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Statistical Inference for Nonstationary Processes

In: Long-Memory Processes

Author

Listed:
  • Jan Beran

    (University of Konstanz, Dept. of Mathematics and Statistics)

  • Yuanhua Feng

    (University of Paderborn, Faculty of Business Administration and Economics)

  • Sucharita Ghosh

    (Swiss Federal Research Institute WSL)

  • Rafal Kulik

    (University of Ottawa, Dept. of Mathematics and Statistics)

Abstract

In this chapter, statistical inference for nonstationary processes is discussed. For long-memory, or, more generally, fractional stochastic processes this is of particular interest because long-range dependence often generates sample paths that mimic certain features of nonstationarity. It is therefore often not easy to distinguish between stationary long-memory behaviour and nonstationary structures. For statistical inference, including estimation, testing and forecasting, the distinction between stationary and nonstationary, as well as between stochastic and deterministic components, is essential.

Suggested Citation

  • Jan Beran & Yuanhua Feng & Sucharita Ghosh & Rafal Kulik, 2013. "Statistical Inference for Nonstationary Processes," Springer Books, in: Long-Memory Processes, edition 127, chapter 0, pages 555-732, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-35512-7_7
    DOI: 10.1007/978-3-642-35512-7_7
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