Spatiotemporal analysis of regional inflation in an emerging country: The case of Indonesia

Aginta, Harry

doi:10.1111/rsp3.12539

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…This could be interpreted in two ways; first, places with high inflation rates are, on average, surrounded by other regions with high inflation rates and vice versa, and second, inflation in the focal region will rise as inflation in neighbouring regions increase, and vice versa. This particular finding supports the evidence of spatiotemporal dependence in Indonesian regional inflation documented by Aginta (2022). A similar finding has been also reported by Yesilyurt and Elhorst (2014) and Nagayasu (2017) for provinces in Turkey and Japan, respectively.…”

Section: Empirical Findings and Discussionsupporting

confidence: 90%

“…Perpendicular bisectors between each nearby point are calculated to create this polygon (more details on the application of the Thiessen polygon are discussed in Anselin et al (2010)). 4 Recent studies have also applied the Thiessen polygon in spatial analysis using regional data of Indonesia (Santos-Marquez et al, 2022;Aginta, 2022) and Thailand (Tipayalai & Mendez, 2022).…”

Section: Contiguity-based On the Constructed Thiessen Polygonmentioning

confidence: 99%

“…(2010)). Recent studies have also applied the Thiessen polygon in spatial analysis using regional data of Indonesia (Santos‐Marquez et al ., 2022; Aginta, 2022) and Thailand (Tipayalai & Mendez, 2022).…”

Section: Econometric Specifications and Datamentioning

confidence: 99%

“…Despite these findings, no study has attempted to use Indonesia's regional data to assess the spatial spillovers of inflation. To date, analyses on regional inflation dynamics in Indonesia focus in testing the Phillips curve (Aginta, 2023), examining the convergence (Ridhwan, 2016;Purwono et al, 2020), and exploring the role of spatial and temporal interaction (Tirtosuharto & Adiwilaga, 2013;Aginta, 2022), with hardly any evidence on spatial spillovers. Indeed, regional development pattern of Indonesia holds promising potential for spatial studies and policy concerns.…”

Section: Introductionmentioning

confidence: 99%

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Inflation and Spatial Spillovers in a Large Archipelago: Evidence from Indonesia*

Aginta

2023

J of App Econs and Policy

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This study examines inflation dynamics in Indonesia, a large archipelagic country, using the Phillips curve specification. Utilising province‐level data from 2015 to 2019, the analysis accounts for regional interaction across Indonesian provinces and estimates spatial spillovers. To address the challenges of applying spatial methods to the world's largest archipelagic nation, two new approaches for the spatial weight matrix criteria are introduced: contiguity‐based from artificial boundaries (Thiessen polygons) and trade‐based from the Interregional Input–Output Tables. Furthermore, exploiting a new dataset, the study contrasts alternative measurements of inflation and output gap. These distinctions yield new findings. The results indicate the presence of a conventional Phillips curve in Indonesia. Different measures of inflation and the output gap have distinct spatial spillover effects.

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Section: Empirical Findings and Discussionsupporting

confidence: 90%