available.

This action will lock the model and will prevent you from changing it.



• The model can also be changed by unlocking it, and new models can be constructed

using any of the models available as a starting point (right-clicking on any given model,

and select “clone”).



Open existing Workspace• The model can also be changed by unlocking it, and new models can be constructed

using any of the models available as a starting point (right-clicking on any given model,

and select “clone”).

• For example, let’s open the worskpace BM2014_JAE, which contains a couple of

model specifications used in the paper by Banbura and Modugno (2014). In particular,

DFM_r2p2 is a model where 101 variables load on two factors, which follow a VAR of

order 2. By using the option clone, we have made a copy of that model, and then

increased the number of factors to 5. We renamed the resulting model as DFM_r5p2

For each model:

• Change options and loading structure

• Parameterize the dataflow

• Transform the data


Estimate Models• Move from the tab Model to the tab Processing

• Choose EM, numerical optimization, or a combination of both:


Estimate Models• Let’s estimate our model with N=101 variables until year 2000, 𝑟 = 5, 𝒑 = 2

• Multiple options are possible to estimate the parameters. Numerical

optimization and the EM algorithm play the most important role:


ytN×1

= ZN×r

𝐟tr×1

+ 𝜉𝑡N×1

𝑤𝑖𝑡ℎ 𝜉𝑡 ∼ 𝑁 0, 𝑅 , where 𝑅 is diagonal

𝐟𝑡 = A1r×r

f𝑡−1 +⋯+ A𝒑𝐟𝑡−𝒑 + 𝑢𝑡 𝑤𝑖𝑡ℎ 𝑢𝑡 ∼ 𝑁 0, 𝑄 ,with 𝑢𝑡 ⊥ 𝜉𝑡

Numerical optimization Expectations maximization (EM) algorithm

STEP 1: Principal components (PC) to extract the factors and run OLS regressions to get Z, R,A, and Q


STEP 2: Use PC as starting value of anoptimization procedure for the pseudo-likelihood (𝒑 = 1 instead of 2). Not necessary to iterate until convergence

STEP 2: Use PC as an initial condition for theEM algorithm and run it until the likelihooddoes not increase by much at each iteration

STEP 3: Use the estimator obtained in theprevious step as a starting value of theoptimization procedure for the likelihood

STEP 3: Use the estimator obtained in theprevious step as a starting value of theoptimization procedure for the likelihood

Estimate Models


Numerical optimization


STEP 2: Use PC as starting value of an optimization procedure for thepseudo-likelihood (𝒑 = 1).Not necessary to iterate untilconvergence: in this case, 15 iterations.

STEP 3: Use the estimator obtainedin the previous step as a startingvalue of the optimization procedure for the true likelihood (𝒑 =2 in this example)

Estimate Models


Numerical optimization


STEP 2: Use PC as starting value of an optimization procedure for thepseudo-likelihood (𝒑 = 1).Not necessary to iterate untilconvergence: in this case, 15 iterations.

STEP 3: Use the estimator obtainedin the previous step as a startingvalue of the optimization procedure for the true likelihood (𝒑 =2 in this example)

The model parameters are divided in two blocks: {Z,R} and {A1,…,Ap,Q}. While the EM algorithm requires oneiteration per block, the numerical optimization allows us to set the number of iterations desired per blockThe mixed estimation option alternates between the iterations for the VAR block: {A1,…,Ap,Q} alone andsimultaneous iterations for the two blocks

Estimate Models


EM algorithm


STEP 2: Use PC as an initialcondition for the EM algorithm andrun it until the likelihood does notincrease by much at each iteration

STEP 3: Use the estimator obtainedin the previous step as a startingvalue of the optimization procedure for the likelihood. Here, simplified model iterations is set to zero so that the likelihood continues improving

A final run of the EM algorithm is also possible, but useless in practice. Using the EM results as startingvalues for numerical optimization, as ilustrated here, is standard practice, but not the other way round

Estimate Models


Numerical optilization wins in this case, which corresponds to the vintage of November 1999. When the full sample is used, both method yield the same likelihood

In both cases, the largest gains take place after very few iterations Small jumps in the likelihood happen when switching from pseudo-likelihood (restricted

model) to true-likelihood (unrestricted model), and from “VAR block” optimization to “allparameters”

Numerical optimization required 5 minutes in this example (N=101, r=5, p=2). For largermodels numerical optimization should be used only after the EM algorithm

Estimate Models


• Output tab of each model contains all the results

1. Model specification, estimation options and input data (both original and transformed)

2. Estimation results:

i. Model parameters: Z (Loadings ), A1, … , Ap(VAR transition), 𝑅 (measurement error), Q

ii. Shock decomposition from Choleski factorization of Q (the VAR innovations covariance )iii. Fit

• Signals vs Data: Plot for each variable in yt and the resulting signal 𝔼 Z𝐟t| y1 , … , yT• Residuals, yt - 𝔼 Z 𝐟t| Info Set , can be easily analysed. The program displays a

table with their variance and their autocorrelation of order one (using red colors when

they are significant). The cross-correlation patterns are also displayed in matrix form

and highlighted. For the analysis of large datasets, a more powerful tool can be used

(Schema ball)

3. Factors: A plot of the so-called “smoothed” underlying factors 𝔼 𝐟t| y1 , … , yT and the uncertainty

around them. In the same plot, the “filtered” factors are displayed 𝔼 𝐟t| y1 , … , yt−1

4. Analysis of impulse response functions and variance decompositions consistent a the choleski

decomposition. Advanced users can easily introduce restrictions in the loadings in order to

interpret those results from an economic point of view (Structural VAR analysis)

5. Forecasts based on the information set used during the estimation. The last branch will display

results only if simulations based on recursive or rolling estimation schemes have taken place.

ytN×1

= ZN×r

𝐟tr×1

+ 𝜉𝑡N×1

𝐟𝑡 = A1r×r

f𝑡−1 +⋯+ A𝒑𝐟𝑡−𝒑 + 𝑢𝑡

Estimate Models


• The results obtained using the two different estimation options can be analysed by

clicking on the Output tab of each model. The results are different when the data

corresponds to the vintage 1999m10 (middle of November of 1999), which implies

particularily short series for survey data.

• Estimation based on the EM algorithm yields a better in-sample fit for GDP growth, with a

Estimate Models


• We have obtained different estimation results of the same model. What does it

mean? The model based on the EM algorithm obtains factors that account for a large

proportion of the variance of GDP growth and survey data, while the model based on

numerical optimization tuns out to yields a better fit for oil prices at the cost of accounting

for a smaller fraction of GDP growth.

• A quick overview of the model’s in-sample fit. The conclusions described above can

be drawn by comparing the standard deviation of the residuals of DFM_r5p2_NumOptim

and DFM_r5p2_EmAlgorithm. The residuals (see next slide) can be computed as the

difference between the time series Signals and Data, which are displayed in

Estimation/Fit

Estimate Models


Estimate Models


• Forecasting performance? We have seen both estimation methods yield

similar nowcasts for GDP 1999Q3 in this particular example. However, the

forecasting path for both models can be different not only for GDP but also for

the rest of the variables.

Estimate Models


• Before entering the topic of forecasting evaluation, let’s calculate the GDP

forecasts resulting from both estimation approaches when we assume perfect

foresight of all survey or survey+financial data. Is that information useful to

capture the factors underlying the Great Recession?

• To calculate the forecasts under such scenario:

1. Update those series in your excel file

2. Refresh your data

3. Click on the green arrow of the processing tab to re-run the model with the

refreshed data (not that this does not require to re-estimate the

parameters)

Estimate Models


• Before entering the topic of forecasting evaluation, let’s calculate the GDP

forecasts resulting from both estimation approaches when we assume perfect

foresight of all survey or survey+financial data. Is that information useful to

capture the factors underlying the Great Recession?

Estimate Models• The results of the estimation for both models can be seen by clicking on the Output tab

• We also keep track of the original data prior to transformation and everything related to model specifications and

estimation options (Input tree). The Estimation tree hopefully contains everything you need, just explore it.

• Finally, the Forecasts tree contains predictions and forecasting intervals. The Forecasts Simulation tree is empty.


• If you are planing to use your model DFM_r5p2 for nowcasting:

a) archive it (data and model freeze)

b) save it , and close JDemetra+. You will be able reopen it in the future: File/Open Recent Works


Reading News

Reading news with model DFM_r5p2

• If you are planing to use your model DFM_r5p2 for nowcasting:

a) archive it (data and model freeze)

b) save it , and close JDemetra+. You will be able reopen it in the future: Open Recent Works

• Suppose that after some time (date dd/mm/yy), you have new data releases and you want to update

your forecasts without loosing your previous results, which you may want to keep to make sure you can

reproduce them in the future.

1. Open the workspace containing your model and save it as BM2014_JAE_ dd/mm/yy (for

example). You have made a copy of your previous workspace before updating your results.

2. Second, open JDemetra+ and open the workshpace BM2014_JAE_ dd/mm/yy. Then, Refresh

the data. Your Excel file with the updated data and the variables themselves should have

exactly the same name and, in principle, remain in the same location


Reading news with model DFM_r5p2

A. Go to the News tab, and click on New Forecast to know whether all the new

data releases where better or worse than predicted by the model, and how the

unexpected component weights on the forecasting updates for all variables

B. Click on Impacts to visualize a decomposition of the forecasting revision (New

Forecast minus Old Forecast) in terms of news, for each one of the forecast

horizons. The impacts are equal to the news times the weights


Reading news: a real example


26 oct 2015 4:00 am

A. New Forecast



26 oct 2015 4:00 am

6 nov 2015 2:00 am

A. New Forecast



26 oct 2015 4:00 am

6 nov 2015 2:00 am

Official release?

?

A. New Forecast



13 nov 2015 2:00 amDESTAT releases 0.3%

26 oct 2015 4:00 am

6 nov 2015 2:00 am

0.3%

Actual data (solid) and Forecasts (dashed)

A. New Forecast



26 oct 2015 4:00 am

6 nov 2015 2:00 am

Forecasting revisions

A. New Forecast

B. Impacts

Real-Time Simulations: Set-up

• In this simple example, we will consider a very stylized calendar of data releases, following exactly the paper by

Banbura and Modugno (2014).

• We go to the simulation tab and then click on tools in order to fix the dates at which we want the model to be re-

estimated: once a year in this example. The evaluation sample will correspond to the last 10 years in this example.

• Finally, we click on the green arrow to run the simulation (we will be asked to store the forecasts in a folder)


Real-Time Simulations: Results

• The results will appear in the tab Output, in the Forecasting Simulation tree:


• Notice that the evaluation sample and the forecasting “information leads or lags

(=horizon)” can be modified for each variable.


• The simulation results will be available only for the variables in which you have placed a

“blue eye” during the specification stage (Model Tab). Nevertheless, all variables will be

part of the estimation


• The calendar sign on each variable will ensure that the forecasts are updated at the same time that

variable is released . That’s how euro area GDP for 2008Q2 was updated in real time (blue line)

If you place in all

variables, you will have

very frequent forecasting

updates.

If you place it only in one

variable that is released

each month (like in this

example), then you will

only have one forecasting

update per month


• The simulation results will be available only for the variables in which you have placed a

“blue eye” during the specification stage (Model Tab). Nevertheless, all variables will be

part of the estimation


• The calendar sign on each variable will ensure that the forecasts are updated at the same time that

variable is released . That’s how euro area GDP for 2008Q2 was updated in real time (blue line)

If you place in all

variables, you will have

very frequent forecasting

updates.

If you place it only in one

variable that is released

each month (like in this

example), then you will

only have one forecasting

update per month

How do we obtainthis graph?



• Output tab, Forecasting Simulation tree/”Real time perspective”

• DFM forecasts in blue, univariate (TRAMO) forecasts in green, realization in red

• Zooming in (double click) / zooming out (right click)

ADDITIONAL MATERIAL

, 𝐼𝑣+1 , 𝐼𝑣+1

𝐼𝑣+1𝐼𝑣+1 𝐼𝑣+1 𝐼𝑣+1

THE WEIGHTS ARE RELATED TO THE CALENDAR

*

Definition: quality is defined here as the correlation between the factor and the news

Assume only two indicators are released


Assume only two indicators are released

Definition: quality is defined here as the correlation between the factor and the news


Assume one indicator was earlier

*

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 besmaller (because of the correlationwith CES-IFO), so the impact“wx news” will be smaller for the subsequent CES-IFO release


“Timeliness” also matters

• This simple mathematical expression has explained the importanceof 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 incetivesto release their data as early as possible (without compromising on their quality, which can be objectively evaluated too)

y𝑘,𝑡

w2𝑘,𝑡

w1𝑘,𝑡

w4𝑘,𝑡

w5𝑘,𝑡

w3𝑘,𝑡

[w1𝑘,𝑡 , … , w5

𝑘,𝑡 ]

W

= E[y𝑘,𝑡 Iv+1′ ]

A

E I𝑣+1 I𝑣+1′ −1

(LL′)−1

computeNewsCovariance() computes a lower triangular matrix

lcov_, which is the choleski factor of E I𝑣+1 I𝑣+1′

weights(int series, TsPeriod p) first stores E[y𝑘,𝑡 Iv+1′ ] in a DataBlock a,

and then it solves the system without the need to invert E I𝑣+1 I𝑣+1′

The weights (see JDemetra+ wiki) are computed by solving two systems,

which can be represented in matrix notation:

• First, calculate B = wL by solving LB’ =A’ using the rsolve methodcontained in the class lower triangular (a child of the class Matrix)

• Second, calculate w by solving wL=B using the lsolve method

COMPUTATION OF WEIGHTS

https://github.com/jdemetra/jdemetra-core/wiki/Matrix

Matrix computation

Basic data handling

Complex, polynomials

Linear filters

Function optimization

Time series, calendars, regression variables...

Basic statistics

Utilities...

Basic econometrics

Arima, Ucarima

VAR,Dynamic factor model

Seats

X11

State space framework

Arima modelling

RegArima

Tramo

Seasonal adjustment

Structural models...

Benchmarking, temporal disaggregation

M3

Joint Model

with 2 types

of factors

αt βt

αt

βt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

Aruoba and Diebold (2010) propose two separate one-factor models to discussreal activity and inflation interactions (M1, M2)

We add some more variables: Real GDI , Nominal GDI and GDP

Advance releases separate from last available, as in Kishor & Koenig (2009) For key variables such as IPI, Employment, real income, CPI, PPI: only first

Related examples: Banbura et al. (2011), Modugno and Reichlin (2014)

Real and inflation interactions with M3

M1 M2

Real

Model

Inflation

Model

βt

αt

M4

Joint Model

with 2 types

of factors

αt

αt βt

βt

M3

Joint Model

with 2 types

of factors

αt βt

αt

βt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

M3 no Real M3

Joint Model

with 2 types

of factors

αt βt

αt

βt

Real GDI

Nominal GDP, GDI

Inflation (AD,2010)

Real (ASD,2009)

M3 no INF

Real and inflation interactions with M3

Role of real variables vs inflation variables

a real-time forecasting evaluation library for jdemetra+

Data & Analytics

real time

intuition technology

macro economy

realtime forecasts

certain skills

big data

soft data

adjusted data