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The generalized STAR(1,1) modeling with time correlated errors to red-chili weekly prices of some traditional markets in Bandung, West Java
Fadlilah F.I.N.a, Mukhaiyar U.a, Fahmi F.a
a Statistics Research Division, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 AIP Publishing LLC.The observations at a certain location may be linearly influenced by the previous times of observations at that location and neighbor locations, which could be analyzed by Generalized STAR(1,1). In this paper, the weekly red-chili prices secondary-data of five main traditional markets in Bandung are used as case study. The purpose of GSTAR(1,1) model is to forecast the next time red-chili prices at those markets. The model is identified by sample space-time ACF and space-time PACF, and model parameters are estimated by least square estimation method. Theoretically, the errors’ independency assumption could simplify the parameter estimation’s problem. However, practically that assumption is hard to satisfy since the errors may be correlated each other’s. In red-chili prices modeling, it is considered that the process has time-correlated errors, i.e. martingale difference process, instead of follow normal distribution. Here, we do some simulations to investigate the behavior of errors’ assumptions. Although some of results show that the behavior of the errors’ model are not always followed the martingale difference process, it does not corrupt the goodness of GSTAR(1,1) model to forecast the red-chili prices at those five markets.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]autoregressive,Least square estimation,martingale difference process,STACF-STPACF,time correlated errors[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1063/1.4936442[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]