Hypothesis testing in time series analysis

By P. Whittle. 1954

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Citations in the corpus

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017, methodology): “Although the well-known autoregressive moving average (ARMA) model [Whittle, 1951] and its variants [As-teriou and Hall, 2011; Brockwell and Davis, 2009] have shown their effectiveness for various real world applications, they cannot model nonlinear relationships and do not differentiate among the…”
Hawkes and INAR($\infty$) processes (2015, background): “Standard time series theory for sequences of real-valued data points has been developed in seminal works like Whittle (1951) and Box and Jenkins (1970).”
Identification and frequency domain quasi-maximum likelihood estimation of linearized dynamic stochastic general equilibrium models (2012, unknown): “The FQML approach was first proposed by Whittle (1951).”
Narrow-Band Analysis of Nonstationary Processes (2001, background): “For univariate time series, Gaussian rules of inference lead to consideration of quadratic forms, and Gaussian methods developed by Whittle (1951) and others in stationary short-range dependent environments were extended to unit root nonstationary ones by Box and Jenkins (1971), with limit theory de”
Least-squares estimation for uncertain moving average model (2020, background): “And the other famous time series models can be composed of those two basic models, such as autoregressive moving average model (Whittle 1951), autoregressive integrated moving average model (Box and Jenkins 1970), and autoregressive conditional heteroskedasticity model (Engle 1982).”
Maximum Likelihood Estimation of VARMA Models Using a State‐Space EM Algorithm (2007, methodology): “Whittle (1951) and Durbin (1959) develop approximations for univariate MA models.”
Parameter estimates for fractional autoregressive spatial processes (2005, background): “(1.3) It is assumed throughout this work that all processes considered are Gaussian.”