1. Asymptotic distribution of least squares estimators for linear models with dependent errors : regular designs
- Author
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Sophie Dede, Emmanuel Caron, Laboratoire de Mathématiques Jean Leray (LMJL), Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN), École Centrale de Nantes (ECN), and Lycée Stanislas, Paris
- Subjects
Statistics and Probability ,Independent and identically distributed random variables ,FOS: Computer and information sciences ,Asymptotic distribution ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,Statistics - Applications ,01 natural sciences ,Least squares ,spectral density ,62F12, 62J05, 62M10, 62M15 ,010104 statistics & probability ,short memory processes ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,Linear regression ,FOS: Mathematics ,Applied mathematics ,Applications (stat.AP) ,0101 mathematics ,Mathematics ,Central limit theorem ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Covariance matrix ,010102 general mathematics ,Probability (math.PR) ,Linear model ,Estimator ,least squares estimator ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,linear model ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,Statistics, Probability and Uncertainty ,Mathematics - Probability - Abstract
In this paper, we consider the usual linear regression model in the case where the error process is assumed strictly stationary. We use a result from Hannan, who proved a Central Limit Theorem for the usual least squares estimator under general conditions on the design and on the error process. We show that for a large class of designs, the asymptotic covariance matrix is as simple as the independent and identically distributed case. We then estimate the covariance matrix using an estimator of the spectral density whose consistency is proved under very mild conditions., Comment: 31 pages
- Published
- 2017
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