Skip to main content

New Methods for Quantitative Analysis of Short-Term Economic Activity

  • Conference paper
COMPSTAT

Abstract

We concern ourselves with statistical treatment of economic time-series data used in short-term economic policy, control and monitoring. Although other frequencies are possible, our attention centers on monthly (also quarterly) series. The statistical treatment we have in mind includes short-term forecasting, seasonal adjustment, estimation of the trend, estimation of the business cycle, estimation of special effects and removal of outliers, perhaps for a large number of series.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Anderson, B. and Moore, J. (1979), Optimal Filtering, New Jersey: Prentice Hall.

    MATH  Google Scholar 

  • Bell, W.R. and Hillmer, S.C. (1984), “Issues Involved with the Seasonal Adjustment of Economic Time Series”, Journal of Business and Economic Statistics 2, 291–320.

    Article  Google Scholar 

  • Box, G.E.P., Hillmer, S.C. and Tiao, G.C. (1978), “Analysis and Modeling of Seasonal Time Series”, in Zellner, A. (ed.), Seasonal Analysis of Economic Time Series, Washington, D.C.: U.S. Dept. of Commerce — Bureau of the Census, 309–334.

    Google Scholar 

  • Box, G.E.P. and Jenkins, G.M. (1970), Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.

    MATH  Google Scholar 

  • Box, G.E.P. and Tiao, G.C. (1975), “Intervention Analysis with Applications to Economic and Environmental Problems”, Journal of the American Statistical Association 70, 71–79.

    Article  MathSciNet  Google Scholar 

  • Burman, J.P. (1980), “Seasonal Adjustment by Signal Extraction”, Journal of the Royal Statistical Society A, 143, 321–337.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, C. and Liu, L.M. (1993), “Joint Estimation of Model Parameters and Outlier Effects in Time Series”, Journal of the American Statistical Association 88, 284–297.

    Article  MATH  Google Scholar 

  • Chen, C., Liu, L.M. and Hudak, G.B. (1990), “Outlier Detection and Adjustment in Time Series Modeling and Forecasting”, Working Paper and Reprint Series, Scientific Computing Associates, Lyle (II), August 1990.

    Google Scholar 

  • Cleveland, W.P. and Tiao, G.C. (1976), “Decomposition of Seasonal Time Series: A Model for the X-11 Program”, Journal of the American Statistical Association 71, 581–587.

    Article  MathSciNet  MATH  Google Scholar 

  • Findley, D.F., Monsell, B., Otto, M., Bell, W. and Pugh, M. (1992), “Towards X-12 Arima”, mimeo, Bureau of the Census.

    Google Scholar 

  • Fischer, B. (1994), “Decomposition of Time Series, Comparison Between Five Methods of Seasonal Adjustment”, Eurostat

    Google Scholar 

  • Gómez, V. (1994), “Especificación Automatica de Modelos Arima en Pres-encia de Observaciones Atípicas”, mimeo, Departamento de Estadística e I.O., Universidad Complutense de Madrid, June 1994.

    Google Scholar 

  • Gomez, V. and Maravall, A. (1992), “Time Series Regression with Arima Noise and Missing Observations — Program TRAM”, Eui Working Paper Eco No. 92/81, Department of Economics, European University Institute.

    Google Scholar 

  • Gómez, V. and Maravall, A. (1993), “Initializing the Kaiman Filter with Incompletely Specified Initial Conditions”, in Chen, G.R. (ed.), Approximate Kaiman Filtering (Series on Approximation and Decomposition), London: World Scientific Publ. Co.

    Google Scholar 

  • Gómez, V. and Maravall, A. (1994), “Estimation, Prediction and Interpolation for Nonstationary Series with the Kaiman Filter”, Journal of the American Statistical Association, 89, 611–624.

    Article  MathSciNet  MATH  Google Scholar 

  • Hannan, E.J. and Rissanen, J. (1982), “Recursive Estimation of Mixed Autoregressive-Moving Average Order”, Biometrika 69, 81–94.

    Article  MathSciNet  MATH  Google Scholar 

  • Hillmer, S.C., Bell, W.R. and Tiao, G.C. (1983), “Modeling Considerations in the Seasonal Adjustment of Economic Time Series”, in Zellner, A. (ed.), Applied Time Series Analysis of Economic Data, Washington, D.C.: U.S. Department of Commerce — Bureau of the Census, 74–100.

    Google Scholar 

  • Hillmer, S.C. and Tiao, G.C. (1982), “An Arima-Model Based Approach to Seasonal Adjustment”, Journal of the American Statistical Association 77, 63–70.

    Article  MathSciNet  MATH  Google Scholar 

  • Kohn, R. and Ansley, C.F. (1985), “Efficient Estimation and Prediction in Time Series Regression Models”, Biometrika 72, 694–697.

    Article  MathSciNet  Google Scholar 

  • Kohn, R. and Ansley, C.F. (1986), “Estimation, Prediction and Interpolation for Arima Models with Missing Data”, Journal of the American Statistical Association 81, 751–761.

    Article  MathSciNet  MATH  Google Scholar 

  • Maravall, A. (1988), “The Use of Arima Models in Unobserved Components Estimation”, in Barnett, W., Berndt, E. and White, H. (eds.), Dynamic Econometric Modeling, Cambridge: Cambridge University Press.

    Google Scholar 

  • Maravall, A. (1995), “Unobserved Components in Economic Time Series”, Pesaran, EL, Schmidt, P. and Wickens, M. (eds.), The Handbook of Applied Econometrics, vol. 1, Oxford: Basil Blackwell.

    Google Scholar 

  • Maravall, A. and Gómez, V. (1992), “Signal Extraction in ARIMA Time Series — Program Seats”, Eui Working Paper Eco No. 92/65, Department of Economics, European University Institute.

    Google Scholar 

  • Maravall, A. and Pierce, D.A. (1987), “A Prototypical Seasonal Adjustment Model”, Journal of Time Series Analysis 8, 177–193.

    Article  MATH  Google Scholar 

  • Mélard, G. (1984), “A Fast Algorithm for the Exact Likelihood of Autoregressive-Moving Average Models”, Applied Statistics 35, 104–114.

    Article  Google Scholar 

  • Morf, M., Sidhu, G.S. and Kailath, T. (1974), “Some New Algorithms for Recursive Estimation on Constant, Linear, Discrete-Time Systems”, Ieee Transactions on Automatic Control, AC — 19, 315–323.

    Article  MATH  Google Scholar 

  • Stock, J. H. and Watson, M.W. (1988), “Variable Trends in Economic Time Series”, Journal of Economic Perspectives 2, 147–174.

    Google Scholar 

  • Tiao, G.C. and Tsay, R.S. (1983), “Consistency Properties of Least Squares Estimates of Autoregressive Parameters in Arma Models”, The Annals of Statistics 11, 856–871.

    Article  MathSciNet  MATH  Google Scholar 

  • Tsay, R.S. (1984), “Regression Models with Time Series Errors”, Journal of the American Statistical Association 79, 118–124.

    Article  MathSciNet  MATH  Google Scholar 

  • Tsay, R.S. (1986), “Time Series Models Specification in the Presence of Outliers”, Journal of the American Statistical Association 81, 132–141.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Physica-Verlag Heidelberg

About this paper

Cite this paper

Gómez, V., Maravall, A. (1996). New Methods for Quantitative Analysis of Short-Term Economic Activity. In: Prat, A. (eds) COMPSTAT. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-46992-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-46992-3_6

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-7908-0953-4

  • Online ISBN: 978-3-642-46992-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics