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Spatial Dependence in Regional Business Cycles: Evidence from Mexican States

Keisuke Kondo

No DP2015-27, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: This study investigates how regional business cycles are spatially dependent in Mexico by developing a Markov switching model with a spatial autoregressive process. The Markov switching model with two regimes distinguishes business cycles between expansion and recession phases (i.e., high -and low-growth rate regimes). The objective of this study is twofold. First, this study aims to identify which states transitioned from expansion to recession during the Great Recession in 2008–2009. Second, it numerically examines the extent to which states that experienced this transition caused a deterioration in neighboring states' economies. Employing Bayesian inference for the Markov switching model with quarterly data of state economic activity during the period 2003:Q1–2015:Q4, this study finds that Mexican states with higher manufacturing sector shares tended to be in recession during the Great Recession. Although some states experienced economic downturns in this period, they were not in a recessionary regime. is study also finds that business cycles across states were spatially dependent during the Great Recession. The numerical simulations of spatial spillover effects suggest that states that regime switched from expansion to recession during the Great Recession caused a reduction in the quarterly growth rate of their nearest neighboring economies by an average of 0.26 percentage points.

Keywords: Spatial dependence; Spatial spillover effects; Regional business cycles; Markov switching model; Markov chain monte carlo (search for similar items in EconPapers)
JEL-codes: C33 E32 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2015-07, Revised 2021-06
New Economics Papers: this item is included in nep-geo, nep-mac, nep-ore and nep-ure
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Journal Article: Spatial dependence in regional business cycles: evidence from Mexican states (2022) Downloads
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