HIERARCHICAL BAYESIAN MODELING FOR SPATIAL TIME SERIES: AN ALTERNATIVE APPROACH TO SPATIAL SUR

Despite the fact that the amount of datasets containing long economic time series with a spatial reference has significantly increased during the years, the presence of integrated techniques that aim to describe the temporal evolution of the series while accounting for the location of the measurements and their neighboring relations is very sparse in the econometric literature. This paper shows how the Hierarchical Bayesian Space Time model presented by Wikle, Berliner and Cressie (Environmental and Ecological Statistics, l998) for temperature modeling, can be tailored to model relationships between variables that have both a spatial and a temporal reference. The first stage of the hierarchical model includes a set of regression equations (each one corresponding to a different location) coupled with a dynamic space-time process that accounts for the unexplained variation. At the second stage, the regression parameters are endowed with priors that reflect the neighboring relations of the locations under study; moreover, the spatio-temporal dependencies in the dynamic process for the unexplained variation are being established. Putting hyperpriors on previous stages’ parameters completes the Bayesian formulation, which can be implemented in a Markov Chain Monte Carlo framework. The proposed modeling strategy is useful in quantifying the temporal evolution in relations between economic variables and this quantification may serve for excess forecasting accuracy.space-time models

Spatial Time-Series Modeling: A review of the proposed methodologies

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better suited for applications of large spatial and temporal dimension whereas the latter can be realistically performed when the number of locations of the study is rather small. Next, we consider models that aim to describe relationships between variables with a spatio-temporal reference and discuss the general class of dynamic space-time models in the framework presented by Elhorst (2001). Each model class is introduced through a motivating application.spatial time-series, space-time models, STARIMA, Bayesian Vector Autoregressions

Geographical competition-complementarity relationships between Greek regional economies

This paper examines the nature of interregional competition and complementarity in Greece. Covering the 1975-2002 period, the analysis provides a picture of the interregional interactions by focusing on derived linkages between the regional economies. The adopted methodology combines previous approaches based on the Dendrinos-Sonis model (e.g. Nazara et al 2002, Bonet 2003) and a cointegration modeling framework (Marquez and Hewings, 2003). A sensitivity analysis of the model coefficients that designate competition or complementarity, with respect to time, is undertaken as well.

Controlling the risky fraction process with an ergodic criterion

This article examines a tracking problem, similar to the one presented in Pliska and Suzuki (Quantitative Finance, 2004): an investor would keep constant proportions of her wealth in different assets if markets were frictionless; however, in the presence of fixed and proportional transaction costs her implementation problem is to keep asset proportions close to the target levels whilst avoiding too much intervention costs. Instead of minimizing discounted tracking error plus transaction costs over an infinite horizon, the optimization objective here is minimization of long run tracking error plus intervention costs per unit time. This ergodic problem is treated via combining basic tools from diffusion theory and nonlinear optimization techniques. A comparative sensitivity analysis of the ergodic and discounted problems is undertaken.

An irreversible investment model with a stochastic production capacity and fixed plus proportional adjustment costs

This paper studies the problem of a company which expands its stochastic production capacity in irreversible investments by purchasing capital and faces both fixed and proportional costs. The objective of the company is to find optimal production decisions to maximize its expected total net profit in an infinite horizon. We solve this problem explicitly by applying the theory of stochastic impulse controls.Irreversible investment; production; quasi-variational inequalities; stochastic impulse control

Space-time modeling of traffic flow

A key concern in transportation planning and traffic management is the ability to forecast traffic flows on a street network. Traffic flows forecasts can be transformed to obtain travel time estimates and then use these as input to travel demand models, dynamic route guidance and congestion management procedures. A variety of mathematical techniques have been proposed for modeling traffic flow on a street network. Briefly, the most widely used theories are: -Kinetic models based on partial differential equations that describe waves of different traffic densities, -deterministic models that use nonlinear equations for the estimation of different car routes, -large scale simulation models such as cellular automata and, -stochastic modeling of traffic density at distinct points in space. One problem with these approaches is that the traffic flow process is characterized by nonstationarities that cannot be taken into account by the vast majority of modeling strategies. However, recent advances in statistical modeling in fields such as econometrics or environmetrics enable us to overcome this problem. The aim of this work is to present how two statistical techniques, namely, vector autoregressive modeling and dynamic space-time modeling can be used to develop efficient and reliable forecasts of traffic flow. The former approach is encountered in the econometrics literature, whereas the later is mostly used in environmetrics. Recent advances in statistical methodology provide powerful tools for traffic flow description and forecasting. For a purely statistical approach to travel time prediction one may consult Rice and van Zwet (2002). In this work, the authors employ a time varying coefficients regression technique that can be easily implemented computationally, but is sensitive to nonstationarities and does not take into account traffic flow information from neighboring points in the network that can significantly improve forecasts. According to our approach, traffic flow measurements, that is count of vehicles and road occupancy obtained at constants time intervals through loop detectors located at various distinct points of a road network, form a multiple time series set. This set can be described by a vector autoregressive process that models each series as a linear combination of past observations of some (optimally selected) components of the vector; in our case the vector is comprised by the different measurement points of traffic flow. For a thorough technical discussion on vector autoregressive processes we refer to Lutkerpohl (1987), whereas a number of applications can be found in Ooms (1994). Nowadays, these models are easily implemented in commercial software like SAS or MATLAB; see for example LeSage (1999). The spatial distribution of the measurement locations and their neighboring relations cannot be incorporated in a vector autoregressive model. However, accounting for this information may optimize model fitting and provide insight into spatial correlation structures that evolve through time. This can be accomplished by applying space-time modeling techniques. The main difference of space-time models encountered in literature with the vector autoregressive ones lies in the inclusion of a weight matrix that defines the neighboring relations and places the appropriate restrictions. For some early references on space-time models, one could consult Pfeifer and Deutsch (1980 a,b); for a Bayesian approach, insensitive to nonstationarities we refer to Wikle, Berliner and Cressie (1998). In this work, we discuss how the space-time methodology can be implemented to traffic flow modeling. The aforementioned modeling strategies are applied in a subset of traffic flow measurements collected every 15 minutes through loop detectors at 74 locations in the city of Athens. A comparative study in terms of model fitting and forecasting accuracy is performed. Univariate time series models are also fitted in each measurement location in order to investigate the relation between a model's dimension and performance. References: LeSage J. P. (1999). Applied Econometrics using MATLAB. Manuscript, Dept. of Economics, University of Toronto Lutkerpohl H. (1987). Forecasting Aggregated Vector ARMA Processes. Lecture Notes in Economics and Mathematical Systems. Springer Verlag Berlin Heidelberg Ooms M. (1994). Empirical Vector Autoregressive Modeling. Springer Verlag Berlin Heidelberg Pfeifer P. E., and Deutsch S. J. (1980a). A three-stage iterative procedure for Space-Time Modeling. Technometrics, 22, 35-47 Pfeifer P. E., and Deutsch S. J. (1980b). Identification and Interpretation of First-Order Space-Time ARMA models. Technometrics, 22, 397-408 Rice J., and van Zwet E. (2002). A simple and effective method for predicting travel times on freeways. Manuscript, Dept. of Statistics, University of California at Berkeley Wikle C. K., Berliner L. M. and Cressie N. (1998). Hierarchical Bayesian space-time models. Environmental and Ecological Statistics, 5, 117-154

Stochastic impulse control with discounted and ergodic optimization criteria: A comparative study for the control of risky holdings

We consider a single-asset investment fund that in the absence of transactions costs would hold a constant amount of wealth in the risky asset. In the presence of market frictions wealth is allowed to fluctuate within a control band: Its upper (lower) boundary is chosen so that gains (losses) from adjustments to the target minus (plus) fixed plus proportional transaction costs maximize (minimize) a power utility function. We compare stochastic impulse control policies derived via ergodic and discounted optimization criteria. For the solution of the ergodic problem we use basic tools from the theory of diffusions whereas the discounted problem is solved after being characterized as a system of quasi-variational inequalities. For both versions of the problem, derivation of the control bands pertains to the numerical solution of a system of nonlinear equations. We solve numerous such systems and present an extensive comparative sensitivity analysis with respect to the parameters that characterize investor’s preferences and market behavior.Transaction costs; stochastic impulse control; ergodic criteria

The evolution of regional productivity disparities in the European Union, 1975-2000

The aim of this paper is to assess the evolution of regional productivity disparities in the European Union. Using a sample of 205 regions and 8 sectors on the 1975-2000 period, we use Esteban’s (2000) shift-share analysis to investigate the extent to which the existing interregional inequalities in productivity can be attributed to differences in sectoral composition between regions and/or to uniform productivity gaps across sectors. After a specification search on the bivariate functional forms that relate productivity differentials to their shift-share decomposition, the difference between regional and EU average productivity is regressed on the three shift-share components: industry-mix, differential and allocative (i.e. the covariance between the first two components). In that purpose, spatial seemingly unrelated (SUR) regressions are carried out to study the evolution of the impact of the components on the productivity gap over time, while allowing for intertemporal covariance. Moreover, spatial autocorrelation is also included in the SUR regressions, and its evolution over the period is analyzed. Results indicate that both spatio-temporal dependencies are essential in model specification.European regions, productivity disparities, spatial autocorrelation, SUR

Priorities for Mediterranean marine turtle conservation and management in the face of climate change

As climate-related impacts threaten marine biodiversity globally, it is important to adjust conservation efforts to mitigate the effects of climate change. Translating scientific knowledge into practical management, however, is often complicated due to resource, economic and policy constraints, generating a knowledge-action gap. To develop potential solutions for marine turtle conservation, we explored the perceptions of key actors across 18 countries in the Mediterranean. These actors evaluated their perceived relative importance of 19 adaptation and mitigation measures that could safeguard marine turtles from climate change. Of importance, despite differences in expertise, experience and focal country, the perceptions of researchers and management practitioners largely converged with respect to prioritizing adaptation and mitigation measures. Climate change was considered to have the greatest impacts on offspring sex ratios and suitable nesting sites. The most viable adaptation/mitigation measures were considered to be reducing other pressures that act in parallel to climate change. Ecological effectiveness represented a key determinant for implementing proposed measures, followed by practical applicability, financial cost, and societal cost. This convergence in opinions across actors likely reflects long-standing initiatives in the Mediterranean region towards supporting knowledge exchange in marine turtle conservation. Our results provide important guidance on how to prioritize measures that incorporate climate change in decision-making processes related to the current and future management and protection of marine turtles at the ocean-basin scale, and could be used to guide decisions in other regions globally. Importantly, this study demonstrates a successful example of how interactive processes can be used to fill the knowledge-action gap between research and management.This work was conducted under FutureMares EU project that received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869300. The Mediterranean Marine Turtle Working Group was established in 2017 and is continuously supported by MedPAN and the National Marine Park of Zakynthos. The work of AC was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 2340).Peer reviewe