Short-term forecasting of the GDP growth rate using the BTS in industry and in services: An out-of-sample analysis

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Short-term forecasting of the GDP growth rate
using the BTS in industry and in services An
out-of-sample analysis
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The two issues addressed in the paper

• 1) BTS are widely used for the short-term
  forecasting of economic activity.
• However, to our knowledge, there has been no
  recent attempt to establish the significance of
  their contribution to the quality of forecasts
  (using modern econometric techniques).

• 2) The service survey contains a specific piece
  of information on GDP growth with respect to the
  industry survey (Bouton and Erkel-Rousse, 2003).
• However, it remains to be shown that this
  specific piece of information permits one to
  significantly improve the quality of short-term
  GDP forecasts with respect to models involving
  variables from the industry survey exclusively.
  

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The study consists in

• - estimating several VAR models and univariate
  calibration models of the GDP quarterly growth
  rate encompassing miscellaneous variables
  derived from the industry and service surveys
  as well as GDP lags
• - estimating competing models with no service
  variable or, even, no BTS variable (simple
  AR models of GDP) on several time periods
  (real-time analysis - RTA)
• - simulating each model up to a four-quarter
  forecast horizon, and deriving series of
  forecasting errors (RTA)
• - comparing the predictive accuracy of the
  different models using Clark-West or Harvey,
  Leybourne and Newbold tests (depending on the
  models)

• Data used quarterly survey data and, to a lesser
  extent, monthly survey data.

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Structure of the Talk

• Data
• Methodology
• Results
• Conclusion

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Data (1) General characteristic features

• - 1962 creation of the INSEE industry survey
• - 1988 creation of the INSEE service survey (on
  a quarterly basis)
• - June 2000 creation of the monthly service
  survey, adding of a few questions
• - Since the 1990s progressive extension of the
  sector coverage of the service survey (in the
  data used business services (2/3), household
  services (gt 20), real estate activities
  (gt10))
• - January 2004 several changes in the wording of
  the questionnaires of the two surveys and
  adding of a few questions in the service survey
  for European harmonisation purpose
• - January 2004 the two surveys become compulsory
  

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Data (2) The data used in the study

• Macro variable to be forecasted
• ? the quarterly GDP growth rate from the
  French Quarterly Accounts (hereafter
  GDP)
• Industry survey data
• ? the 6 main monthly balances and 2
  quarterly ones (past and expected demand)
• ? 2 static common factors in industry a
  monthly one (derived from the 6 main
  monthly balances published each month
  by INSEE) and a quarterly one
  (encompassing the 6 monthly balances the 2
  quarterly ones)
• Service survey data
• ? the 3 main monthly balances and 3 main
  quarterly ones ? the dynamic common factor in
  services (introduced by Cornec and
  Deperraz, 2007, derived from the 6 balances
  and published each month by INSEE)

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Methodology (1)

• We define several forecasting models of GDP for
  each month in a given quarter, so as to be able
  to up-date the short-term forecasts of GDP each
  month on the basis of the most recent piece of
  information given by the BTS.
• Notation
• Ind_m1 variable Ind derived from the industry
  survey relating to month 1
• Ser_m4 variable Ser derived from the industry
  survey relating to month 4
• with m1 (m2,m3, resp.) 1st (2nd, 3rd, resp.)
  month in the current
  quarter and m4 1st month in the following
  quarter.
• Examples
• - In the second quarter of year 2000, m1
  April, m2 May, m3 June, m4 July, of year
  2000.
• - In the last quarter of year 2004, m1
  October, m2 November, m3
  December 2004, m4 January 2005.

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Methodology (2) VAR models

• Comparison of the predictive accuracy of 3 VAR
  models of 2 kinds (non restricted, restricted)
  per couple of survey variables used
• - VAR with 3 variables GDP, Ind_mi,
  Ser_mi
• - VAR with 2 variables GDP, Ind_mi
• - simple AR model of GDP (basic
  benchmark)
• with Ind_mi (Ser_mi, resp.) a survey variable
  from the industry (services resp.) survey
  relating to month mi, i 1 to 4.
• Non restricted VAR VAR with 2 lags estimated
  using OLS Restricted VAR VAR with 4
  lags with exclusion restrictions,
  estimated using SURE
• Tests of equal predictive accuracy in nested
  models Clark-West tests, with
• 1) Scilab Grocer (correcting autocorrelation
  within forecast error series using Newey-West
  variances, for forecast horizons 2 to 4
  quarters)
• 2) SAS procedure autoreg, options nlag4 and
  backstep (testing for
  autocorrelation up to 4 lags and correcting it
  when necessary using Yule-Walker
  estimates)

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Methodology (3) Univariate calibration models (1)

• Intuition when the length of both estimation
  series and forecast error series is short
  (months 2 and 3, especially 2 no survey in
  August), univariate calibration models might be
  better adapted because more parsimonious than VAR
  models.
• Kinds of models estimated one set per month mi
  (i 1 to 4). At a given month when BTS are
  available up to the 1st forecast horizon
• Models used for the forecasting of GDP at a 1
  quarter horizon
• GDP function of the current and
  lagged values of survey
  variables
• Models used for the forecasting of GDP at a 2
  quarter horizon
• GDP function of the lagged values
  of survey variables
• Models used for the forecasting of GDP at a 3
  quarter horizon
• GDP function of the lagged values
  of survey variables to the
  exclusion of the first lags

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Methodology (4) Univariate calibration models (2)

• Comparison of the predictive accuracies of
  several univariate calibration models of GDP
• - some including explanatory variables
  from the two BTS
• - some including explanatory variables
  from the industry survey only
• - AR model of GDP (basic benchmark)
• - the best VAR3 models
• with, again, different sets of models depending
  on the month in (or just after) the quarter (mi,
  i 1 to 4).
• Depending on the models whose predictive
  accuracies are compared, we performed
• - either Clark-West tests (for the comparison
  of nested models)
• - or Harvey, Leybourne and Newbold tests (for
  the comparison of non-nested models)
• Software used Scilab
  Grocer.

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Results 1) VAR models (1)

• Choice of survey variables
• - Most leading balances
• Industry survey expected production
  (monthly) Service survey expected operating
  profit (quarterly)
• - Most correlated common factors (with GDP) at
  each month mi
• Industry survey
• for m1 and m4 the quarterly common
  factor
• for m2 and m3 the monthly common factor
• Service survey the dynamic common factor
  

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Results 1) VAR models (2)

• Contribution of BTS to the short-term forecasting
  of GDP (with respect to benchmark AR models)
• - Very significant at the 1 and 2 quarter
  horizons in most cases (very small P-values)
• - Significant at the 5 or 10 thresholds for
  several models relating to quarterly months
  (m1 and m4) at the 3 and, even, 4 quarter
  horizons (even though the quality of their
  forecasts remains poor)
• - Often more significant to forecast the first
  release of GDP than the last available release
  (at the 3 and 4 quarter horizons and, to a
  lesser extent at the 2 quarter horizon)

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Results 1) VAR models (3)

• Contribution of the service survey to the
  short-term forecasting of GDP (with respect to
  VAR models with 2 variables GDP and Ind_mi)
• 1) In the case of quarterly months (m1 and
  m4), for which fairly long time series in
  services are available
• - Significant, especially when the service
  variable is the dynamic common factor
• - Higher contribution for the 2 and 3
  quarter forecast horizons (non significant
  at the 4 quarter horizon)

2) In the case of non quarterly months (m2 and
m3), for which only short time series for
services are available - Less high
contribution of the service survey at this stage
but some encouraging significant results for
month m2 - Important remark The methodology
used creates a serious bias against the
service survey (either linear interpolations are
used for data before June 2000 or the last
available quarterly value of a balance
that in m1- is used for months m2 and m3
while more recent monthly industry data are
used)
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Results 1) VAR models (4)

• Example Results from the non restricted VAR
  model with the quarterly common factor in
  industry and the dynamic common factor in
  services (m4)

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Results 2) Univariate calibration models (1)

• We are currently working on these models.
• Two kinds of models have been estimated, using
  the Scilab-Grocer software
• Models whose optimal specifications are
  automatically determined by the software
• Models whose specifications are determined by the
  authors so that every explanatory variable has an
  impact of the expected sign on GDP.
• Our first preliminary results suggest that these
  kinds of models might enable one to lead to
  clearly positive results as concerns the
  contribution of the service survey, notably in
  month m3 (especially models whose specifications
  are automatically determined).
• However (at least on the basis of our
  preliminary results), these kinds of models do
  not appear to always lead to significantly better
  GDP forecasts than the VAR models.

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Results 2) Univariate calibration models (2)
Example Univariate models relating to a
three-quarter forecast horizon
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Conclusion

• 1) The study clearly confirms the predictive
  power of BTS for GDP growth at short-term
  horizons
• 2) It also shows that the quarterly service
  survey has predictive power alongside with
  the manufacturing survey
• 3) As far as monthly service data are concerned,
  it is definitely too early to have firm
  conclusions. Our results derive from
  methodologies that generate negative biases
  to the detriment of the service survey (use of
  either linear interpolated data before June
  2000 or less up-to-date data than industry
  ones). Nonetheless, some of the results
  suggest that monthly service data might also
  permit one to improve the short-term
  forecasting of GDP.

To be confirmed in 6 or 7 years when long enough
monthly service series are available!