Workshop on
spatial and spatio-temporal
data analysis

Program and Abstracts

November 10, 2017
Tohoku University

Program

November 10, 2017
10:00-10:50

Jaromir Antoch,: Structural breaks in panel data
Large number of panels and short length timeseries

10:50-11:40

Dieter Wang: Estimating Dynamic Spillover
Effects with Spatial State-Space Models

11:40-13:00

Lunch

13:00-13:30

Navruzbek Karamatov: Spatial CAPM andSpatial model for international stock markets

13:30-14:00

Takashi Matsuki: Linear and nonlinear
comovement in Southeast Asian local currency
bond markets: a stepwise multiple testingapproach

14:00 14:15

Coffee break

14:15-14:45

Naoki Awaya: Particle rolling MCMC withdouble block sampling: conditional SMC update
approach

14:45-15:15

Takaki Sato: Spatio-temporal autoregressive
conditional heteroscedasticity model

15:15-15:30

Coffee break

15:30-16:00

Yoko Konishi: Measuring the Value of Time in
Freight Transportation

16:00-16:30

Daisuke Murakami: Moran coefficient
basedmixed effects approach to investigate
spatially varying relationships

18:00-Banquet at Restaurant Hagi

Structural breaks in panel data
Large number of panels and short length time series
Jaromı́r Antoch1
Charles University, Dept. of Probability and Mathematical Statistics, Sokolovská 83, CZ-186 75 Praha,
Czech Republic; antoch@karlin.mff.cuni.cz
Abstract: The detection of the (structural) break or so called change point problem has drawn increasing
attention from both theoretical and applied economic and financial research over the last decade. A largepart of the existing research concentrates on the detection and asymptotic properties of the change point
problem for panels with a large time dimension T . In this lecture we will present a different approach,i.e., we will consider the asymptotic properties with respect to N (number of panel members) while
keeping T fixed. This situation (N → ∞ but T being fixed and rather small) is typically related to thelarge data sets containing information about an enormous number of units across a limited number oftime moments (years/quarters/months).
It is worth of noticing that in recent years also the spatial econometrics literature has exhibited a
growing interest in the specification and estimation of econometric relationships based on spatial panels,
typically referring to data containing time series observations of a number of spatial units (municipalities,regions, states, jurisdictions, etc.). This interest can be explained by the fact that the spatial panel dataoffer an extended modeling possibilities as compared to the single equation cross-sectional setting, which
was the primary focus of the spatial econometrics literature for a long time, and allow for the specificationof more complicated behavioral hypotheses, including effects that cannot be addressed using pure crosssectional approach.
In our contribution a general approach for testing for the break(s) in considered setup, which also
allows their detection, will be presented. The asymptotic behavior of the test statistics, along with analternative wild bootstrap procedure that can be used to generate the critical values of the test statistics,
will be shown. In the practical application will be demonstrated the testing procedure in the frameworkof the four factors CAPM model. In particular, we will concentrate on eventual breaks in monthly returnsof the US mutual funds during the period January 2006 to February 2010, covering the subprime crises.
Key words: Change point problem; stationarity; panel data; bootstrap; four factor CAPM model; spatial
econometrics; US mutual funds.
References[1] Antoch, J., Hanousek, J., Horváth, L., Hušková, M., and Wang, S. Structural breaks in panel data: Large number ofpanels and short length time series. The Centre for Economic Policy Research, March 2017.
http://cepr.org/active/publications/discussion papers/dp.php?dpno=11891[2] Arellano, M. Panel Data Econometrics. Oxford University Press, Oxford, 2004.
[3] Bai, J. Common breaks in means and variances for panel data. J. of Econometrics 157, 78–92, 2010.
[4] Baltagi, B.H. Econometric analysis of panel data (3rd ed.). J. Wiley, New York, 2005.
[5] Baltagi, B.H., Kao, C., and Liu, L. Estimation and identification of change points in panel models with nonstationaryor stationary regressors and error terms. J. of Econometrics 191, 176–195, 2016.
[6] Elhorst, J.P. Specification and estimation of spatial panel data models. International Regional Science Review 26,244-268, 2003.
[7] Elhorst, J.P. Spatial panel data models. In: Handbook of Applied Spatial Analysis, Eds. Fischer M.M. and Getis A.
377–407, Springer, Berlin, 2010.
[8] Fama, E.F., and French, K.R., A five-factor asset pricing model. Fama-Miller Working Paper, September 2014. Available at http://dx.doi.org/10.2139/ssrn.2287202[9] Fama, E.F., and Kenneth, R.F. Common risk factors in the returns on stocks and bonds. J. of Financial Economics
33, 3–56, 1993.
[10] Horváth, L., and Hušková, M. Change–point detection in panel data. J. of Time Series Analysis 33, 631–648, 2012.
[11] Horváth, L., and Rice, G. Extensions of some classical methods in change point analysis (with discussions). Test 23,219–290, 2014.
[12] Hsiao, C. Analysis of Panel Data, 2nd ed. Cambridge University Press, Cambridge, 2003.
[13] Lemmon, M.L., Roberts, M.R., and Zender, J.F. Back to the beginning: Persistence and the cross-section of corporate
capital structure. J. of Finance 63, 1575–1608, 2008.
[14] Millo, G. and Piras, G. splm: Spatial panel data models in R. J. of Statistical Software 47, 1–38, 2012.
[15] The Oxford Handbook of Panel Data. Ed. Baltagi, B.H. Oxford University Press, New York, 2015.

1
This is a joint work with J. Hanousek, L. Horváth, M. Hušková and S. Wang. The work was supported by grant
GA ČR 15–09663S and StUDyS project IAP P7/06.

Dieter Wang, Vrije Universiteit Amsterdam, Nederland
Estimating Dynamic Spillover Effects with Spatial State-Space Models
Abstract:
We propose a spatial state-space model (SpatialSSM) as a novel framework toquantify time-varying spillover effects in economic networks. Similar to spatial
econometric models, this framework distinguishes between parameters for spatial
lag and spatial error components. We interpret these parameters as spillover
intensities for a given network. However, these parameters are often assumed to beconstant, which is restrictive for the analysis of time-variant systems. The key
innovation of SpatialSSMs is that they model these parameters as latent states withstochastic components. To estimate the resulting highly nonlinear state-spacemodels with stochastic volatility, we propose the smooth marginalized particle filter(SMPF). The good finite sample properties of the SMPF are demonstrated in an
extensive simulation study for a variety of network types. As an application, weexamine financial contagion between the 24 largest banks in the Eurozone for theperiod 2014Q1-2016Q2. Using confidential supervisory datasets we construct a
portfolio overlap network to capture spillovers between banks. We find that
SpatialSSMs improve upon established regression models. The network structure inparticular helps explain a major component in the structural regression residuals.
Furthermore, we find that default risk peaks are mainly driven by the spatial error
component, and that neglecting the network will likely overstate the importance ofstructural regressors.

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Navruzbek Karamatov, Tohoku UniversitySpatial CAPM and Spatial model for international stock markets
Abstract:
Co-movements among international stock markets have been studied for a long timein econometrics and portfolio performances. Especially after the 2007-2009 financial

crisis, a large number of studies have been published based on Spatial statisticalmodels. We will review several works on Spatial Contagion of international financial(stock) markets and Spatial CAPM (or APT) model from the viewpoints of thefollowing:(a) How they look the dependency and/or metric distance between the markets(b) How they depict the contagion, how they measure the degree of contagions(c) How they depict the change of “distance” and degrees of contagions, statistically.
(d) How the financial theory can be constructed, too much Simplifications?
The papers we have looked at were only several among many. They are listed below.
Kou et al (2017) proposes Spatial CAPM (Capital Asset Pricing Model) and Spatial
APT (Arbitrage Pricing Theory) with mathematical proofs by extending the classicalmodels and APT. Their approach includes the impacts of spatial impacts in the riskreturn relationships for financial assets. Then they study the empirical implicationby using the data of European countries (euro-currency-zone) over the period 20012013.
V. Fernandez (2011) defines/assumes a spatial CAPM by formulating a statisticalmodel with a spatial statistical terms added to the classical CAPM statistical model(Market model). This paper studies a spatial dependency in Latin American firms
inform Brazil, Chile and Mexico over the period 1997-2006.
G. Fernandez-Aviles (2015) takes up the questions of modelling the propagation of afinancial crisis where daily stock market indices from major international stock
exchanges are studied for contagion in financial markets, using spatio-temporal
statistical models. This uses multidimensional scaling (MDS) to obtain “a financialdistance” between the stock markets.
Durante et’al (2014) proposes a contagion measure that is based on a conditional
Spearman’s correlations. This can be used to measure an asymmetric linkages in thefinancial systems.

Reference:
1. Viviana Fernandez (2011). “Spatial linkages in international financialmarkets”, Quantitative Finance. Vol.11, No.2. pp.237-245.
2. Gema Fernandez-Aviles (2015). “Spatio-Temporal Modelling of Financial
Crisis Propagation”. International Conference on Regional Science:
Innovation and Geographical Spillovers; New Approaches and Evidence.
3. Steven Kou, Xianhua Peng and Haowen Zhong (2017). “Asset Pricing with
Satial Interaction”. Management Science. Articles in Advance pp1-19.
4. Fabrizio Durante, Enrico Foscolo, Piotr Jaworski and Hao Wang (2014)“A Spatial Contagion Measure for Financial Time Series”. Expert Systems
with Applications Vol.41. pp.4023-4034.

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Takashi Matsuki, Osaka Gakuin University
Linear and nonlinear comovement in Southeast Asian local currency bondmarkets: a stepwise multiple testing approach
Abstract:
This study investigates the existence of long-run comovement in the returns of local
currency denominated bonds of ASEAN-5 countries (Indonesia, Malaysia, thePhilippines, Singapore, and Thailand). We explore the pairwise cointegration
between Asian local currency bond returns indices using stepwise multiple testing.
This helps to identify which pairs of bond indices are cointegrated, while avoiding
over-rejection of the null hypothesis or the multiplicity problem. This method isadjusted to deal with possible cross-sectional correlation among the countries. Inaddition, we assume linear as well as nonlinear models in order to capture potential
gradual and asymptotic adjustment of a linear combination of bond indices toward
its mean. We find long-term stable relationships among local currency bond returns
for some pairs of countries. Specifically, close inter-linkages captured as nonlinear
cointegration are evident for at least four pairs of countries, namely, Indonesia andthe Philippines, Malaysia and the Philippines, the Philippines and Thailand, andSingapore and Thailand. In addition, a relatively weak but significant relationshipbetween Malaysia and Thailand is found. Although the adjustment process toward
long-run market equilibrium is characterized by the linear model, comovement inbond returns is observed between Malaysia and Singapore.

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Naoki Awaya, University of TokyoParticle rolling MCMC with double block sampling: conditional SMC update
approach
Abstract:
An efficient simulation-based methodology is proposed for the rolling window
estimation of state space models. Using the framework of the conditional sequential
Monte Carlo update in the particle Markov chain Monte Carlo estimation, weighted
particles are updated to learn and forget the information of new and old observations
by the forward and backward block sampling with the particle simulation smoother.
These particles are also propagated by the MCMC update step. Theoreticaljustifications are provided for the proposed estimation methodology. As a special case,
we obtain a new sequential MCMC based on Particle Gibbs. The computational
performance is evaluated in illustrative examples, showing that the posterior
distributions of model parameters and marginal likelihoods are estimated withaccuracy. Its application to S&P 500 data analysis is also provided.

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Takaki Sato, Tohoku University
Spatio-temporal autoregressive conditional heteroscedasticity model
Abstract:
This study proposes a spatio-temporal extension of time series autoregressive
conditional heteroskedasticity (ARCH) models. We call the spatio-temporallyextended ARCH models as spatio-temporal ARCH (ST-ARCH) models. ST-ARCH
models specify conditional variances given same time surrounding observations andpast time observations, which constitutes a good contrast with time series ARCH
models that specify conditional variances given past observations. A spatial weightmatrix which quantify the closeness between observations is used to express effects
from surrounding observations. We estimate the parameters of ST-ARCH models by
a two-step procedure of least squares and the quasi maximum likelihood estimation
to avoid bias of least squares estimators. We demonstrate the empirical properties
by real data analysis of stock price data in the Japanese market to show the relation
between volatility of a particular stock and change rates of same time and past timeother stock prices.

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Yoko Konishi, RIETI
Measuring the Value of Time in Freight Transportation
Abstract:
This paper presents an alternative approach to measuring the values of transport
time for freight transportation, and examines its applicability through empiricalanalysis. We develop a model of the freight transportation market, in which carriers
incur the cost associated with the effort to reduce transport time, and transport timeis endogenously determined in the market. We estimate the freight charge function,
expressway choice model, and transport time function, using microdata of freightflow in Japan collected by the Ministry of Land, Infrastructure, Transport andTourism. Based on the estimated freight charge function, we obtain the values oftransport time for shippers as an implicit price in the hedonic theory. The estimatedvalues of transport time for shippers are larger than those obtained by the widelyadopted method based on the discrete choice model. We also develop a method toevaluate the benefit of time-saving technological change (including infrastructureimprovement) based on the hedonic approach. Application to the evaluation ofexpressway construction suggests that the benefits calculated by our method tend to
be larger than those based on the other methods.

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Daisuke Murakami, Institute of Statistical Mathematics
Moran coefficient-based mixed effects approach to investigate spatiallyvarying relationships
Abstract:
This study develops a spatially varying coefficient (SVC) model by extending aspatial mixed effects approach called random effects eigenvector spatial filtering.
The developed model has the following properties: the SVCs are interpretable interms of the Moran coefficient, which is a diagnostic statistics for spatial dependence;unlike conventional approaches, the developed approach allows for estimating scalesof each SVC; it yields an approximation of a Bayesian SVC model; and, it iscomputationally efficient and applicable for large samples. Results of a Monte Carlosimulation reveals that our model outperforms conventional approaches, including
the geographically weighted regression (GWR) and the eigenvector spatial filtering
approaches, in terms of the accuracy of the SVC estimates and computational time.
We empirically apply our model to a land price analysis of flood hazards in Japan.

List of Participants
Jarom´ır Antoch (Charles University in Praha, Czech Republic)
Dieter Wang (Vrije Universiteit Amsterdam, Nederland)
Navruzbek Karamatov, (Tohoku University)
Takashi Matsuki, (Osaka Gakuin University)
Naoki Awaya (University of Tokyo)
Takaki Sato, (Tohoku University)
Yoko Konishi (RIETI)
Daisuke Murakami (Institute of Statistical Mathematics)