1. Nowcasting of Gross Regional Product and Analyzing Regional Business Cycle Nariyasu Yamasawa

2. Introduction Which data and method is relevant to measure Regional business cycle? How similar are the prefectures business cycle? To what extent have prefecture' recession and expansion experiences been in sync each other ? what might explain the differences in business cycle ?

3. 1.Data 2.Methodology 3.Emprical Result 4.Conclusion My presentation has 4 parts.

4. 1.Data Problem in Japan AnnualQuarterly, Monthly NationalGDPComposite Index, GDP RegionalGRP 2years lag Composite Index, Industrial production Monthly GRP GDP and GRP(Gross Regional Product) is the best for analyzing business cycle. For the analysis of the regional business cycle, the key is the choice of the data which represent the business cycle.

5. Nowcasting of GRP Present Official GRP TIME Real Monthly GRP Past 2 years lag 90 days Lag

6. The Japanese Cabinet Office has been releasing data on most of the GRP components in the form of a monthly index called the Regional Domestic Expenditure Index (RDEI) since May 2012,. We estimated the rest of components, that is Government Consumption, Net export. By summing up the expenditure components, we could estimate the monthly GRP. How to estimate monthly GRP?

7. ItemMethodology for estimation Private consumptionDivided by 44 types of consumption Private residential investment “Statistics of Construction Starts of Residential Properties” Private fixed investment Estimated by building, construction, machinery, aircraft, motor vehicles, and other transportation machinery Public investment “Statistics of Construction Order by 47 Prefectures” Government Consumption Estimated by Author. Net ExportEstimated by Author RDEIRDEI

8. Real Monthly GRP for 47 prefectures (Note) The data Start from April 2002

9. 2.Methodology How do we measure business cycle? Band Pass filter (Baxter and King Filter) – Can remove noise and trend – Considered Business Cycle Frequency 18months – 96 months – Weak point : Baxter King filter cannot analyze present situation Regime Switching model(Hamilton(1989)) – Suppose mean growth rate switches between high- and low- growth regimes. – Probability of recession – Apply spatial analysis with weights matrix W We tried to extract business cycle by two types of methodology.

10. Band Pass Filter

11. Level dataAfter filtering

12. Regime Switching Model We use regime switching model which enable us to identify recession period and expansion period. It suppose the series can divided by two regimes, that is, the high growth rate and low growth rate regime.

13. Regime Switching model Growth RateRecession Probability

14. Spatial Model

15. Spatial Contiguity Weights Matrix Indicate whether prefectures share a boundary or not. Neighbors = 1 Then, calculate the share.

16. 3. Estimation Result Analyze Mainly Great Recession 2008-2009

17. Result (Band Pass Filter) Tokyo

18. Recession Period(Filter) PREFECTURESPREFECTURES

19. Result(Band Pass filter) Almost all the data have the same behavior. Tokyo is not the same. The peak is 10 months earlier than the official peak(Feb. 2008). Hokkaido, Aomori, Kanagawa went in recession earlier. Osaka, Hyogo, Aichi went in recession later.

20. Result of Regime Switching Model

21. Recession Period(Regime Switching) PREFECTURESPREFECTURES

22. Recession(Regime Switching) Prefectures in blue are in recession. Hokkaido, Kagoshima, Yamaguchi, Hiroshima,Niigata, Shizuoka is earlier. Prefectures which are far from Tokyo tend to go recession early. Tokyo

23. It took 4 months for almost all prefectures to be in recession.

24. Spatial Model

25. Estimation of ρ Ｐｒｅｆｅｃｔｕｒｅｓ ρ is positive and significant.

26. What might explain the differences in business cycle ? Dependent Variables = Beginning date of Recession – Date is number of days from 1900/1/1 – e.g.Feb.2008 is “39479” Explanation Variables various kind of data Cross Section Regression,Year of 2008

27. Explanation variables POP Population(person) OLD Population of Old age(%of Total) PERCAPITA Gross Regional Income Per capita(Yen) HIGH High School Enrolment(%) UNIV University Students(% of Total Population) GDPAGRI Agriculture (% of GRP) GDPMANU Manufacturing(% of GRP) GDPCONST Construction(% of GRP) GDPGOV Government Service(% of GRP) RIPUB Public Construction(% of GRP) LEND Lending(% of GRP) TK Tokyo Dummy KG Kanagawa Dummy

28. Regression Result

29. What affects the business cycle? Frequency FilterMarkov Switching Model 1 Share of Manufacturing Industry(%of GRP) Population Share of Old age (%) 2 GRP Per Capita(Yen)GRP per Capita(yen) 3 Share of Construction Industry(% of GRP) High school enrollment (%)

30. Conclusion Which data is relevant to measure Regional business cycle. Monthly GRP How similar are the prefectures business cycle? Recession date is different, the range is about 4months To what extent have prefecture' recession and expansion experiences been in sync with each other's ? Neighborhood Effect exists what might explain the differences in business cycle ? GRP per capita etc.

31. Reference Michael T. Owyang, Jeremy Piger and Howard J. Wall Source(2005)” Business Cycle Phases in U.S. States” The Review of Economics and Statistics,Vol. 87, No. 4 (Nov., 2005), pp. 604-616 Hamilton, J. D., (1989)"A New Approach to the Economic Analysis of Nonsta- tionary Time Series and the Business Cycle," Econometrica 57:2 (1989), 357-384.

32. Earlier prefecture is darker

33. Recovery (Regime Switching) Prefectures in Red are in recession. Kagoshima, Yamaguchi, Osaka, Saitama, Chiba recovered earlier. Prefectures which are near Tokyo and Osaka recovered earlier.

34. Spatial Weight Matrix Based on Distance – Nearest Neighbor Weights – Radial Distance Weights – Power Distance Weights – Exponential Distance Weights Based on Boundaries – Spatial Contiguity Weights – Shared-Boundary Weights Stakhovych and Bijmolt (2009)

35. Band Pass Filter Regime Switching Model DataLevelGrowth rate PeakMaxEnd point of High growth regime BottomMinEnd point of Low growth regime Data Limitation Need 18month lag and lead Influenced outlier

36. Regime Switching Model Markov Chain : Any persistence in the regime is completely summarized by the value of the state in the last period.