Sparse Bayesian State-Space and Time-Varying Parameter Models

2021 ◽  
pp. 297-326
Author(s):  
Frühwirth-Schnatter Sylvia ◽  
Peter Knaus
Keyword(s):  
State Space ◽  
Time Varying ◽  
Space And Time ◽  
Time Varying Parameter

Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions

2019 ◽  
Vol 82 ◽  
pp. 26-41 ◽  
Author(s):  
Aynur Alptekin ◽  
David C. Broadstock ◽  
Xiaoqi Chen ◽  
Dong Wang
Keyword(s):  
State Space ◽  
Energy Demand ◽  
Time Varying ◽  
Demand Functions ◽  
State Space Methods ◽  
Time Varying Parameter

scholarly journals Mustard Yield Prediction using State Space Models

2020 ◽  
pp. 483-494
Author(s):  
Ekta Hooda ◽  
B. K. Hooda
Keyword(s):  
State Space ◽  
State Space Models ◽  
Yield Prediction ◽  
Time Varying ◽  
Polynomial Splines ◽  
Growth Trend ◽  
Time Varying Parameter ◽  
Mustard Crop ◽  
Develop State ◽  
Over Time

Forecasting of agricultural outputs well in advance has always been the focus of numerous researchers due to its direct implications on various areas of the society. This study aims to develop State Space Models (SSMs) with weather as an exogenous input over the commonly used ARIMA and regression analysis for yield prediction for mustard crop in eight districts of Haryana state in India. These models are time-varying parameter models and take into account for changes that are known over time in structure of the framework. SSMs with various kinds of growth trends were tried and model performances were compared using AIC and BIC criteria but the growth trend represented by polynomial splines of order-2 with the weather as an exogenous input was chosen as the most appropriate model for mustard yield prediction in all the eight districts under study. Based on the developed models, post-sample yield predictions for the next three years, i.e. 2016-17 to 2018-19 have been obtained and the deviations from actual values are also calculated which came out to be acceptable in an agricultural setup.

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