nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

13. Time Series Modeling and the Forecasting Effectiveness of the US Leading Economic Indicators

verfasst von : John B. Guerard Jr., Anureet Saxena, Mustafa N. Gültekin

Erschienen in: Quantitative Corporate Finance

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

An important aspect of financial decision-making may depend on the forecasting effectiveness of the composite index of leading economic indicators, LEI. The leading indicators can be used as an input to a transfer function model of real gross domestic product, GDP. The previous chapter employed four quarterly lags of the LEI series to estimate regression models of association between current rates of growth of real US GDP and the composite index of leading economic indicators. This chapter examines whether changes in forecasted economic indexes help forecast changes in real economic growth. The transfer function model forecasts are compared to several naïve models in order to test which model produces the most accurate forecast of real GDP. No-change forecasts of real GDP and random walk with drift models may be useful when forecasting benchmarks (Mincer & Zarnowitz, 1969; Granger & Newbold, 1977). Economists have constructed leading economic indicator series to serve as a business barometer of the changing US economy since the time of Wesley C. Mitchell (1913). The purpose of this study is to examine the time series forecasts of composite economic indexes produced by The Conference Board (TCB) and test the hypothesis that the leading indicators are useful as an input to a time series model to forecast real output in the United States.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Regression Analysis and Estimating Regression Models

Nächstes Kapitel Risk and Return of Equity, the Capital Asset Pricing Model, and Stock Selection for Efficient Portfolio Construction

Nur mit Berechtigung zugänglich

This section draws heavily from Box and Jenkins, Time Series Analysis, Chapters 2 and 3.

Please see Box and Jenkins, Time Series Analysis, Chapter 3, for the most complete discussion of the ARMA (p,q) models.

A stationary AR(p) process can be expressed as an infinite weighted sum of the previous shock variables

$$ {\tilde{Z}}_1={\phi}^{-1}(B){\alpha}_t. $$

In an invertible time series, the current shock variable may be expressed as an infinite weighted sum of the previous values of the series

$$ {\theta}^{-1}(B){\tilde{Z}}_t={\alpha}_t. $$

Box and Jenkins, Time Series Analysis. Chapter 6; C.W.J. Granger and Paul Newbold, Forecasting Economic Time Series. Second Edition (New York: Academic Press, 1986), pp. 109–110, 115–117, 206.

Granger and Newbo1d, Forecasting Economic Time Series. pp. 185–186.

See Box and Jenkins, Time Series Analysis, p. 79.

Box and Jenkins, Time Series Analysis. pp. 173–179. Nelson (1973) is one of the texts used in the 1970s for forecasting and is still a very useful guide to applied time series modeling.

G.E. Box and D.R. Cox, “An Analysis of Transformations,” Journal of the Royal Statistical Society, B 26 (1964), 211–243.

G.M. Jenkins, “Practical Experience with Modeling and Forecasting Time Series,” Forecasting (Amsterdam: North-Holland Publishing Company, 1979).

Jenkins, op. cit., pp. 135–138.

Box and Jenkins, Time Series Analysis, pp. 305–308.

Box and Jenkins, op. cit.

The cutting off of the PACF suggests an AR(1) process. Had one estimated an ARIMA (1,1,0), the estimated AR (1) = −0.261 (t = − 3.63) and the estimated variance = 23116.6, slightly less than the ARIMA (0,1,1). The estimated time series models are essentially identical. The residuals are random, having a chi-square statistics of 2.93 at lag 18.

Box and Jenkins, Time Series Analysis. Chapter 6; C.W.J. Granger and Paul Newbold, Forecasting Economic Time Series. Second Edition (New York: Academic Press, 1986), pp. 109–110, 115–117, 206.

Automatic time series modelling has advocated since the early days of Box and Jenkins (1970). Reilly (1980), with the Autobox System, pioneered early automatic time series model implementation. Tsay (1988) identified outliers, level shifts, and variance change models that were implemented in PC-SCA. SCA was used in modelling time series in MZTT (1998).

Doornik and Hendry (2013) remind the reader that the data generation process (DGP) is impossible to model, and the best solution that one can achieve is estimate the models to reflect the local DGP, through reduction, described above. The Automatic Gets algorithm reduces GUM to nest LGDP, the locally relevant variables. Congruency, in which the LGDP has the same shape and size as the GUM or models, reflects the local DGP.

In the selection process, one tests the null hypothesis that the parameter in front of a variable is zero. The relevant t-statistic from a two-sided test is used.

The use of saturation variables avoids the issue of forcing a unit root to capture the shifts, leading to an upward biased estimate of the lagged dependent variable coefficient. The authors are indebted to Jenny Castle for her comments on the application of saturation variables, which she observes addresses this very well.

The authors estimated separate transfer function models using the WKUCL time series as input to the UER time series. The WKUCL input produced positive and statistically significant coefficients in the Autometrics estimations.

Guerard et al. (2020) reported that the LEI time series was a useful input in the real GDP time series and changes in the US unemployment rate models estimated with Autometrics during the 1959–2018 period.

The authors are indebted to Professor Jennifer Caste of Oxford for her support in reading and helping the authors efficiently explore structural break levels. Any errors remaining are solely those of the authors.

Ashley, R. (1998). A new technique for postsample model selection and validation. Journal of Economic Dynamics and Control, 22, 647–665.CrossRef

Ashley, R. A. (2003). Statistically significant forecasting improvements: How much out-of-sample data is likely necessary? International Journal of Forecasting, 19, 229–240.CrossRef

Ashley, R., Granger, C. W. J., & Schmalensee, R. (1980). Advertising and aggregate consumption: An analysis of causality. Econometrica, 48, 149–167.CrossRef

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day.

Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Association Series B, 26, 211–243.

Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER.

Castle, J., & Hendry, D. F. (2019). Modelling our changing world. Palgrave.CrossRef

Castle, J., & Shepard, N. (2009). The methodology and practice of econometrics. Oxford University Press.CrossRef

Castle, J., Doornik, J. A., & Hendry, D. F. (2013). Model selection in equations with many ‘small’ effects. Oxford Bulletin of Economics and Statistics, 75, 6–22.CrossRef

Castle, J., Clements, M. P., & Hendry, D. F. (2015). Robust approaches to forecasting. International Journal of Forecasting, 31, 99–112.CrossRef

Chen, C., & Lee, C. J. (1990). A VARMA test on the Gibson paradox. Review of Economics and Statistics, 72, 96–107.CrossRef

Chen, C., & Liu, L.-M. (1993a). Joint estimation of model parameters and outliers in time series. Journal of the American Statistical Association, 88, 284–297.

Chen, C., & Liu, L.-M. (1993b). Forecasting time series with outliers. Journal of Forecasting, 12, 13–35.CrossRef

Clemen, R. T., & Guerard, J. B. (1989). Econometric GNP forecasts: Incremental information relative to naive extrapolation. International Journal of Forecasting, 5, 417–426.CrossRef

Clements, M. P., & Hendry, D. F. (1998). Forecasting economic time series. Cambridge University Press.CrossRef

Dhrymes, P. J. (2017). Introductory econometrics. Springer.CrossRef

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253–263.

Diebold, F. X., & Rudebusch, G. D. (1999). Business cycles: Durations, dynamics and forecasting. Princeton University Press.CrossRef

Fisher, I. (1911a). Recent changes in price levels and their causes. The American Economic Review, 1, 37–45.

Fisher, I. (1911b). The purchasing power of money. Macmillan.

Gordon, R. J. (1986). The American business cycle. University of Chicago Press.CrossRef

Granger, C. W. J. (1969). Investigating casual relations by economic models and cross-spectral methods. Econometrica, 37, 424–438.CrossRef

Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329–352.CrossRef

Granger, C. W. J. (2001). In E. Ghysels, N. R. Swanson, & M. W. Watson (Eds.), Essays in econometrics. Two volumes. Cambridge University Press.

Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. Academic Press.

Guerard, J. B. (1985). Mergers, stock prices, and industrial production: An empirical test of the Nelson hypothesis. In O. D. Anderson (Ed.), Time series analysis: Theory and practice 7. North-Holland Publishing Company.

Guerard, J. B., & McDonald, J. B. (1995). Mergers in the United States: A case study in robust time series. In C. F. Lee (Ed.), Advances in financial planning and forecasting. JAI Press.

Guerard, J., Thomakos, D., & Kyriazi, F. (2020). Automatic time series modeling and forecasting: A replication case study of forecasting real GDP, the unemployment rate and the impact of leading economic indicators. Cogent Economics & Finance, 8(1), 1759483.CrossRef

Hamilton, J. R. (1994). Time series analysis. Princeton.CrossRef

Hendry, D. F. (1986). Using PC-give in econometrics teaching. Oxford Bulletin of Economics and Statistics, 48, 87–98.CrossRef

Hendry, D. F. (2000). Econometrics: Alchemy or science? Oxford University Press.CrossRef

Hendry, D. F., & Doornik, J. A. (2014). Empirical model discovery and theory evaluation. MIT Press.CrossRef

Hendry, D. F., & Krolzig, H. M. (2005). The properties of automatic gets modelling. The Economic Journal, 115, c32–c61.CrossRef

Koopmans, T. C. (1947). Measurement without theory. The Review of Economic Statistics. Reprinted in R. Gordon & L. Klein, Readings in Business Cycles, Homewood, Illinois, Richard A. Irwin, Inc, 1965.

Koopmans, T. C. (1950). When is an equation system complete for statistical purposes. Technical report, Yale University. URL http://cowles.econ.yale.edu/P/cm/m10/m10-17.pdf

Krolzig, H.-M. (2001). Business cycle measurement in the presence of structural change: International evidence. International Journal of Forecasting, 17, 349–368.CrossRef

Krolzig, H.-M., & Hendry, D. F. (2001). Computer automation of general-to-Specific model selection procedures. Journal of Economic Dynamics & Control, 831–866.

Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibian-Barrera, M. (2019). Robust statistics: Theory and methods (with R). Wiley.

McCracken, M. (2000). Robust out-of-sample inference. Journal of Econometrics, 39, 195–223.CrossRef

Melicher, R., Ledolter, J., & D'Antonio, L. J. (1983). A time series analysis of aggregate merger activity. Review of Economics and Statistics, 65, 423–430.CrossRef

Mincer, J., & Zarnowitz, V. (1969). The evaluation of economic forecasts. In J. Mincer (Ed.), Economic forecasts and expectations. Columbia University Press.

Mitchell, W. C. (1913). Business cycles. Burt Franklin reprint.

Mitchell, W. C. (1951). What happens during business cycles: A Progress report. NBER.

Montgomery, A. L., Zarnowitz, V., Tsay, R., & Tiao, G. C. (1998). Forecasting the U.-S. unemployment rate. Journal of the American Statistical Association, 93, 478–493.

Moore, G. H. (1961). Business cycle indicators (2 Vols). Princeton University Press.

Nelson, R. L. (1959). Merger movements in American industry 1895–1954. Princeton University Press.

Nelson, R. L. (1966). Business cycle factors in the choice between internal and external growth. In W. W. Alberts & J. E. Segall (Eds.), The corporate merger. The University of Chicago Press.

Nelson, C. R. (1973). Applied time series analysis for managerial forecasting. Holden-Day, Inc.

Nelson, C. R., & Plosser, C. I. (1982). Trends and random walks in macroeconomic time series. Journal of Monetary Economics, 10, 139–162.CrossRef

Swanson, N. R. (1998). Money and output viewed through a rolling window. Journal of Monetary Economics, 41(1998), 455–473.CrossRef

Theil, H. (1966). Applied economic forecasting. North-Holland.

Thomakos, D., & Guerard, J. (2004). Naïve, ARIMA, transfer function, and VAR models: A comparison of forecasting performance. The International Journal of Forecasting, 20, 53–67.CrossRef

Thomakos, D., & Guerard, J.. (2022). Causality testing and forecasting of the US unemployment rate. Unpublished Working Paper. University of Athens.

Tsay, R. S. (1988). Outliers, level shifts, and variance changes in time series. Journal of Forecasting, 7, 1–20.CrossRef

Tsay, R. S. (1989). Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association, 84, 231–249.CrossRef

Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). Wiley.CrossRef

Tsay, R. S., & Chen, R. (2019). Nonlinear time series analysis. Wiley.

Vining, R. (1949). Koopmans on the choice of variables to be studied and on methods of measurement. The Review of Economics and Statistics. Reprinted in R. Gorgon & L. Klein, Readings in Business Cycles, Homewood, Illinois: Richard A. Irwin, Inc, 1965.

Vinod, H. D. (2008). Hands-on intermediate econometrics using R: Templates for extending dozens of practical examples. World Scientific. ISBN 10-981-281-885-5. URL http://www.worldscibooks.com/economics/6895.htmlCrossRef

Vinod, H. D. (2014). Chapter 4: Matrix algebra topics in statistics and economics using R. In M. B. Rao & C. R. Rao (Eds.), Handbook of statistics: Computational statistics with R (Vol. 34, pp. 143–176).CrossRef

Vinod, H. D. (2017). Causal paths and exogeneity tests in generalCorr package for air pollution and monetary policy. A vignette accompanying the package “generalCorr” of R. URL: https://cloud.r-project.org/web/packages/generalCorr/vignettes/generalCorr-vignette3.pdf

Vinod, H. D. (2019). Chapter 2: New exogeneity tests and causal paths. In H. D. Vinod & C. R. Rao (Eds.), Handbook of statistics: Conceptual econometrics using R (Vol. 41, pp. 33–64). North Holland, Elsevier. https://doi.org/10.1016/bs.host.2018.11.011CrossRef

West, K., & McCracken, M. (1998). Regression-based tests of predictive ability. International Economic Review, 39, 817–840.CrossRef

Zarnowitz, V. (1992). Business cycles: Theory, history, indicators, and forecasting. University of Chicago Press.CrossRef

Zarnowitz, V. (2001). The old and the new in the U.S. economic expansion. The Conference Board. EPWP #01-01.

Zarnowitz, V. (2004). The autonomy of recent US growth and business cycles. In P. Dua (Ed.), Business cycles and economic growth: An analysis using leading indicators (pp. 44–82). Oxford University Press.

Zarnowitz, V., & Ozyildirim, A. (2001). On the measurement of business cycles and growth cycles. Indian Economic Review, 36, 34–54.

Titel: Time Series Modeling and the Forecasting Effectiveness of the US Leading Economic Indicators
verfasst von: John B. Guerard Jr.
Anureet Saxena
Mustafa N. Gültekin
Verlag: Springer International Publishing
Buch: Quantitative Corporate Finance
Print ISBN: 978-3-030-87268-7

Electronic ISBN: 978-3-030-87269-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-87269-4_13

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"