Giulio Cainelli , Claudio Lupi (p), Myriam Tabasso

In this paper we investigate the extent and the economic determinants of business cycle phase synchronization among the US states.
Consistently with the existing literature (see, e.g., Carlino and Sill, 2001) we examine the behaviour of quarterly per capita real income dynamics in 49 US states (including the Federal District of Columbia and excluding Hawaii and Alaska) over the period 1948 – 2014. In order to compute per capita income, we estimate quarterly population by temporally disaggregating each state mid-year population using the Denton-Cholette approach (see, e.g., Dagum and Cholette, 2006). Because regional price indexes are not available, in order to deflate nominal per capita income we use the national consumer price index. Finally, we estimate the cyclical component of each regional per capita real income series by using the approximate band-pass filter proposed in Baxter and King (1999). Based on these results, we compute the pairwise synchronization indexes over all the possible couples of estimated cyclical components among the 49 US States following the lines suggested in Meller and Metiu (2017). The resulting 49 x 49 matrix is our main object of investigation and it is used to evaluate the evolution of business cycle synchronization and to assess the existence of spatial correlation among the synchronization indexes over the whole period 1948 – 2014. Finally, in order to identify the economic determinants of synchronization, we focus on the recent years and we use the estimated synchronization indices as dependent variables in modified spatial gravity models (Le Sage and Pace, 2008). We build these spatial models using census-based covariates measuring phenomena such as employment density, production similarities, similarities in manufacturing specialization, and agglomeration effects.
The contribution of the paper is fourfold: first, the existence, the extent, and the variability of regional business cycle synchronization are assessed over a long time span. Second, contrary to the prevalent practice, phase synchronization is not considered with reference to a single national reference series. Third, the existence of spatial spillover effects on business cycle synchronization is investigated. Fourth, the regional economic determinants of phase synchronization are studied by using modified spatial gravity models.

Nooshin Mahmoodi , Roberto Patuelli (p)

The debate on the choice of the ideal spatial weights matrix (W) is long-standing in applied spatial statistics and econometrics. While some authors point out aspects related to possible misspecification from the choice of the wrong W, others suggest that there is little difference in practical and interpretation terms at least between geography-based spatial weights matrices. A particular difficulty in defining W may therefore emerge especially when it may imply spillovers based not only on geography, but also on socio-economic variables (for instance, differences in the endowment of amenities or industrial activity). Even when guessing correctly the factors behind W, defining the way they influence spillovers may be particularly difficult. To this aim, we propose an approach to defining W based on fuzzy logic, which allows to deal in a flexible way with its numerical specification. We set up Monte Carlo simulations to evaluate how a fuzzy W can approximate different types of spatial weights matrices, and in settings with spatial dependence of different strength, with particular reference to the estimation of the regression coefficients.

Kateryna Zabarina (p)

Firms and real estate remain interesting not only in the field of economics, but also for finance, statistics, geography, urban planning and design. Though these formations are different in terms of origin, one can easily observe that in terms of location decision they have to solve the same problem. Moreover, in both cases optimization problem also should be solved – minimum price of real estate and maximum firm profit. However, similarity doesn’t hold when talking about methodology – most studies about firm location use simple regressions, as long as usage of spatial methods and machine learning techniques in real estate studies has increased (Krause, Bitter, 2012). This paper describes briefly similarities and differences in firms and real estate location determinants and methods.