Nominal GDP now-casting

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

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

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?

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)

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

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

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

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

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

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

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?

The Real-Time Data-Flow: Yesterday in Europe

A model of Now-Casting y Q t : GDP at time t. Ω v : vintage of data (quarterly, monthly, possibly daily) available at time v (date of a particular data release)

A model of Now-Casting y Q t : 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 yt Q : orthogonal projection of yt Q information: ] E [y t Q Ω v, on the available

A model of Now-Casting y Q t : 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 yt Q : orthogonal projection of yt Q information: ] E [y t Q Ω v, on the available 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

Further features Projections need to be updated regularly ] ] E [y t Q Ω v, E [y t Q Ω v+1,... 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.

News and nowcast revisions New release the information set expands (new releases): Ω v Ω v+1 [we are abstracting from data revisions]

News and nowcast revisions New release the information set expands (new releases): Ω v Ω v+1 [we are abstracting from data revisions] Decompose new forecast in two orthogonal components: ] ] ] E [y t Q Ω v+1 = E [yt Q Ω v + E [yt Q I v+1, } {{ } } {{ } } {{ } new forecast old forecast revision I v+1 information in Ω v+1 orthogonal to Ω v

News and nowcast revisions New release the information set expands (new releases): Ω v Ω v+1 [we are abstracting from data revisions] Decompose new forecast in two orthogonal components: ] ] ] E [y t Q Ω v+1 = E [yt Q Ω v + E [yt Q I v+1, } {{ } } {{ } } {{ } new forecast old forecast revision I v+1 information in Ω v+1 orthogonal to Ω v 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 yt Q Ω v+1 E yt Q Ω v } {{ } forecast revision = j J v+1 b j,t,v+1 For detailed derivation see Banubra and Modugno, 2008. ( E [ xj,tj,v+1 x Ω j,tj,v+1 v]). } {{ } news

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

Computing projections. What kind of model? The dynamic factor model x t = µ + Λf t + ε t, f t : (unobserved) common factors; ε t : idiosyncratic components Λ factor loadings Factors are modelled as a VAR process: f t = A 1 f t 1 + + A p f 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

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 b j,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 frequencies Example: Let Yt Q denote the vector of (log of) the quarterly flow series. We assume that Y Q t is the sum of daily contributions X t Y Q t = t s=t k+1 X s, t = k, 2k,.... Hence we will have that the stationary series yt Q written as: y Q t = k t s=t k+1 t + 1 s k x s + t k s=t 2 (k 1) = Y Q t s t + 2 k 1 k x s Yt k Q can be where x s = X s X s 1 can be thought of as an unobserved daily growth rate (or difference). See also Modugno 2011: Nowcasting Inflation, t = k, 2k,.

Data Table

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

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

Now-cast

Evolution of RMSFE over the quarter daily r=1, monthly with r=1 and r=2 0.75 0.70 0.65 0.60 0.55 0.50 0.45 r1p1 r2p1 daily RW 0.40 0.35 0.30 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

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

Monthly model: Now-cast nominal GDP 3 2.5 2 1.5 1 0.5 0 r1p1 r2p1 actual -0.5-1 -1.5-2 -2.5

Monthly model: Now-cast deflator 1.4 1.2 1 0.8 0.6 0.4 r1p1 r2p1 actual 0.2 0-0.2-0.4

Monthly model: Now-cast real GDP 2.5 2 1.5 1 0.5 0-0.5 r1p1 r2p1 actual -1-1.5-2 -2.5-3

Nominal GDP 0.75 0.70 0.65 0.60 0.55 0.50 0.45 r1p1 r2p1 RW 0.40 0.35 0.30 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

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

Deflator 0.29 0.27 0.25 0.23 0.21 0.19 r1p1 r2p1 RW 0.17 0.15 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

Observations Not much seems to matter except past real GDP and deflator Te second factor helps

Real GDP 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 r1p1 r2p1 RW 0.4 0.35 0.3 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

Observations Monotonic pattern confirmed (see Giannone et al 2008 and Banbura et al 2012) Important role of the surveys

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

In-sample fit at different frequencies: deflator

In-sample fit at different frequencies: nominal GDP

In-sample fit at different frequencies: real GDP

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)

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

Average impact nominal GDP r=2 0.30 0.25 0.20 0.15 0.10 0.05 0.00-0.05 m3 m2 m1-0.10-0.15-0.20 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

Average impact deflator r=2 0.12 0.10 0.08 0.06 0.04 0.02 m3 m2 m1 0.00-0.02-0.04 IPTOT PMI DPRI URATE PYRL_TOT PCETOT HSTARTS HSOLD ORD_MFGD PPI_FG CPITOT EXPORT IMP_CUST PHBOS_GA S_RETAIL_NOM CONF_CFB S&P GS10 GS3M MCOILWTICO TWEXMMTH M1SL M2SL Real GDP Deflator

Average impact real GDP r=2 0.25 0.20 0.15 0.10 0.05 0.00-0.05 m3 m2 m1-0.10-0.15-0.20 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

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

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

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

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)

Persistent shock on unemployment effect on nominal GDP level 1550000.00 1500000.00 1450000.00 1400000.00 nominal gdp level base nominal gdp level cond 1% nominal gdp level cond 5% nominal gdp level cond 10% 1350000.00 1300000.00 1 2 3 4 5

Persistent shock on unemployment effect on the level of the deflator 115.00 114.50 114.00 113.50 113.00 deflator level base deflator level cond 1% deflator level cond 5% deflator level cond 10% 112.50 112.00 111.50 1 2 3 4 5

Implications in terms of CPI inflation monthly growth rate 0.60 0.40 0.20 0.00-0.20-0.40 1 2 3 4 5 6 7 8 9 10 11 12 13 CPI growth rate base CPI growth rate cond 1% CPI growth rate cond 5% CPI growth rate cond 10% -0.60-0.80-1.00

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

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

Work in progress Fully real time Uncertainty Revision errors and statistical errors important to understand economic significance of the news analyis