RES Easter School 2012 The Economics and Econometrics of ... · Performance of statistical nowcasting models deteriorated with onset of recession. Current nowcasting methods break

RES Easter School 2012The Economics and Econometrics of

Forecasting

Jennifer L. Castle

Birmingham University

April 2012

Lecture 4: Nowcasting

Castle (Oxford) Forecasting April 2012 1 / 50

Why nowcast?

24 July 2009 ONS official GDP growth data:0.8% decline in GDP for 09Q2

Much worse than anticipated – severe forecast failure.

Financial Times (25 July 2009):

‘the ONS said that its estimates were even less reliable thannormal because the economy’s unpredictability meant itsmodels had “broken down” .’

National Institute (Mitchell, 2009):

Performance of statistical nowcasting models deterioratedwith onset of recession.

Current nowcasting methods break down when structuralbreaks/turning points.Consider reasons for nowcasting and implications for how to producerobust nowcasts.


Problems

Missing data – not all disaggregated contemporaneous dataavailable to construct aggregate.Statistical agencies views differ on optimal trade-off betweentimeliness and accuracy.

Measurement error – ‘flash’ estimates subject to potentiallysubstantial revisions.Not reliable and accurate guide to current conditions.

Changing database – different components unavailable indifferent periods and missing on non-systematic basis.

Breaks – Nowcasts act as ‘early warning signals’ when they differradically from the measured series.Rapid action to update/account for breaks, or warn ofmeasurement error.


GDP outturns: initial and latest estimates

Source: Bank of England Quarterly Bulletin, Autumn 2005, p.338


Timing/sources of information

Why nowcasting differs from forecasting:Contemporaneous Observations

Sources of contemporaneous data:

Disaggregates available closer to nowcast origin (e.g. retail sales);

Covariates available at higher frequencies/more timely (e.g. newpassenger car registrations, construction output, industrial neworders, road traffic, air passenger numbers, energy consumption);

Surveys (business/consumer plans and expectations);

Google Trends data (Choi and Varian, 2009);

Prediction markets (Croxson and Reade, 2008).


Variable of interest

Objective: nowcast of aggregate yT .where:

yt =

N∑i=1

wiyi,t, (1)

yi,t disaggregates;

wi weights.

Data released with varying time delays: at T some componentsobserved, some unavailable until T + δ.At T :

J known components;

N − J unknown components.

J not fixed, sets not uniquely defined.


Aggregates versus disaggregates

Forecast aggregate using aggregate information:

yT |T−δ∗ = f(Y0T−1,Z

0T−δ∗

),

Forecast disaggregates unknown at T :

yi,T |T−δ∗ = f(y0i,T−δ,Z

0T−δ∗

), i = J + 1, . . . , N,

and aggregate with the known data:

yT |T =

J∑i=1

wiyi,T +

N∑i=J+1

wiyi,T |T−δ∗



Forecast aggregate, conditioning on aggregate and disaggregateinformation.

Balanced panel:

yT |T−δ = f(Y0T−δ,y

0i,T−δ,Z

0T−δ

), ∀i ∈ N,

Unbalanced panel:

yT |T = f(Y0T−1; y0

1,T , . . . ,y0J,T ,y

0J+1,T−δ, . . . ,y

0N,T−δ; Z

0T−δ∗

),

– results in ‘ragged edge’ problem.



Can be shown that when predicting the aggregate, nothing is lost bydirectly predicting from disaggregate information, without predictingdisaggregates.

yT = w1y1,T + (1− w1) y2,T (2)

Assume J = 0. DGP for the disaggregates is:

yi,t = γ′ixt−1 + ηi,t (3)

where xt−1 = (y1,t−1, . . . , yN,t−1; zt−1)′ so:

ET−1 [yi,T | xT−1] = γ′ixT−1 for i = 1, 2 (4)



Aggregating delivers:

ET−1 [yT | xT−1] =

(2∑i=1

wiγ′i

)xT−1 = ψ′xT−1 (5)

Predicting yT directly from xT−1 yields:

ET−1 [yT | xT−1] = π′xT−1 (6)

Since the left-hand sides of (5) and (6) are equal:

π′xT−1 = ψ′xT−1 (7)

so nothing is lost by predicting yT directly, instead of aggregatingcomponent predictions once the same information set xT−1 is used forboth.



Hendry and Hubrich (2009) generalize to non-constant weights,changing parameters.Does not mitigate breaks.

BUT contemporaneous information is available at T –unbalanced panel

helps distinguish between breaks and measurement errorespecially when breaks occur simultaneously across thedisaggregates (or a subset)

Need a combination of forecasting unknown disaggregates, augmentedwith structural break detection

Current methods – dynamic factor models/MIDAS.


Selecting nowcasting models

Nowcasts required of N − J unknown disaggregates.GUM:

yi,t = β′xt−1 + α′ft−1 +

T−1∑j=1

ςj1tj=t + νt, i = J + 1, . . . , N (8)

xt−1: all available information (lagged disaggregates, survey data,leading indicators)

ft−1: set of q latent common factors (ft = Λ′yd,t);

1tj=t: set of T − 1 impulse-indicators.

Selection using Autometrics (Doornik, 2009a, Doornik, 2009b) formore variables than observations (and ∴ perfect collinearity)Forecasts:

yi,T |T−1 = γ′x∗T−1 + θ′f∗T−1 + ς ′dT−1 (9)


Nowcasting strategy

If break occurs in J observed disaggregate series,use information to improve forecast yi,T |T−1, i = J + 1, . . . , N .

1 Detect whether break occurred at T

yi,T = β′xt−1 + ρ′ft−1 +

T∑j=1

ςj1tj=t + vt, i = 1, . . . , J ; t = 1, . . . , T.

(10)Test whether ςT significant

Conservative significance level: 0.5% ≤ α ≤ 0.1%(Conventional tests low or no power at end-point)Forecast error:

ei,T = yi,T − yi,T |T−1, i = 1, . . . , J,


Nowcasting strategy

2 If evidence of break at T , adjust N − J unknown disaggregates(role of judgment).

Is break thought to be:

permanent or transitory?– measurement error or location shift;

idiosyncratic or common?– correlated with unknown disaggregates at T .

Forecasting rule:

yi,T |T = (1− Ik) yi,T |T−1 + Iky∗i,T |T , i = J + 1, . . . , N, (11)

where Ik = 1 when kαJ ≥ p for p =∑J

i=1 1T and y∗i,T |T interceptcorrected forecast


Nowcasting strategy

IC forecast:

y∗i,T |T = yi,T |T−1 +1

p

p∑j=1

ςp,T (12)

Or weighted average based on correlations between the disaggregates.

IC analogous to setting forecast back on track but across disaggregateseries as opposed to through time.

Error variance equal to uncorrected error variance.

Contrast to standard intercept correction that sets forecast back ontrack and doubles the error variance.

Alternative: differencing – doesn’t impose break magnitude butdoubles error variance.


Intercept correction

Let:yi,T = ψiyi,T−1 + vi,T (13)

when Ik = 0: yi,T |T−1 = ψiyi,T−1;

when Ik = 1: y∗i,T |T = yi,T |T−1 + µp,T ;

where µp,T =∑p

j=1 ςp,T

Forecast error:

v∗i,T = yi,T − y∗i,T |T= vi,T − µp,T (14)

vi,T = forecast error from uncorrected forecast

V[v∗i,T

]≈ V [vi,T ] as µp,T nearly a fixed constant for each i when J is

large.


Refinements

Split disaggregates into blocks corresponding to groups of variables(e.g. industrial production, prices, financial variables, interest rates,labour variables, housing market variables, etc.)

Requirements:

close linkages: breaks likely to spread within block;

different release timings;

common trends or cycles for subsets of disaggregates

Apply intercept correction to each block separately.

Strategy is computationally feasible, allows for breaks in-sample and atnowcast origin, employs full information set but relies on someproportion of known disaggregates containing signal about unknowndisaggregates


Measurement errors versus breaks

Signal extraction problem at T .

If yi,T |T−δ differs significantly from yi,T :

outlier due to measurement error?

more permanent location shift?

combination of both?

observationally equivalent with one data point

Measurement errors ⇒ autocorrelated residuals but only fewobservations to detect.

Residual analysis needed at end of sample


Residual analysis under measurement error

No measurement error: t = 1, . . . , T e

Data measured with error: s = T e, . . . , TDGP for disaggregates:

yi,t = ψiyi,t−1 + vi,t for t = 1, . . . , T ; i = 1, . . . , J (15)

vi,t ∼ IN[0, σ2vi

]; |ψi| < 1.

For 1, . . . , T e: model coincides with (15)For s = T e, . . . , T we observe yi,s = y∗i,s + ηi,s.

Assume ηi,s ∼ IN[0, σ2ηi

]; E[y∗i,sηi,t

]= 0 for all s, t.

(Systematic measurement error: e.g. E [ηi,s] = µηi 6= 0)


Residual analysis under measurement error

For T e, . . . , T :

yi,s = ψiyi,s−1 + (vi,s + ηi,s − ψiηi,s−1) = ψiyi,s−1 + ei,s

When ψi = 1, residuals over s:

E [ei,s] ' 0

V [ei,s] ' σ2vi +(1 + ψ2

i

)σ2ηi

E [ei,sei,s−1] = ρi '−ψiσ2ηi

σ2vi +(1 + ψ2

i

)σ2ηi

ρi denotes late-onset error autocorrelation (assuming σ2ηi constant ass→ T )If last few residuals exhibit increase in variance andautocorrelation, more weight placed on measurement errorhypothesis


Residual analysis under location shifts

Alternatively, indicator-impulse saturation used to detect late onsetshiftsSome cases can be justified ex ante (e.g. VAT changes).

Problem:

Measurement error and location shifts at T require differentforecasting models.

Measurement error ⇒ EWMA schemes optimal

Location shift ⇒ intercept correction and differencingBut exacerbates impact of measurement errors

Location shift requires + IC for nowcast periodMeasurement error requires − one-off IC to offset error

Resolving conflict between opposite responses is central toaccurate nowcasting



Can we distinguish between location shifts and measurementerrors?

measurement error at T does not ‘carry forward’, although effectswill in dynamic process;

data at T usually revised: revisions to errors at T + 1 informativeabout source of error from T to T + 1;

next nowcast error (from T + 1 to T + 2) large if source is locationshift, ∴ repeated mis-forecasting indicative of location shift;

extraneous contemporaneous data help to discriminate:discrepancy from existing models persist or disappear;

variance of measurement errors usually changes as the forecastorigin approaches.



Simplest model:yt = y∗ + εt (16)

where εt ∼ IN[0, σ2ε

].

Three cases:

y∗ is constant – measurement error;

εt = 0 ∀t but y∗ alters – location shift;

both y∗ shifts and εt 6= 0

Nowcast at time T from estimation sample 1, . . . , T − δ for small δSequence of nowcasts as if in real time, at T + 1, T + 2 etc.


1. Measurement errors only

ET+1 [yT+1 | IT+1] = ET+1 [(y∗ + εT+1)] = y∗ (17)

Despite measurement error, 1-step minimum MSE forecasting device:

yT+1|T = y(T ) =1

T

T∑t=1

yt.

Thus:

ET+1

[yT+1|T | IT+1

]= y∗

VT+1

[yT+1|T

]= σ2ε

(1 +

1

T

)Full-sample estimate y(T ) of y∗ is unbiased predictor, with

smallest variance of any sample size choice, which is just 1T

larger than nowcast from the known location y∗.Castle (Oxford) Forecasting April 2012 24 / 50

2. Location shifts only

Best predictor of yT+1 is yT+1|T = yT .

When no location shifts, yT+1|T = yT = y∗ is exact;When shift at T (∇(T )y

∗) a one-period mistake is made:

yT+1 − yT+1|T = yT+1 − yT = ∇(T )y∗

nowcast at T + 2 unbiased if process remains constant:

yT+2 − yT+2|T+1 = yT+2 − yT+1 = 0.

Best 1-step predictor is lagged value, uses smallest possiblesample size – final observation – ignoring all earlier observations.


3. Cross solutions

1 Use yT+1|T = yT but only measurement error:

ET+1

[yT+1|T

]= ET+1 [yT ] = y∗

VT+1

[yT+1|T

]= 2σ2ε

Predictor unbiased but variance increases by (T − 1) /T .

2 Use yT+1|T when εt = 0 ∀t but location shift (∇(τ)y∗):

ET+1

[yT+1 − yT+1|T

]=τ∗∇(τ∗)y

∗

T

y(T ) is biased predictor – impact of in-sample breakunder-estimated when not modelled. Either model break oruse rolling sample spanning post-break period.


Measurement errors and location shifts

DGP: yT+1 = y∗a + εT+1 and yT+2 = y∗a + εT+2 where nounmodelled in-sample shifts, but:

y∗a = y∗ +∇(T+1)y∗

and after data revision at T + 2:

yT+1 = y∗a + εT+1

y(T ) minimum MSE estimator of y∗, with variance σ2εT−1 on

average;

yT+1 − yT+1 estimates revision error;

yT+1 − yT and yT+1 − yT estimate ∇(T+1)y∗ imprecisely;

VT+1 [εT+1] and VT+2 [εT+2] larger than σ2ε


Measurement errors and location shifts

(yT+1 + yT+2) /2 estimates y∗a with variance of 14

(σ2ε + VT+2 [εT+2]

),

combining with y estimated up to T , and the estimate of ∇(T+1)y∗

gained from yT+1 − yT yields:

y∗a =1

2

(y(T ) + (yT+1 − yT ) +

1

2(yT+1 + yT+2)

)(18)

with:

VT+2 [y∗a] =1

4

(σ2ε

(2 +

1

T

)+

1

4

(σ2ε + VT+2 [εT+2]

))

Weighted estimator (18) has lower variance under certain conditions,see Castle and Hendry (2010).Signal extraction suggests using both latest yT+1 and yT , whereweights vary with measurement error variance.


Empirical application

Empirical application from Castle, Fawcett, and Hendry (2009).Compute nowcasts of Euro-area quarterly GDP growthQuarterly observations indexed by t;τ denotes the monthly index, with t = 3τ .Three releases:Flash estimate ≈43 days after reference quarter;Two revised releases in following consecutive months.Denote releases as vintages:

yv1t released at τ + 2



Survey data – 0 publication lagOther data (e.g. industrial production, financial variables) releasedwith intermediate lag of between 1 and 3 months.


Data

Label Description Lag releaseYt real GDP, million, constant prices 2,3,4IPt industrial production index (NSA) 3IPCt industrial production index for construction (NSA) 3SCIt service sector confidence indicator (survey) 0RSt retail trade index, except motor vehicles, constant prices 2CARSt new passenger car registrations, total 2MCIt industry confidence indicator (survey) 0ESIt economic sentiment indicator (base=100) 0CCIt consumer confidence indicator (survey) 0RCIt retail trade confidence indicator (survey) 0EERt real effective exchange rate, CPI deflated, core group 1EUROXt Dow Jones Euro Stoxx Broad stock exchange index 1OECDLIt OECD composite leading indicator, Euro area, trend restored 0EUROCOINt Eurocoin indicator 0URt unemployment rate, total 2M1t Index of notional stock, money M1 2SPREADt =(3MTB-10YB)/100 1


Nowcasts

Calculate nowcasts for period 2003Q2–2008Q1, (20 observations).

For each quarter, compute 3 nowcasts of quarterly GDP growth;

H1 for month at end of reference quarter;

H2 for month after end of reference quarter;

H3 for second month after end of reference quarter.


Timing of nowcasts and data releases

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

H1 H2 H3Q1yv1t yv2

t yv3t

H1 H2 H3Q2yv1t yv2

t yv3t

H1 H2 H3Q3yv3t yv1

t yv2t

H2 H3 H1Q4yv1t yv2

t yv3t


Empirical Specifications

[A] Benchmark quarterly univariate models:

H1 : ∆yt = f(∆yv1

t−1, . . . ,∆yv1t−4; ∆yv2

t−1, . . . ,∆yv2t−4; ∆yv3

t−2, . . . ,∆yv3t−4

)H2 : ∆yt = f

(∆yv1

t−1, . . . ,∆yv1t−4; ∆yv2

t−1, . . . ,∆yv2t−4; ∆yv3

t−1, . . . ,∆yv3t−4

)H3 : ∆yt = f

(∆yv1

t , . . . ,∆yv1t−4; ∆yv2

t−1, . . . ,∆yv2t−4; ∆yv3

t−1, . . . ,∆yv3t−4

)[B] Benchmark quarterly models with covariates:

Conditioning variables xt = (∆ipt , ∆ipct, ∆SCIt, ∆rst, ∆CARSt,∆MCIt, ∆ESIt, ∆CCIt, ∆RCIt, ∆eert, ∆euroxt, ∆oecdlit,∆EUROCOINt, ∆urt, ∆m1t, SPREADt), and (xr1

t , xr2t , xr3

t ).

[C] Disaggregate nowcasts

1-step ahead forecasts from recursively-selected models for each horizon.


Methodology

1 Obtain forecasts for monthly variables released with lagAutomatic Gets with IIS at α = 1%Direct forecasts, depending on publication lagModels selected and estimated recursivelyRecord retained final observation impulsesAlso test for final observation impulses in contemporaneous data(surveys)

2 Compute nowcasts of GDP growthCreate quarterly time series for monthly variables:

xr1t = xτ−2, xτ−5, xτ−8, . . .

xr2t = xτ−1, xτ−4, xτ−7, . . .

xr3t = xτ , xτ−3, xτ−6, . . .


Methodology

Form GUM with all three quarterly series– avoids aggregation issue and allows all monthly data to be includedin quarterly model

GUM includes all in-sample data for the conditioning variables andlatest available vintage of GDP growth.

Models selected recursively with IIS for each nowcast origin and eachnowcast horizon

Nowcasts computed by plugging in forecasts for variables selected butunknown at nowcast horizon.

Nowcasts compared to actuals given by final available vintage of data,(December 2008).

1 Adjustments made if significant impulses at T .


Stage 1: GUM specification

Forecast GUM Variables ESE RMSE

∆ipτ ∆ipτ−3,...,τ−12; ∆ipcτ−3,...,τ−12; SCIτ,...,τ−12;C;S;I 1.02% 1.32%

∆ipcτ ∆ipτ−3,...,τ−12; ∆ipcτ−3,...,τ−12; SCIτ,...,τ−12;C;S;I 2.04% 3.83%

∆rsτ ∆rsτ−2,...,τ−12; ∆CARSτ−2,...,τ−12; ∆ipτ−3,...,τ−12;∆ipcτ−3,...,τ−12;C;I 0.59% 0.63%

∆CARSτ ∆rsτ−2,...,τ−12; ∆CARSτ−2,...,τ−12; ∆ipτ−3,...,τ−12;∆ipcτ−3,...,τ−12;C;I 2.81% 3.41%

∆eerτ ∆eerτ−1,...,τ−12; ∆euroxτ−1,...,τ−12; SPREADτ−1,...,τ−12;C;I 1.30% 1.08%

∆euroxτ ∆eerτ−1,...,τ−12; ∆euroxτ−1,...,τ−12; SPREADτ−1,...,τ−12;C;I 2.22% 3.33%SPREADτ ∆eerτ−1,...,τ−12; ∆euroxτ−1,...,τ−12; SPREADτ−1,...,τ−12;C;I 0.14% 0.16%

∆urτ ∆urτ−2,...,τ−12; ∆rsτ−2,...,τ−12; ∆ipτ−3,...,τ−12;∆ipcτ−3,...,τ−12; ESIτ,...,τ−12; C;I 0.35% 0.56%

∆m1τ ∆m1τ−2,...,τ−12; SPREADτ−1,...,τ−12; ∆ipτ−3,...,τ−12;∆ipcτ−3,...,τ−12; ∆rsτ−2,...,τ−12; C;I 0.47% 0.89%


Stage 1: GUM specification

GUM includes: SCIrjt−i, MCI

rjt−i, ESI

rjt−i, CCI

rjt−i, RCI

rjt−i,

SPREADrjt−i, ∆ip

rjt−i, ∆ipc

rjt−i, ∆rs

rjt−i, ∆CARS

rjt−i, ∆eer

rjt−i,

∆euroxrjt−i, ∆oecdli

rjt−i, ∆ur

rjt−i, ∆EUROCOIN

rjt−i, ∆m1

rjt−i, for

i = 0, . . . , 2 and j = 1, 2, 3, an intercept and impulse indicatordummies.Also LDVs:

H1 : ∆yv1t−1,∆y

v1t−2; ∆yv2

t−1,∆yv2t−2; ∆yv3

t−2H2 : ∆yv1

t−1,∆yv1t−2; ∆yv2

t−1,∆yv2t−2; ∆yv3

t−1,∆yv3t−2

H3 : ∆yv1t ,∆y

v1t−1,∆y

v1t−2; ∆yv2

t−1,∆yv2t−2; ∆yv3

t−1,∆yv3t−2

150–152 variables + T indicator dummies.Models selected recursively at α = 1%


Stage 2: computing nowcasts

Nowcasts computed as:

∆yHkt = β′xt + γ′xt + δ′ιt (19)

xt – retained variables known at t for horizon k;

xt – forecasts for retained variables unknown at t for horizon k;

ιt – retained impulse indicators.

[C1] Ex post nowcasts – all disaggregates known

[C2] Disaggregate nowcasts (19)

[C3] Double differenced device: xτ = xτ−l


Results

A B C1 C2 C3

H1 0.2951 0.9823 0.4340 0.7994 0.7531H2 0.3056 1.2245 0.4808 0.6372 0.6222H3 0.1486 1.0372 0.8320 0.8320 0.8320

Table: Actual RMSE for [A] and ratio of RMSE to the univariate benchmarkmodel [A] for [B] and [C].


MAE and RMSE for nowcasting models

A B C1 C2 C3

0.025

0.050

0.075

Mean Absolute Error (%)

H1

H2 H3

A B C1 C2 C3

A B C1 C2 C3

0.1

0.2

0.3

RMSE (%)

H1

H2 H3

A B C1 C2 C3


Nowcast errors

A B C1 C2 C3

2003 2004 2005 2006 2007 2008

-0.005

0.000

0.005

0.010 Nowcast errors for H1A B C1 C2 C3

A B C1 C2 C3

2003 2004 2005 2006 2007 2008

-0.005

0.000

0.005

0.010 Nowcast errors for H2A B C1 C2 C3

A B C

2003 2004 2005 2006 2007 2008

-0.005

0.000

0.005

0.010 Nowcast errors for H3

A B C


Results

Higher-frequency monthly data yields smaller RMSE, particularly overshort horizons

Main benefits come from:

rapid incorporation of information available at the monthlyfrequency;

accounting for outliers using IIS;

use of recursive selection and estimation to update models rapidly.

Little benefit to well-specified econometric models to forecast themonthly indicators as opposed to robust forecasting device such asDDD.


Revisions

∆yv1t −∆yv2

t ∆yv1t −∆yv3

t ∆yv1t −∆y

vft

ME (%) 0.0045 -0.0059 -0.0674SD (%) 0.0214 0.0391 0.1583

Table: Magnitude of revisions over nowcast horizon

Substantial measurement errors in the first few vintages of data

Little information content in the revisions initially.

In nowcasting context, detecting measurement errors close tonowcast origin is difficult.


Revisions to GDP growth

∆ytvf

(∆ytv1−∆yt

v2) (∆yt

v1−∆ytv3)

(∆ytv1−∆yt

vf)

2003 2004 2005 2006 2007 2008

-0.0025

0.0000

0.0025

0.0050

0.0075

0.0100

Revisions to GDP growth

∆ytvf

(∆ytv1−∆yt

v2) (∆yt

v1−∆ytv3)

(∆ytv1−∆yt

vf)


Breaks in monthly indicators

Significant impulses at α = 1%:

∆ipcτ = 2005(12); 2006(1)

∆CARSτ = 2005(5); 2005(6)

MCIτ = 2003(12)

ESIτ = 2003(12)

∆urτ = 2006(4); 2007(7); 2007(9); 2008(1)

∆m1τ = 2005(1); 2005(6); 2006(12)

SPREADτ = 2007(8)

No impulses for ∆ipτ , SCIτ , ∆rsτ , CCIτ , RCIτ , ∆eerτ , ∆euroxτ ,∆oecdliτ and ∆EUROCOINτ .


Monthly impulses at α = 1%

∆ytvf

Dummies

2002 2003 2004 2005 2006 2007 2008

0.002

0.004

0.006

0.008

0.010

0.012

∆ytvf

Dummies


Robust nowcasts

Recall rule:∆yt = (1− Ik) ∆yt + Ik∆y

∗t (20)

∆yt nowcasts obtained from [C2];

∆y∗t DDD given by:

∆y∗t =

∆yv2

t−1 for H1

∆yv3t−1 for H2

∆yv1t for H3

C2 C2-adjust

H1 0.2359 0.2327H2 0.1948 0.2146H3 0.1236 0.1603


Conclusion

Nowcasting differs significantly from forecasting: problems ofmissing data, measurement errors, changing database, andbreaks.

Location shifts induce nowcast failure, but interact with measurementerrors to make discrimination difficult in the available time horizon.

Methodology includes Gets recursive selection for many covariates,higher frequency data, in-sample and end-point break detection, robustdevices.

Application suggests substantial gains to flash estimates.

Apparent ‘break down’ of the ONS’s current models, reported by theFinancial Times, shows the difficulty of nowcasting in times ofeconomic uncertainty and structural change.


References I

Castle, J. L., N. W. P. Fawcett, and D. F. Hendry (2009).Nowcasting is not just contemporaneous forecasting.National Institute Economic Review 210, 71–89.

Castle, J. L. and D. F. Hendry (2010).Nowcasting from disaggregates in the face of location shifts.Journal of Forecasting 29, 200–214.

Choi, H. and H. Varian (2009).Predicting the present with Google Trends.Unpublished paper, Economics Research Group, Google.

Croxson, K. and J. J. Reade (2008).Information and efficiency: Goal arrival in soccer betting.Mimeo, Economics Department, Oxford University.

Doornik, J. A. (2009a).Autometrics.In J. L. Castle and N. Shephard (Eds.), The Methodology and Practice of Econometrics, pp. 88–121. Oxford: Oxford

University Press.

Doornik, J. A. (2009b).Econometric model selection with more variables than observations.Working paper, Economics Department, University of Oxford.

Hendry, D. F. and K. Hubrich (2009).Combining disaggregate forecasts versus disaggregate information to forecast an aggregate.Journal of Business and Economic Statistics, DOI:10.1198/jbes.2009.07112.


References II

Hendry, D. F., S. Johansen, and C. Santos (2008).Automatic selection of indicators in a fully saturated regression.Computational Statistics 33, 317–335.Erratum, 337–339.


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