Nominal GDP now-casting

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Nominal GDP now-casting

Michele Modugno, ECARES

Lucrezia Reichlin, London Business School and CEPR Frontiers of Macroeconometrics

Bank of England, UCL and cemmap workshop 25 and 26 April 2013

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Motivation

• Since 2008 great recession, prolonged weakness of real economic activity

• Central Banks have been increasingly focusing on real economic activity ...

• Different proposals: dual target, forward guidance, nominal GDP targeting generally within flexible inflation targeting framework

Some problems:

• Real time monitoring of nominal GDP: nominal GDP published late and subject to large revisions

• Not clear exploitable Phillips curve trade-off in the data

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Real time monitoring of economic variables

• In central banks (and markets) there is a long tradition of monitoring real GDP in real time

• Since Giannone et al (2008) it has been shown by a large literature that this can be done via formal models in an effective way →→ nowcasting:

contraction of the terms Now and Forecasting Meteorology Now-casting

forecasting up to 6-12 hours ahead long tradition, since 1860

Economic Now-casting

forecasting the near future, the present and even recent past

• Most empirical work is on real GDP

• What is the case for now-casting nominal GDP?

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The idea of now-casting: learning from markets

Market participants can be viewed as now-casters

• they routinely monitor all macroeconomic data to get a view on current and future fundamentals and their effects on

policy

• The relevant information on the state of the economy is

conveyed to markets through the release of macroeconomic reports

• Market expectation for the headlines of these reports are collected up to the day before the actual release of the

indicator and distributed by data providers (i.e. Bloomberg)

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

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

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The Real-Time Data-Flow: This Month...

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Now-casting real GDP

Motivation:

• It arrives late and it is subject to large revisions

• The present is the only horizon of predictability - unpredictability beyond current quarter (cite)

• Accuracy of macroeconomic projections heavily rely on starting conditions

Empirical results (see Banbura et al 2012, Handbook of Forecasting):

1. Only real variables matter

2. Timeliness matters: in particular survey at the beginning of the quarter

3. Information matters

4. Daily model with daily financial variables does not outperform monthly real model

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Now-casting - other key variables?

• Motivation less clear for CPI inflation: available monthly, less revised

although Modugno (2012) shows in a daily model of CPI inflation that high frequency information on oil price helps providing an accurate early signal on current inflation

• Clear motivation for nominal GDP: available quarterly, delay of publication, large revisions

However no now-casting studies or in general predictability studies are available for nominal GDP

Conjecture: daily model with financial variables, possibly monetary aggregates, may do well

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Plan of the presentation

1. Introduce the idea of now-casting 2. Introduce the model

3. Apply to nominal GDP

-- data: daily and weekly and oil prices, daily financial variables, monthly price and real variables, quarterly deflator and real GDP -- evolution of the precision in relation to the flow of data

publications throughout the quarter

-- effect of unanticipated component of data releases (news) on the now-cast

-- Do for nominal GDP, real GDP and the deflator

-- Analyze two models: daily model and monthly model

4. Analyze relation between nominal and real variables through the analysis of the news and through a conditional forecast exercise

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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 the real-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?

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The Real-Time Data-Flow: Yesterday in Europe

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A model of Now-Casting

• y_t^Q: GDP at time t.

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

Nowcasting of y_t^Q: orthogonal projection of y_t^Q on the available information:

E h

y_t^Q|Ω_vi ,

The information set Ω_v has particular characteristics:

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

2 it contains mixed frequency series, in our case monthly and quarterly

3 it could be large

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E h

y_t^Q|Ω_vi ,

3 it could be large

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E h

y_t^Q|Ω_vi ,

3 it could be large

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Further features

• Projections need to be updated regularly E

h

y_t^Q|Ω_vi , Eh

y_t^Q|Ω_{v +1}i , ...

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

Typically the intervals between two consecutive data releases are short (possible couple of days or less) and change over time.

Consequently, v has high frequency and it is irregularly spaced.

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News and nowcast revisions

• New release ⇒ the information set expands (new releases): Ωv ⊆ Ω_{v +1}[we are abstracting from data revisions]

• Decompose new forecast in two orthogonal components: E

h

y_t^Q|Ω_{v +1}i

| {z }

new forecast

= Eh

y_t^Q|Ω_vi

| {z }

old forecast

+ Eh

y_t^Q|I_{v +1}i

| {z }

revision

,

I_{v +1}information in Ω_{v +1}“orthogonal” to Ωv

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

E h

y_t^Q|Ω_{v +1}i

− Eh y_t^Q|Ω_vi

| {z }

forecast revision

= X

j∈Jv +1

bj,t,v +1 xj,Tj,v +1− Exj,Tj,v +1|Ω_v

| {z }

news

.

For detailed derivation see Banubra and Modugno, 2008.

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• Decompose new forecast in two orthogonal components:

E h

y_t^Q|Ω_{v +1}i

| {z }

new forecast

= Eh

y_t^Q|Ω_vi

| {z }

old forecast

+ Eh

y_t^Q|I_{v +1}i

| {z }

revision

,

E h

y_t^Q|Ω_{v +1}i

− Eh y_t^Q|Ω_vi

| {z }

forecast revision

= X

j∈Jv +1

| {z }

news

.

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• Decompose new forecast in two orthogonal components:

E h

y_t^Q|Ω_{v +1}i

| {z }

new forecast

= Eh

y_t^Q|Ω_vi

| {z }

old forecast

+ Eh

y_t^Q|I_{v +1}i

| {z }

revision

,

E h

y_t^Q|Ω_{v +1}i

− Eh y_t^Q|Ω_vi

| {z }

forecast revision

= X

j∈Jv +1

| {z }

news

.

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What kind of framework?

Three desiderata:

1 can capture joint dynamics of inputs and target

2 can be estimated on many series while retaining parsimony

3 can handle jagged edged data and mix frequency

Idea: use parsimonious model that can be cast in state space form and use Kalman filter to project and to handle jagged edged data

Evans 2005 IJCB; Giannone, Reichlin and Small, 2008 JME

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Computing projections. What kind of model?

The dynamic factor model

xt = µ + Λft+ εt,

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

• Λfactor loadings

• Factors are modelled as a VAR process:

f_t = A₁f_t−1+ · · · +A_pf_t−p+u_t

Parsimonious and robust model for large macroeconomic datasets.

- Forni et al. 2000 REStat, ...

- Stock and Watson, 2002 JASA, JBES ...

- Bernanke and Boivin, 2002 JME;

- Bai, 2003 Econometrica;

- Giannone et al. 2005 Macroeconomic Annual See keynote Lecture by James Stock tomorrow morning

Alternative: Bayesian Vector Autoregression

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Problems and solutions

• Missing data

Kalman filter and smoother can be used to obtain, in an efficient and automatic manner, the projection for any pattern of data availability in Ω_v as well as the news I_{v +1}and expectations needed 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

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State space representation with mixed frequencies

Example: Let Y_t^Qdenote the vector of (log of) the quarterly flow series.

We assume that Y_t^Q is the sum of daily contributions X_t

Y_t^Q=

t

X

s=t−k +1

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

Hence we will have that the stationary series y_t^Q =Y_t^Q− Y_t−k^Q can be written as:

y_t^Q=k





t

X

s=t−k +1

t + 1 − s k xs+

t−k

X

s=t−2∗(k −1)

s − t + 2 ∗ k − 1

k xs



, t = k , 2k , . . . ,

where xs=Xs− X_s−1can be thought of as an unobserved daily growth rate (or difference).

See also Modugno 2011: Nowcasting Inflation

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Data Table

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Some details

• Consider also monthly model with higher frequency variables aggregated to monthly and made available at the beginning of the month

• Results based on pseudo-real time

• For daily model the forecast is performed every day, for the monthly model every week

• Nominal GDP derived from aggregating real GDP and the deflator appropriately

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Daily now-cast of GDP, evolution of RMSFE, news

• INSERT CHARTS

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Now-cast

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Evolution of RMSFE over the quarter daily r=1, monthly with r=1 and r=2

0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75

Q-1 M1 D7 Q-1 M1 D14 Q-1 M1 D21 Q-1 M1 D28 Q-1 M2 D7 Q-1 M2 D14 Q-1 M2 D21 Q-1 M2 D28 Q-1 M3 D7 Q-1 M3 D14 Q-1 M3 D21 Q-1 M3 D28 Q0 M1 D7 Q0 M1 D14 Q0 M1 D21 Q0 M1 D28 Q0 M2 D7 Q0 M2 D14 Q0 M2 D21 Q0 M2 D28 Q0 M3 D7 Q0 M3 D14 Q0 M3 D21 Q0 M3 D28 Q1 M1 D7 Q1 M1 D14 Q1 M1 D21

r1p1 r2p1 daily RW

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news

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Comments

• The model does well

• Daily model outperforms monthly for nominal GDP in forecast and early now-cast : timely oil prices and financial news

matter then

• These news affect nominal GDP because they affect the deflator (to check)

• Some of the features of real GDP forecasting are confirmed -- information matters

-- real variables news have sizeable effect -- timeliness matters

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Monthly model:

Now-cast nominal GDP

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

r1p1 r2p1 actual

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Monthly model:

Now-cast deflator

-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4

r1p1 r2p1 actual

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Monthly model:

Now-cast real GDP

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

r1p1 r2p1 actual

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Nominal GDP

0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75

r1p1 r2p1 RW

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Observations

• The monotone behavior of the RMSFE is less clear in the monthly model since the high frequency variables are

aggregated and artificially made available at the beginning of the month

• From the end of the first month of the current quarter (wen the deflator and real GDP from the previous quarter arrive) incremental news reduce uncertainty and the now-cast model clearly outperform the constant growth model

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Deflator

0.15 0.17 0.19 0.21 0.23 0.25 0.27 0.29

r1p1 r2p1 RW

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Observations

• Not much seems to matter except past real GDP and deflator

• Te second factor helps

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Real GDP

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

r1p1 r2p1 RW

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Observations

• Monotonic pattern confirmed (see Giannone et al 2008 and Banbura et al 2012)

• Important role of the surveys

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Comments

• Inflation - more predictable in an absolute sense since it is less volatile. However harder to beat the random walk benchmark. This is possible only at the end of this first month of the current quarter when the deflator is released

• The release of the deflator has also a major impact on nominal GDP but in the daily model the pattern is declining from previous quarter give the availability of daily financial variables

• Strong case for now-casting nominal GDP – can beat random walk by exploiting timely releases in back-cast, now-cast and forecast

Moreover:

• Two factors fit better the deflator (it captures the price dimension )

• For nominal and real variable the difference between one and two factors is small

Conjecture:

Weak relation between nominal and real side

However both nominal and real side matters for the deflator

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In-sample fit at different frequencies: deflator

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In-sample fit at different frequencies: nominal GDP

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In-sample fit at different frequencies: real GDP

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Comments

• Second factor small but relevant for the deflator.

• Fit is good for nominal and real GDP up to a year cycle – very poor for deflator – as known in the literature difficult to

capture the low frequency component of inflation (see Lenza et al for the euro area)

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What are the news that matter?

• We extract the news as described and we present average result for first, second and third month

• Present results for nominal GDP, the deflator and real GDP

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Average impact nominal GDP r=2

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30

IPTOT PMI DPRI URATE PYRL_TOT PCETOT HSTARTS HSOLD ORD_MFGD PPI_FG CPITOT EXPORT IMP_CUST PHBOS_GA SALES_RETAIL_NOM CONF_CFB S&P GS10 GS3M MCOILWTICO TWEXMMTH M1SL M2SL GDP

m3 m2 m1

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Average impact deflator r=2

-0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12

IPTOT PMI DPRI URATE PYRL_TOT PCETOT HSTARTS HSOLD ORD_MFGD PPI_FG CPITOT EXPORT IMP_CUST PHBOS_GA ES_RETAIL_NOM CONF_CFB S&P GS10 GS3M MCOILWTICO TWEXMMTH M1SL M2SL Real GDP Deflator

m3 m2 m1

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Average impact real GDP r=2

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25

IPTOT PMI DPRI URATE PYRL_TOT PCETOT HSTARTS HSOLD ORD_MFGD PPI_FG CPITOT EXPORT IMP_CUST PHBOS_GA SALES_RETAIL_NOM CONF_CFB S&P GS10 GS3M MCOILWTICO TWEXMMTH M1SL M2SL Real GDP Deflator

m3 m2 m1

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What is it that matters?

Summary

• Nominal GDP:

real variables, interest rates and exchange rate – no monetary aggregates, no prices

• Deflator:

Very small impact of the news; prices, interest rates and exchange rate – CPI, PPI and oil inflation - no real variables, no monetary aggregates / this for r=2 otherwise constant model is best

• Real GDP:

same as nominal except for inflation news which affect it negatively

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Remarks

Some of these results to be expected

Little effect of real news on deflator arises from poor relative predictability of the deflator

By the same reasoning, poor relative now-casting predictability of inflation and high relative predictability of real GDP imply that real news move the signal of nominal GDP more than nominal news

Some are interesting

Monetary aggregates news have no role for neither nominal GDP nor the deflator exactly as we had found for real GDP. The yield curve takes their role No Phillips curve relation seems to emerge from the effect of inflation news to real GDP

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Considerations on Phillips curve

• Very little Phillips curve relation in the data and in the news analysis → real economy objective does not affect inflation even in the short-run?

• Literature has suggested that Phillips curve relations emerge conditional to large shocks (Giannone et al 2011) or around recessions (Stock and Watson 2012)

Use the model to study effect of different types of shocks: oil prices and unemployment

Report here only persistent effect of an increase in unemployment

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Counterfactual analysis

• Use model (balanced panel version) to compute conditional forecasts given an assumed path of a given variable

Two exercises:

1. Condition on future path of oil price – 1 month jump and 6 month increase

2. Condition on future impact of unemployment – 1 month and 6 month increase.

Report here only last exercise as an illustration:

Cumulated change (over 6 month) on level: 1%, 5% and 10%

(10% corresponds approx to 1 point increase)

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Persistent shock on unemployment effect on nominal GDP level

1300000.00 1350000.00 1400000.00 1450000.00 1500000.00 1550000.00

1 2 3 4 5

nominal gdp level base nominal gdp level cond 1%

nominal gdp level cond 5%

nominal gdp level cond 10%

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Persistent shock on unemployment effect on the level of the deflator

111.50 112.00 112.50 113.00 113.50 114.00 114.50 115.00

1 2 3 4 5

deflator level base deflator level cond 1%

deflator level cond 5%

deflator level cond 10%

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Implications in terms of CPI inflation

monthly growth rate

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60

1 2 3 4 5 6 7 8 9 10 11 12 13 CPI growth rate base

CPI growth rate cond 1%

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Comments

• Phillips curve relation emerges in response to persistent shock in unemployment

• Result obtained in Giannone et al 2011 via a different model and euro area data is confirmed

– suggest tradeoff policy relevant during recessions – case for active output stabilization in the short run

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Tentative Conclusions

On now-casting nominal GDP

• Now-casting is an appropriate framework for nominal GDP and now-casting model outperforms naïve constant growth when is able to exploit timely information

• Daily financial variables add to now-casting ability f nominal GDP (unlike what found for real GDP by Banbura et al) since they are valuable for capturing the nominal side.

• Monthly monetary aggregates do not have any role

• All the other results found in the real GDP now-casting literature are confirmed On Phillips curve tradeoffs

• No strong tradeoff between nominal and real variables as shown by the model fit and forecasting results for the deflator as opposed to the real GDP as well as the analysis of the news

• Tradeoff emerges for large shocks

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Work in progress

• Fully real time

• Uncertainty

Revision errors and statistical errors important to understand economic significance of the news analyis