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DISCRETE TIME REPRESENTATION OF CONTINUOUS TIME ARMA PROCESSES

Published online by Cambridge University Press:  02 August 2011

Abstract

This paper derives exact discrete time representations for data generated by a continuous time autoregressive moving average (ARMA) system with mixed stock and flow data. The representations for systems comprised entirely of stocks or of flows are also given. In each case the discrete time representations are shown to be of ARMA form, the orders depending on those of the continuous time system. Three examples and applications are also provided, two of which concern the stationary ARMA(2, 1) model with stock variables (with applications to sunspot data and a short-term interest rate) and one concerning the nonstationary ARMA(2, 1) model with a flow variable (with an application to U.S. nondurable consumers’ expenditure). In all three examples the presence of an MA(1) component in the continuous time system has a dramatic impact on eradicating unaccounted-for serial correlation that is present in the discrete time version of the ARMA(2, 0) specification, even though the form of the discrete time model is ARMA(2, 1) for both models.

Type
Brief Report
Copyright
Copyright © Cambridge University Press 2011

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Footnotes

The second author’s research was supported by an Economic and Social Research Council studentship (PTA-030-2006-00395). We thank the editor, Peter Phillips, and two anonymous referees for helpful comments.

References

REFERENCES

Abowd, J.M. & Card, D. (1989) On the covariance structure of earnings and hours changes. Econometrica 57, 411445.10.2307/1912561CrossRefGoogle Scholar
Benth, F.E., Koekebakker, S., & Zakamouline, E.V. (2010) The CARMA Interest Rate Model. Manuscript, University of Agder.CrossRefGoogle Scholar
Bergstrom, A.R. (1983) Gaussian estimation of structural parameters in higher order continuous time dynamic models. Econometrica 51, 117152.10.2307/1912251CrossRefGoogle Scholar
Bergstrom, A.R. (1984) Continuous time stochastic models and issues of aggregation over time. In Griliches, Z. & Intriligator, M.D. (eds.), Handbook of Econometrics, vol. 2, pp. 11451212. North-Holland.CrossRefGoogle Scholar
Bergstrom, A.R. (1985) The estimation of parameters in nonstationary higher-order continuous-time dynamic models. Econometric Theory 1, 369385.10.1017/S0266466600011269CrossRefGoogle Scholar
Bergstrom, A.R. (1986) The estimation of open higher-order continuous time dynamic models with mixed stock and flow data. Econometric Theory 2, 350373.10.1017/S026646660001166XCrossRefGoogle Scholar
Bergstrom, A.R. (1990) Continuous Time Econometric Modelling. Oxford University Press.Google Scholar
Bergstrom, A.R. (1997) Gaussian estimation of mixed-order continuous-time dynamic models with unobservable stochastic trends from mixed stock and flow data. Econometric Theory 13, 467505.10.1017/S0266466600005971CrossRefGoogle Scholar
Brockwell, P.A. (2001) Lévy-driven CARMA processes. Annals of the Institute of Statistical Mathematics 53, 113124.10.1023/A:1017972605872CrossRefGoogle Scholar
Brockwell, P.A. (2004) Representations of continuous-time ARMA processes. Journal of Applied Probability 41A, 375382.10.1239/jap/1082552212CrossRefGoogle Scholar
Brockwell, P.A. & Marquardt, T. (2005) Lévy-driven and fractionally integrated ARMA processes with continuous time parameter. Statistica Sinica 15, 477494.Google Scholar
Chambers, M.J. (1999) Discrete time representation of stationary and non-stationary continuous time systems. Journal of Economic Dynamics and Control 23, 619639.10.1016/S0165-1889(98)00032-3CrossRefGoogle Scholar
Chambers, M.J. (2009) Discrete time representations of cointegrated continuous time models with mixed sample data. Econometric Theory 25, 10301049.10.1017/S0266466608090397CrossRefGoogle Scholar
Christiano, L.J., Eichenbaum, M., & Marshall, D.A. (1991) The permanent income hypothesis revisited. Econometrica 59, 397423.10.2307/2938262CrossRefGoogle Scholar
Deaton, A. (1992) Understanding Consumption. Oxford University Press.10.1093/0198288247.001.0001CrossRefGoogle Scholar
Harvey, A.C. & Stock, J.H. (1985) The estimation of higher-order continuous time autoregressive models. Econometric Theory 1, 97117.10.1017/S0266466600011026CrossRefGoogle Scholar
Harvey, A.C. & Stock, J.H. (1988) Continuous time autoregressive models with common stochastic trends. Journal of Economic Dynamics and Control 12, 365384.10.1016/0165-1889(88)90046-2CrossRefGoogle Scholar
Heaton, J. (1993) The interaction between time non-separable preferences and time aggregation. Econometrica 61, 353385.10.2307/2951555CrossRefGoogle Scholar
Kwakernaak, H. & Sivan, R. (1972) Linear Optimal Control Systems. Wiley.Google Scholar
Lütkepohl, H. (2005) New Introduction to Multiple Time Series Analysis. Springer-Verlag.10.1007/978-3-540-27752-1CrossRefGoogle Scholar
MaCurdy, T.E. (1982) The use of time series processes to model the error structure of earnings in a longitudinal data analysis. Journal of Econometrics 18, 83114.10.1016/0304-4076(82)90096-3CrossRefGoogle Scholar
Nowman, K.B. (1997) Gaussian estimation of single-factor continuous time models of the term structure of interest rates. Journal of Finance 52, 16951706.10.1111/j.1540-6261.1997.tb01127.xGoogle Scholar
Nowman, K.B. (1998) Continuous-time short term interest rate models. Applied Financial Economics 8, 401407.10.1080/096031098332934CrossRefGoogle Scholar
Park, J.Y. & Jeong, M. (2010) Asymptotic Theory of Maximum Likelihood Estimator for Diffusion Model. Manuscript, Indiana University.Google Scholar
Phadke, M.S. & Wu, S.M. (1974) Modeling of continuous stochastic processes from discrete observations with application to sunspots data. Journal of the American Statistical Association 69, 325329.10.1080/01621459.1974.10482947CrossRefGoogle Scholar
Phillips, P.C.B. (1991) Error correction and long-run equilibrium in continuous time. Econometrica 59, 967980.10.2307/2938169CrossRefGoogle Scholar
Phillips, P.C.B. & Yu, J. (2009) Maximum likelihood and Gaussian estimation of continuous time models in finance. In Anderson, T.G., Davis, R.A., Kreiss, J.P., & Mikosch, T. (eds.), Handbook of Financial Time Series, pp. 497530. Springer-Verlag.10.1007/978-3-540-71297-8_22CrossRefGoogle Scholar
Pischke, J.-S. (1995) Individual income, incomplete information, and aggregate consumption. Econometrica 63, 805840.10.2307/2171801CrossRefGoogle Scholar
Robinson, P.M. (1976) Fourier estimation of continuous time models. In Bergstrom, A.R. (ed.), Statistical Inference in Continuous Time Economic Models, pp. 215266. North-Holland.Google Scholar
Robinson, P.M. (1993) Continuous-time models in econometrics: Closed and open systems, stocks and flows. In Phillips, P.C.B. (ed.), Models, Methods, and Applications of Econometrics: Essays in Honor of A.R. Bergstrom, pp. 7190. Blackwell.Google Scholar
Thornton, M.A. (2009) Information and aggregation: The econometrics of dynamic models of consumption under cross-sectional and temporal aggregation. Ph.D. Dissertation, University of Essex.Google Scholar
Thornton, M.A. & Chambers, M.J. (2010) Continuous Time ARMA Processes in Rival State Space Forms. Manuscript, University of Reading.Google Scholar
Vasicek, O. (1977) An equilibrium characterization of the term structure. Journal of Financial Economics 5, 177188.10.1016/0304-405X(77)90016-2CrossRefGoogle Scholar
Working, H. (1960) Note on the correlation of first differences of averages in a random chain. Econometrica 28, 916918.10.2307/1907574CrossRefGoogle Scholar
Zadrozny, P. (1988) Gaussian likelihood of continuous-time ARMAX models when data are stocks and flows at different frequencies. Econometric Theory 4, 108124.10.1017/S0266466600011890CrossRefGoogle Scholar