Aggregation of Space-Time Processes

By R. Giacomini, Clive W. J. Granger. 2001

186 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7939178608827025d927050f7bae4ff5b101f8d4

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Citations in the corpus

Estimation of simultaneous systems of spatially interrelated cross sectional equations (2004, background): “Proof of Theorem 4:Analogous to the proof of Theorem 3 observe that substitution of ( 21 ), with ρj replaced by e ρj,n, into (23) yields”
Space-time modeling of traffic flow (2002, methodology): “Since then it has been applied to spatial time series data a wide variety of disciplines such as river flow (Pfeifer and Deutsch, 1981a), spread of disease (Pfeifer and Deutsch, 1980a), and spatial econometrics (Elhorst, 2001; Giacomini and Granger, 2004).”
Forecasting the Path of China's CO2 Emissions Using Province Level Information (2007, methodology): “We will extend the restrictive second order polynomial specification first proposed by Grossman and Krueger (1995) by allowing for a flexible functional form of the pollution income relationship using the semi-parametric Generalized Additive Model (GAM) framework (Hastie and Tibshirani, 1990).”
Forecasting with Panel Data (2008, background): “Another related paper is Giacomini and Granger (2004) who compare the relative e¢ ciency of di¤erent methods of forecasting the aggregate of spatially correlated variables.”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, unknown): “Spatial aggregation has been studied by Giacomini and Granger (2004) and factor models by Forni et al. (2000).”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, background): “A relevant example work is that of Giacomini and Granger (2004), which looks at the problem of pooling and forecasting spatially correlated data sets.”
Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches (2003, methodology): “Since then, it has been used for the analysis of spatial time series data coming from various disciplines such as environmetrics (13, 15, 16), epidemiology (12), and spatial econometrics (17, 18).”
Estimation of the Spatial Weights Matrix under Structural Constraints (2013, background): “While the literature contains an implicit acknowledgment of these issues, most empirical studies treat spatial dependence in a super cial manner assuming in exible di¤usion processes in terms of a priori xed spatial weights matrices (Giacomini and Granger, 2004).”
Predicting User-Cell Association in Cellular Networks from Tracked Data (2009, unknown): “Giacomini and Granger investigate forecasting of a Space-Time Autoregressive model aggregate [14].”