Statistical Inference for Nonstationary Processes

Jan Beran, Yuanhua Feng, Sucharita Ghosh and Rafal Kulik
Additional contact information
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

Chapter Chapter 7 in Long-Memory Processes, 2013, pp 555-732 from Springer

Abstract: 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.

Keywords: Best Linear Unbiased Estimator (BLUE); Local Polynomial Estimator; Boundary Kernels; Fractional ARIMA (FARIMA); Long-memory Parameter (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-35512-7_7

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