De Gooijer, J. G.

García-Ferrer, Antonio

Poncela, Pilar

Ruiz, Esther

English

Research Papers in Economics

Editorial
Introduction to nonlinearities, business cycles, and forecasting
The first International Institute of Forecasters
workshop on bNonlinearities, business cycles and
forecastingQ took place on the 12th and 13th of
December 2003 in Madrid. Nine invited papers by
outstanding speakers were presented, followed by
formal comments of invited discussants. In addition,
there were lively discussions from the audience. This
special issue provides reviewed versions of eight of
these papers with additional comments by the
discussants. Unfortunately the lecture by Mark Wat-
son, entitled bForecasting using empirical Bayes
methods and a large number of predictorsQ, was not
made available for this special issue.
The overall theme of the workshop was motivated
by a recently renewed interest in business cycles;
see, e.g., the 2001 special issue of this Journal edited
by Holden, Klein and Lahiri. Indeed, after several
years where business cycles seemed outdated, the
issue is recovering its flavour and the interest of
academics, financial analysts, and even politicians.
Logically, the complexities associated with business-
cycle analysis have provoked a renewed interest in
the measurement, implications and analysis of the
cycle. Therefore the workshop tried to cover three
fundamental issues:
1. How the cyclical information is extracted;
2. How this information should be analysed; and
3. What are the difficulties when using the cyclical
indicators for monitoring and predicting the eco-
nomic activity?
These questions are linked to a set of technical
aspects such as the presence of nonlinearities (and the
associated test statistics), the choice of the appropriate
transformations to capture the cycles’ asymmetries,
the advantage in forecasting of modelling the aggre-
gate versus modelling the individual series, dimen-
sionality reduction procedures, etc.
On the other hand, understanding the sources of
international economic fluctuations is important both
for developing business-cycle models and for making
policy. In this case, the issues of sample size and using
cross-country panels become very relevant. For
instance, if most of the variation in economic activity
in a set of countries with different economic policies,
institutions and economic structures is explained by a
world business cycle, this lends support to the
predictions of theoretical models emphasizing the
common characteristics in the operations of markets
rather than the differences in economic policies or
institutional environments in those countries. Con-
sequently, if a significant fraction of domestic busi-
ness cycles is due to the common world factor, this
implies that policies targeting external balances to
stabilize sudden movements in economic activity
might be ineffective. Is it true, as some recent and
controversial findings claim, that the distinct
bEuropeanQ business cycle appears to be an artefact
of limited samples? (Kose, Otrok, & Whiteman,
2003). The interdisciplinary nature of the topics
requires a combination of macroeconomic, statistical
and econometric expertise. Against the above back-
ground we grouped the papers presented at the
workshop into two different sets. The first four papers
focus on nonlinearity in relation to the business cycle
while the last four papers deal with various nonlinear
time series models, model selection techniques, and
their application to the analysis of nonlinearties in
macroeconomic time series.
0169-2070/$ - see front matter D 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.ijforecast.2005.04.001
International Journal of Forecasting 21 (2005) 623 625
1The Zellner/Israilevich paper describes the origins
and early development of the structural econometric
modelling and time series analysis (SEMTSA) meth-
odology/approach and its connection with the multi-
sector Marshallian Macroeconomic Model (MMM).
The authors review the past forecasting performance
of the multivariate model. Then, using the SEMTSA
approach, a discrete time one-sector MMM is
presented with monetary and government sectors.
Next the MMM is extended to n-sectors. A series of
dynamic simulation experiments with various (non-
linear) versions of the MMM indicate that disaggre-
gated data produces improved forecasts of aggregate,
real GDP growth rates as well as sector forecasts. The
discussant, Antoni Espasa, summarizes the main
features of the SEMTSA/MMM approach. He points
out the importance of incorporating seasonal effects
and cointegration in the multi-sector MMM. Also, he
stresses the need to detect and model the non-
linearities of aggregate variables by modeling those
components which are clearly nonlinear, rather than
by fitting a nonlinear model directly.
The Engel/Haugh/Pagan paper subjects the view
that nonlinear models are important to an explanation
of the business cycle to some critical analysis. They
discuss ways of measuring the characteristics of the
business cycle. A linear AR model and two univariate
nonlinear models are fitted to US GDP. It appears that
the two nonlinear models add little to the explanation
for the asymmetry of phases. The discussant, Gabriel
Pe ´rez-Quiro ´s, summarizes the main features of the
paper and provides some general suggestions for
further research.
In the Carvalho/Harvey paper a multivariate
unobserved components (structural) model is fitted
to eight US regional time series. A key feature of the
proposed model is that it embodies convergence
components which are able to display temporary
divergence before converging to a common trend, also
when the model is used for forecasting. Other
theoretical issues of the C/H paper are summarized
by the discussants Jerez/Casals/Sotoca. They com-
ment on the profile of the convergence components.
In response to this comment and the final remark by
Augustin Maravall, a new sub-section (2.2) on
convergence was added to the paper. It makes the
motivation behind the multivariate convergence
model much clearer. In particular, the paragraph
below Eq. (7) explains why convergence to a sta-
tionary process can be quite slow for the second-order
error correction model; see also the forecasts of the
US regions in Fig. 9 of the C/H paper.
Kaiser/Maravall suggest that, in the extraction of
smooth trends and cycles, the so-called ARIMA-
model-based (AMB) signal extraction procedure with
the Hodrick-Prescott (HP) filter may solve the
problems associated with the single application of
both methods separately. The comments of the
discussant, Javier Ferna ´ndez-Macho, focus on two
issues: (a) the HP filter can hardly be a solution to the
drawbacks of the AMB approach, and (b) adding the
ad-hoc HP filter to the AMB approach will yield
bspurious and misleadingQ results. As an alternative
the discussant suggests that the structural time series
approach may provide a better solution in the sense
that it is based on proper time series models for each
component, thus allowing the use of classical proce-
dures for testing. Both comments (a) and (b) are
addressed by K/M in detail in Section 5 of their paper.
Harvill/Ray present multi-step ahead forecasting
results using univariate and multivariate functional
coefficient AR (FCAR/VFCAR) models. FCAR
models provide a fairly general, yet relatively simple,
class of nonlinear time series models for practical use.
There are several methods in the literature dealing
with forecasting of general nonlinear time series
processes. The authors restrict their comparison to
three methods: naı ¨ve plug-in predictor, the bootstrap
predictor, and the multi-stage predictor. Both simu-
lation and empirical results indicate that the bootstrap
method appears to give slightly more accurate forecast
results for the data and models under study. The
discussant, Nuno Crato, emphasizes the need for
correctly specified nonlinear models and the effect of
misperceived models on forecast evaluations. This
requires further study and comparison of methods for
identifying the functional structure of (V)FCAR and
other nonlinear time series processes.
This latter issue is explored in more detail in the
paper by Pen ˜a/Rodriguez. In particular, the authors
explore the performance of various model selection
criteria to obtain the order of the best fitted AR(p)
model to the squared residuals of the linear model.
Using four different linear model selection criteria
(called bchecksQ by the discussants), they conclude
that there is no evidence of nonlinearity if p=0. On
Editorial 624
2the other hand, if pN0 there is a strong indication of
nonlinearity. This is further checked by performing
various linearity tests. Monte Carlo simulations
suggest that BIC is a reasonable tool for detecting
nonlinearity in time series. The discussants, Charles
Bos and Ana Justel, summarize the main findings of
the paper in their Table 2. They comment on the
simulation set-up, and the iterative detection proce-
dure. Instead of performing multiple linearity tests
jointly, they propose a sequential testing procedure
and illustrate its use through a small-scale simulation
study.
The Tera ¨svirta/van Dijk/Medeiros paper is a very
thorough evaluation study of the forecast accuracy of
the (logistic) smooth transition autoregression model
and two neural networks models using 47 monthly
macroeconomic time series. Linear AR models are
used as benchmarks. The number of topics discussed
by the authors is quite extensive. Alfonso Novales,
as discussant, summarizes seven topics in his
introductory paragraph. He makes the important
distinction between quantitative and qualitative fore-
cast evaluation. If, as in the T/vD/M paper, the
quantitative approach is followed, and a 5% (alter-
natively 2.5%) difference in RMSEs indicates that
one single model is better than another, no clear
bwinnerQ can be found. As a consequence, Novales
stresses the need for a formal statistical test leading
to a threshold for RMSE differences, as a function of
statistical characteristics of the variable under study,
different from the well-known Diebold-Mariano
approach. Other interesting comments and sugges-
tions for further research include the relationship
between relative forecasting performance and fore-
cast horizon, and the gains of forecast combinations
from nonlinear models.
Can forecasts for macroeconomic aggregates like
total output or total unemployment be improved by
using a multi-level panel smooth transition AR
(STAR) model for the disaggregated series? This is
the key issue examined in the Fok/van Dijk/Franses
paper. Based on simulation experiments and on
comparisons of total US output forecasts with
forecasts for US state-level output, they claim that
improvements in one-step ahead forecasts can indeed
be achieved. The discussant, Juan del Hoyo, points
out some difficulties with the modelling process of
STAR models. Given the complexity of the proposed
panel STAR model, he suggests more research on
the performance of the (time-consuming) ML esti-
mation procedure. Since the differences in average
MSPEs are minimal, the discussant also stresses the
use of alternative forecasting evaluation methods like
interval and density forecasts (one- and multi-step
ahead).
Acknowledgement
We gratefully acknowledge financial support from
the International Institute of Forecasters, Elsevier
Science, The March Foundation, the Spanish Ministry
of Science and Technology (SEC2002-10511-E), and
the Autono ´ma de Madrid and Carlos III universities.
We thank the editor of Macroeconomic Dynamics and
Elsevier for allowing us to reproduce the Zellner/
Israilevich paper.
Reference
Kose, M. A., Otrok, C., & Whiteman, C. H. (2003). International
business cycles: World, region, and country specific factors.
American Economic Review, 93(4), 1216 1239.
Antonio Garcı ´a-Ferrer
Departamento de Analisis Economico,
Universidad Autonoma de Madrid, Cantoblanco,
28049 Madrid, Spain
Email address: Antonio.garcia@uam.es.
Jan G. De Gooijer
Department of Quantitative Economics,
University of Amsterdam, Roetersstraat 11,
1018 WB Amsterdam, The Netherlands
Email address: j.g.degooijer@uva.nl.
Corresponding author.
Pilar Poncela
Departamento de Analisis Economico,
Universidad Autonoma de Madrid, Cantoblanco,
28049 Madrid, Spain
Email address: pilar.poncela@uam.es.
Esther Ruiz
Department of Statistics and Econometrics,
Universidad Carlos III de Madrid, Spain
Email address: ortega@est-econ.uc3m.es.
Editorial 625
3

1239. Antonio Garcı ´a-Ferrer Departamento de Analisis Economico, Universidad Autonoma de

International business cycles: World, region, and country specific factors.

Introduction to nonlinearities, business cycles, and forecasting.

http://e-archivo.uc3m.es/bitstream/10016/4729/1/introduction_ruiz_IJF_2005_ps.pdf

Introduction to nonlinearities, business cycles, and forecasting.

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