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Nonstationary Time-Series Modeling versus Structural Equation Modeling: With an Application to Japanese Money Demand

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  • Hsiao, Cheng

    (U Southern CA and National Taiwan U)

  • Fujiki, Hiroshi

    (Bank of Japan)

Abstract

The issues of identification, estimation, and statistical inferences of nonstationary time series and simultaneous equation models are reviewed. It is shown that prior information matters and the advantage of dichotomization of the traditional autoregressive distributed lag model into the long-run equilibrium relation and the short-run dynamic adjustment process as an empirical modeling device may be exaggerated. A Japanese money demand study is used to illustrate that a direct approach yields a more stable long-run and short-run relationship and has better predictive power than the approach of letting the data determine the long-run relationship and modeling the short-run dynamics as an adjustment of the deviation from its equilibrium position.

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Bibliographic Info

Article provided by Institute for Monetary and Economic Studies, Bank of Japan in its journal Monetary and Economic Studies.

Volume (Year): 16 (1998)
Issue (Month): 1 (May)
Pages: 57-79

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Handle: RePEc:ime:imemes:v:16:y:1998:i:1:p:57-79

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  1. Goldfeld, Stephen M. & Sichel, Daniel E., 1990. "The demand for money," Handbook of Monetary Economics, in: B. M. Friedman & F. H. Hahn (ed.), Handbook of Monetary Economics, edition 1, volume 1, chapter 8, pages 299-356 Elsevier.
  2. Peter C.B. Phillips, 1993. "Fully Modified Least Squares and Vector Autoregression," Cowles Foundation Discussion Papers 1047, Cowles Foundation for Research in Economics, Yale University.
  3. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  4. ZELLNER, Arnold & PALM, Franz, . "Time series analysis and simultaneous equation econometric models," CORE Discussion Papers RP -173, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-91, May.
  6. Stock, James H, 1987. "Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors," Econometrica, Econometric Society, vol. 55(5), pages 1035-56, September.
  7. Peter C.B. Phillips & Joon Y. Park, 1986. "Statistical Inference in Regressions with Integrated Processes: Part 1," Cowles Foundation Discussion Papers 811R, Cowles Foundation for Research in Economics, Yale University, revised Aug 1987.
  8. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, vol. 59(2), pages 283-306, March.
  9. Peter C.B. Phillips, 1985. "Time Series Regression with a Unit Root," Cowles Foundation Discussion Papers 740R, Cowles Foundation for Research in Economics, Yale University, revised Feb 1986.
  10. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
  11. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  12. Hsiao, Cheng, 1997. "Statistical Properties of the Two-Stage Least Squares Estimator under Cointegration," Review of Economic Studies, Wiley Blackwell, vol. 64(3), pages 385-98, July.
  13. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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Cited by:
  1. Dekle, Robert & Hsiao, Cheng & Wang, Siyan, 2001. " Do High Interest Rates Appreciate Exchange Rates during Crisis? The Korean Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 63(3), pages 359-80, July.

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