Beaumont, Paul and Smallwood, Aaron (2019): Conditional Sum of Squares Estimation of Multiple Frequency Long Memory Models.

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Abstract
We review the multiple frequency Gegenbauer autoregressive moving average model, which is able to reproduce a wide range of autocorrelation functions. Extending the result of Chung (1996a), we propose the asymptotic distributions for a conditional sum of squares estimator of the model parameters. The parameters that determine the cycle lengths are asymptotically independent, converging at rate T for finite cycles. This result does not hold generally, most notably for the differencing parameters associated with the cycle lengths. Remaining parameters are typically not independent and converge at the standard rate of T1/2. We present simulation results to explore small sample properties of the estimator, which strongly support most distributional results while also highlighting areas that merit additional exploration. We demonstrate the applicability of the theory and estimator with an application to IBM trading volume.
Item Type:  MPRA Paper 

Original Title:  Conditional Sum of Squares Estimation of Multiple Frequency Long Memory Models 
Language:  English 
Keywords:  kfactor Gegenbauer processes, Asymptotic distributions, ARFIMA, Conditional sum of squares 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C40  General C  Mathematical and Quantitative Methods > C5  Econometric Modeling C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C58  Financial Econometrics G  Financial Economics > G1  General Financial Markets G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing ; Trading Volume ; Bond Interest Rates 
Item ID:  96314 
Depositing User:  Dr Aaron Smallwood 
Date Deposited:  10 Oct 2019 07:25 
Last Modified:  10 Oct 2019 07:25 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/96314 