A semiparametric spatial FARIMA applied in the presence of spatial seasonality

Dominik Schulz (), Thi Thu Huong Do () and Yuanhua Feng ()
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Dominik Schulz: Paderborn University
Thi Thu Huong Do: Technische Universität Ilmenau
Yuanhua Feng: Paderborn University

No 170, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: A semiparametric spatial long-memory time series model, called Semi-SFARIMA, in the presence of a seasonality surface and random intraday eects is introduced and dened. Initially, a local polynomial double conditional smoothing (LP-DCS) tech- nique is employed to smooth the seasonal surface in the nonparametric part. Subse- quently, intraday curves are identified using automated local polynomial smoothing in a one-dimensional context. Ultimately, residual series obtained from the preced- ing steps undergo modeling using a (parametric) spatial FARIMA framework. The statistical significance of the estimated spatial FARIMA parameters are assessed through asymptotically valid standard errors. The estimation method is illustrated by in-depth analysis of air temperature from two distinct weather stations, Yuma and Murphy. Since the example series include missing values, an imputation method for univariate time series is considered as a preliminary treatment during the data collection and processing. The proposed ideas prove to be useful in practice when being confronted with spatial time series with two-directional seasonality patterns.

Keywords: Spatial time series; Semi-SFARIMA; seasonality surface; imputation (search for similar items in EconPapers)
JEL-codes: C14 C23 C51 (search for similar items in EconPapers)
Pages: 35
Date: 2026-03
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