Regional Business Cycles in Canada: A Regime-Switching VAR Approach

This study examines the consistency and gaps in national and regional business cycles in Japan from a Bayesian point of view. The Tokyo monopolar system started in the mid‐1970s, and recent descriptive statistics, such as migration and per capita income, show that the system continues, despite severe crises such as the burst of the 1990s economic bubble and the Lehman Brothers bankruptcy. We explore the relationship between national and regional business cycles in the system using a spatio‐temporal Markov‐switching model with the Markov chain Monte Carlo method. Our empirical results show that overall, the regional business cycle in the Kanto region, including Tokyo, is identical to the national business cycle. Moreover, we find that switches in the degree of spatial dependency occur around the turning points of business cycles, and that the degree of spatial dependency tends to be higher during a recession.

구는 FAVAR 모형을 이용하여 지역별 아파트 경기지수를 작성하고, 아파트 경기지 수의 적정성을 검증하고, 작성된 아파트 경기지수를 이용하여 지역별 아파트 경기의 전이 효과를 분석하였다. 실증분석결과, FAVAR 모형을 이용하여 작성한 전국아파트 경기지수와 통계청에서 발표 하는 경기동행지수 순환변동과 유사한 움직임을 보이며, 특히 2008년 금융위기와 그 이후의 경제 충격을 전국 아파트 경기지수가 잘 트레이싱(tracing)하는 것으로 분석되었으며, 전국 아파트 경기지수는 경기동행 순환변동을 약 4-5개월 정도 선행하는 것으로 나타났다. 지역별 아파트 경기지수 분석결과, 2008년 세계금융위기의 영향을 공통적으로 받은 것을 확인되었지만, 금융위기 이후에는 지역별 아파트 경기지수가 차이를 보이는 것으로 분석되었다. 이러한 결과는 중앙정부에서 시행한 부동산 정책에 대한 효과가 지역별로 다르게 나타날 수 있다는 것이며, 이는 부동산 정책을 시행할 때, 지역별 특성과 영향을 고려해야 함을 시사하는 것이다. 지역별 경기지수 전이효과를 종합하면, 아파트 경기지수는 서울과 수도권이 상호 영향을 주고 받으며, 부산에도 상당한 영향을 미치는 것을 확인할 수 있지만, 부산에서 서울과 수도권에 미치는 영향은 상대적으로 미미하다고 할 수 있다.We used the FAVAR model in order to estimate the regional apartment business cycles. We also analyzed the regional spillover effects by using the methodology suggested by Diebold and Yilmaz (2009, 2012). Based on this analysis, a similarity was found between the national apartment business cycle and the coincident composite index. In addition, the national apartment business cycle can be traced back to the 2008 financial crisis and subsequent economic shocks. The national apartment business cycle also led the coincident composite index by about 4-5 months. The analysis of the regional apartment business cycles showed that the regional apartment business cycles were commonly affected by the 2008 global financial crisis. However, the regional apartment business cycles varied after the financial crisis. These results indicate that the real estate policies of the government should be differentiated by region. Moreover, there is a difference between the economic environment of each region. We conclude that when implementing real estate policies, regional characteristics and impacts need to be considered. The analysis result of the transition effect of regional economic indexes showed that the apartment business cycles have a significant influence on both Busan and Seoul. However, Busan has a relatively small impact on Seoul and the metropolitan area.We estimate that the variation in Seoul’s apartment business cycle is only about 0.291 due to the change within Seoul, and that 0.709 of the transition effect is explained by the change in the other regions. Meanwhile, the transfer effect of Incheon to the other regions was 0.819.

Understanding the Lead/Lag Structure Among Regional Business Cycles

Home Bias and the Structure of International and Regional Business Cycles

Understanding U.S. Regional Cyclical Comovement: How Important are Spillovers and Common Shocks?

In previous studies of regional business cycles in Japan, critical differences in the amplitudes and the turning points in business cycles by region were revealed. However, there is a problem in the previous studies; they relied on one series, typically an index of industrial production in manufacturing sectors, hence, it is necessary to include information on sectors other than manufacturing to provide a more complete measure of the business conditions of a region. Specifically, we extract a regional business index from four business indicators using the principal components and applied the regime switching model to identify the turning points in regional business cycles. Our result shows that the sector that generates the greatest influence on the business cycles differs by region. Furthermore, different regions have different features also from the viewpoint of the turning points of business cycles.

Regional Business Cycles in Italy

Advanced Economies and Emerging Markets: Dissecting the Drivers of Business Cycle Synchronization

In a national economy, are individual subnational regions business cycle takers or setters?We address this important regional policy question by investigating regional business cycles at NUTS-3 granularity in Poland (N = 73), using two metrics in parallel: GDP dynamics and unemployment.To extract the business cycle, we use a spatial Markov switching model that features both idiosyncratic business cycle fluctuations across regions (as a 2-state chain), as well as spatial interactions with other regions (as spatial autoregression).The posterior distribution of the parameters is simulated with a Metropolis-within-Gibbs procedure.We find a clear division into business cycle setters and takers, the latter being largely (but not only) non-metropolitan regions.

Advanced economies and emerging markets: Dissecting the drivers of business cycle synchronization

I. IntroductionEconomic integration in East Asia has progressed with the aid of increasing international trade and financial flows. The size of ASEAN+31 economy has been taking an ever-greater share of the world economy, as shown in Table 1. China has emerged as a major economic partner of both East Asian countries and the rest of the world. This tendency of increasing economic integration is likely to lead regional business cycle synchronization in East Asia.Trade integration in East Asia has actively proceeded over the last two decades through the ASEAN Free Trade Area and the ASEAN Investment Area. These efforts contributed to lowering transaction costs of regional trade. Moreover, strong tendency of regional trade integration is supported by bilateral and plurilateral free trade agreements. For example, ASEAN+3 countries have concluded 131 FTAs and are currently negotiating 53 FTAs, as of 2012.Regional financial cooperation has been developed in East Asia under the ASEAN+3 initiatives since the Asian currency crisis. It has manifested in various aspects, including gradual financial liberalization, increasing cross-border capital flows, establishing regional financial safety nets, and developing local currency bond markets.2 The issue of regional business cycle synchronization is also relevant to the regional monetary cooperation in East Asia. This is, in particular, important for the optimum currency area because the degree of regional business cycle synchronization is understood as one of the economic convergence criteria. In this regard, recent studies have examined the business cycle comovement between countries in Asia in order to assess the desirability of a regional currency union.Empirical literature provides a general tendency of regional business cycle synchronization in East Asia. Selover (1999) uses principal component analysis to find evidence for a shared business cycle among ASEAN countries. However VAR estimations reveal weak evidence of business cycle transmission among ASEAN countries. Selover (2004) examines the economic links between Korea and Japan and finds evidence of moderate synchronization in economic activity. Rana (2007) extends Shin and Wang (2003) to find that intra-industry trade is the major factor explaining business cycle comovement in East Asia.For emerging and developing economies as in Calderon et al. (2007), the empirical evidence of business cycle synchronization appears to be more negligible with mixed results. Moneta and Ruffer (2009) examine business cycle synchronization in East Asia by estimating a factor model through Kalman filtering. Their finding is that Asian countries, except China and Japan, share a common factor of business cycle that appears to reflect export synchronization. The existing studies generally find a positive relationship between trade linkages and synchronization for the Asian region, in line with the relatively high share of intra-industry trade within the region (e.g. Choe, 2001; Shin and Sohn, 2006; and Rana, 2007). In contrast, Kumakura (2006) finds that similarities in the production structure are much more important explanatory variable for bilateral growth synchronization than bilateral trade links. Imbs (2011) examines bilateral business cycle correlations within emerging East Asia. He finds evident business cycle synchronization after the global financial crisis of 2008:Q3, but it is substantially more pronounced amongst developed countries than in emerging East Asia. Nguyen (2007) investigates the determinants of East Asian business cycle synchronization, covering the period 1970-2000, by using an OLS-based extreme bound analysis, and finds that trade openness, intra-industry trade, and the similarity of monetary policy are major channels of business cycle synchronization. Allegret and Essaadi (2011) analyze the feasibility of a monetary union in East Asia focusing on business cycles synchronization. They find that the increase in bilateral trade in East Asia improves long-run business cycle synchronization. …

The Korean government is implementing regionally segmented housing market regulation. To identify the impact of the regulation on specific regions, research on regional ripple effects in housing market should be preceded. We analyze the ripple effects by using regional housing price business cycles. While most of previous studies in Korea used Hodrick-Prescott(HP) filter to extract cyclical components, we use Christiano-Fitzgerald(CF) filter instead. Cross correlation analysis suggests that the capital area’s housing price business cycle precedes the housing price business cycles of 5 metropolitan cities and other provinces. Granger causality test supports this result, and also suggests that all the regional housing price business cycles of the three regions Granger-cause one another. According to VAR analysis, however, 5 metropolitan cities and other provinces do not cause each other but drift independently. We try to explain these relations among regional housing price business cycles by means of housing demand and supply, GDP and housing mortgage. The result of regression analysis on regional housing price business cycles and the business cycles of other variables shows that there is hierarchical sensitivity to GDP and housing mortgage balance among those regions. Therefore, we narrowly conclude that the ripple effects on the three regions might exist not just because of market expectation, but because of regional differences in sensitivity to market fundamentals.

This paper examines the evolution and the underlying forces of the business cycle co‐movements among seven African (A‐7) countries over the period 1970–2016. These countries accounted for over 60% of regional GDP, have abundant natural resources, access to the global capital market and widely viewed as potential drivers of regional (or “African”) business cycle. We model the national business cycle using real output, consumption and investment. We employ a dynamic factor model to decompose fluctuations in these macro‐variables into a regional, country‐specific and idiosyncratic components using Bayesian methods. We also analyse the relative importance of some notable drivers of business cycle fluctuations found in the literature. We find that the idiosyncratic component is dominating cyclical fluctuations in the A‐7 countries, while country‐specific and regional factors play a negligible role suggesting the inexistence of a common regional cycle despite deepening intra‐African trade. Among the driving variables, the terms‐of‐trade shocks exert greater influence on the A‐7 business cycle, while exchange rate movements and changes in money supply explain sizeable fluctuations in consumption and investment in most of the A‐7 countries. Shocks associated with changes in relative domestic oil prices, monetary and fiscal policies cause large output fluctuations.

Regional Business Cycles Across Europe

Global and Regional Business Cycles - Shocks and Propagations