Time Series Modelling and Interpretation

By Clive W. J. Granger, M. J. Morris. 1976

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Generative Speech Coding with Predictive Variance Regularization (2021, background): “It is well-known that the sum of ﬁnite-order linear autoregressive models is, in general, not a ﬁnite-order autoregressive model [13].”
Forecasting Euro Area Inflation: Does Aggregating Forecasts by Hicp Component Improve Forecast Accuracy? (2003, unknown): “Therefore, Lütkepohl (1984a, 1987) presents Monte Carlo simulations to analyse the relative small sample accuracy in terms of the MSFE of directly forecasting the aggregate and aggregating subcompo- 11Some related issues and results are presented in a paper by Granger & Morris (1976).”
Robust Low Rate Speech Coding Based on Cloned Networks and Wavenet (2020, background): “We note that this phenomenon is well-known in linear modeling: the sum of two signals generated by linear autoregressive systems cannot be modeled efﬁciently with an autoregressive model [2].”
Autoregressive Stochastic Volatility Models with Heavy-tailed Distributions: A Comparison with Multifactor Volatility Models (2008, background): “This result comes from Granger and Morris (1976); the sum of two independent AR processes, AR(p1) and AR(p2), is expressed as ARMA(p, q), where p ≤ p1+p2 and q ≤ max(p1, p2).”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, unknown): “We refer the reader to Granger and Morris (1976), Lütkepohl (1984, 1987), Granger (1987, 1990), among others, for a thorough discussion.”
Comparison of Models for the Analysis of Intensive Longitudinal Data (2019, background): “1 , plus a white noise process, represented by the term (cid:2) t , equals another MA(1) process, see Granger and Morris (1976).”
Dynamic Structural Equation Models (2018, methodology): “It should be noted, however, that there are many other possibilities, such as the AR(2) model, the more general ARMA(p,q) model, or a MEAR(p) model, which is a special case of the ARMA(p,p) model (see Granger & Morris, 1976).”
Knowledge, Tests, and Fadeout in Educational Interventions (2012, unknown): “Using results from Granger and Morris (1976), this model is equivalent to an ARMA(1,1) with AR parameter .936 and MA parameter between -.29 and -.38.”
Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data. (2006, methodology): “4 Partition Matrix Improvement For three blocks of time series xt, yt, zt, we can fit a three-variable MVAR model as in Eq.(14) and we can also derive its frequency domain expression as in Eq.”