Nonlinearity, Nonstationarity, and Thick Tails: How They Interact to Generate Persistency in Memory
In this paper, we consider nonlinear transformations of random walks driven by thick-tailed innovations with undefined means or variances. In particular, we show how nonlinearity, nonstationarity, and thick tails interact to generate persistency in memory, and we clearly demonstrate that this triad may generate a broad spectrum of persistency patterns. Time series generated by nonlinear transformations of random walks with thick-tailed innovations have asymptotic autocorrelations that decay very slowly as the number of lags increases or do not even decay at all and remain constant at all lags. Depending upon the type of transformation considered and how the model error is specified, they are given by random constants, deterministic functions which decay slowly at polynomial rates, or mixtures of the two. These patterns in autocorrelations, along with other sample characteristics of the transformed time series, make it very plausible that this triad is involved in the data generating processes for many actual economic and financial time series data. We use our model to analyze two empirical applications: exchange rates governed by a target zone and electricity price spikes driven by capacity shortfalls
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- Joon Y. Park & Yoosoon Chang, 2004. "Endogeneity in Nonlinear Regressions with Integrated Time Series," Econometric Society 2004 North American Winter Meetings 594, Econometric Society.
- Donald W. K. Andrews & Patrik Guggenberger, 2003.
"A Bias--Reduced Log--Periodogram Regression Estimator for the Long--Memory Parameter,"
Econometric Society, vol. 71(2), pages 675-712, March.
- Donald W.K. Andrews & Patrik Guggenberger, 2000. "A Bias-Reduced Log-Periodogram Regression Estimator for the Long-Memory Parameter," Cowles Foundation Discussion Papers 1263, Cowles Foundation for Research in Economics, Yale University.
- Tom Doan, "undated". "AGFRACTD: RATS procedure to compute Andrews-Guggenberger estimate of fractional difference," Statistical Software Components RTS00005, Boston College Department of Economics.
- Paul R. Krugman, 1991. "Target Zones and Exchange Rate Dynamics," The Quarterly Journal of Economics, Oxford University Press, vol. 106(3), pages 669-682.
- de Jong, F, 1994.
"A Univariate Analysis of EMS Exchange Rates Using a Target Zone Model,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 9(1), pages 31-45, Jan.-Marc.
- De Jong , F., 1991. "A Univariate Analysis of EMS Exchange Rates Using a Target Zone Model," Papers 9155, Tilburg - Center for Economic Research.
- Lars E.O. Svensson, 1990.
"The Term Structure of Interest Rate Differentials in a Target Zone: Theory and Swedish Data,"
NBER Working Papers
3374, National Bureau of Economic Research, Inc.
- Svensson, Lars E. O., 1991. "The term structure of interest rate differentials in a target zone : Theory and Swedish data," Journal of Monetary Economics, Elsevier, vol. 28(1), pages 87-116, August.
- Svensson, L.E.O., 1990. "The Term Structure of Interest Rate Differentials in a Target Zone: Theory and Swedish Data," Papers 466, Stockholm - International Economic Studies.
- Svensson, Lars E O, 1991. "The Term Structure of Interest Rate Differentials in a Target Zone: Theory and Swedish Data," CEPR Discussion Papers 495, C.E.P.R. Discussion Papers.
- Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
- Joon Y. Park & Peter C. B. Phillips, 1999.
"Nonlinear Regressions with Integrated Time Series,"
Working Paper Series
no6, Institute of Economic Research, Seoul National University.
- Peter C.B. Phillips & Joon Y. Park, 1998.
"Asymptotics for Nonlinear Transformations of Integrated Time Series,"
Cowles Foundation Discussion Papers
1182, Cowles Foundation for Research in Economics, Yale University.
- Park, Joon Y. & Phillips, Peter C.B., 1999. "Asymptotics For Nonlinear Transformations Of Integrated Time Series," Econometric Theory, Cambridge University Press, vol. 15(03), pages 269-298, June.
- Chang, Yoosoon & Isaac Miller, J. & Park, Joon Y., 2009. "Extracting a common stochastic trend: Theory with some applications," Journal of Econometrics, Elsevier, vol. 150(2), pages 231-247, June.
- Max Stevenson, 2001. "Filtering and Forecasting Spot Electricity Prices in the Increasingly Deregulated Australian Electricity Market," Research Paper Series 63, Quantitative Finance Research Centre, University of Technology, Sydney.
- Park, Joon Y., 2002. "Nonstationary nonlinear heteroskedasticity," Journal of Econometrics, Elsevier, vol. 110(2), pages 383-415, October.
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