Order Selection and Inference with Long Memory Dependent Data. (22nd May 2019)

• Record Type: Journal Article
• Authors: Gupta, Abhimanyu; Hidalgo, Javier
• Other Names: Nielsen Morten Ørregaard guestEditor.; Hualde Javier guestEditor.
• Is Part Of: Journal of time series analysis. Volume 40:Number 4(2019)
• Page Start: 425
• Page End: 446
• Publication Date: 2019-05-22
• Subjects: Long memory -- spectral decomposition -- BIC -- HIC; Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12476
• Languages: English
• ISSNs: 0143-9782

Abstract

Abstract : In empirical studies selection of the order of a model is routinely invoked. A common example is the order selection of an autoregressive model via Akaike's AIC, Schwarz's BIC or Hannan and Quinn's HIC. The criteria are based on the conditional sum of squares (CSS). However, the computation of the CSS might be difficult for some models such as Bloomfield's exponential model and/or when we allow for long memory dependence. The main aim of the article is thus to propose an alternative way to compute the criterion by using the decomposition of the variance of the innovation errors in terms of its frequency components. We show its validity to obtain the correct order the model. In addition, as a by‐product, we describe a simple (two‐step) estimator of the parameters of the model.