Spatio-temporal Autoregressive Model Research Articles

FDI technology spillovers, geography, and spatial diffusion

This paper investigates the geographic extent of FDI technology spillovers and associated spatial diffusion. By adopting a spatiotemporal autoregressive panel model as the platform of our study, the complex impact resulting from FDI penetration is separated into spatial direct and indirect effects while accounting for feedback loops among regions. A set of spatially partitioned summary measures is produced to identify and to quantify FDI spillovers from different channels with distinct geographic scopes. Empirical results based on data from China document that the direct impacts of FDI presence to a specific location itself are likely to be negative. Domestic firms mainly benefit from FDI presence in their neighboring regions through knowledge spillovers that have wider geographic scope. Negative market stealing effect nevertheless has no spatial boundary. Policy implications of these findings are discussed.

International Real Estate Review

In order to explore the long-run equilibrium in the house prices of different cities, studies on house price convergence have been conducted by a number of researchers. However, the majority of previous studies have neglected the effects of spatial heterogeneity and autocorrelation on house prices. This research improves on the investigation of house price convergence by developing a spatio-temporal autoregressive model based on a framework of panel regression methods. Both spatial heterogeneity and autocorrelation of house prices in different cities are taken into account. Geographical distance and the scale of development of the urban housing market are used to construct temporal varying spatial measurements. The spatio-temporal model is then applied to investigate the long-run equilibrium in the house prices of Australian capital cities. The results confirm that house prices in Sydney approach a steady state in the long run, whereas house prices in Brisbane, Canberra, Melbourne and Perth are able to do with lower confidence. However, little evidence supports the existence of long-run equilibrium in the house prices of Adelaide, Darwin and Hobart.

The Spatial Dimensions of State Fiscal Capacity The Mechanisms of International Influence on Domestic Extractive Efforts

This paper expands traditional predatory theory approaches to state fiscal capacity by adopting spatial analytical reasoning and methods. Although previous work in the predatory theory tradition has often incorporated interdependent external influences, such as war and trade, it has often done so in a way that maintains a theoretical and empirical autonomy of the state. Theoretically, we suggest four mechanisms (coercion, competition, learning, and emulation) that operate to channel information through interstate rivalry and territorial contiguity, trade networks, and the political space associated with regime type and intergovernmental organization membership. We test our predictions using a multi-parametric spatio-temporal autoregressive model with four spatial lags capturing the four mechanisms. Our empirical results provide support for the coercion and learning mechanisms.

The Role of Spatial and Temporal Structure for Residential Rent Predictions

This paper examines the predictive power of five linear hedonic pricing models for the residential market with varying complexity in their spatial and temporal structure. In contrast to similar studies, we extend the out-of-sample forecast evaluation to one-day-ahead predictions with a rolling estimation window, which is a reasonable setting for many practical applications. We can show that in-sample fit and cross-validation prediction accuracy improve significantly when we account for spatial heterogeneity. In particular, for one-day-ahead forecasts, the spatiotemporal autoregressive (STAR) model demonstrates its superiority compared to model specifications with alternating spatial and temporal heterogeneity and dependence structures. In addition, sub-market fixed-effects, constructed on the basis of statistical TREE methods, further improve the results of predefined local rental markets.

An evaluation of experimental decadal predictions using CCSM4

This study assesses retrospective decadal prediction skill of sea surface temperature (SST) variability in initialized climate prediction experiments with the Community Climate System Model version 4 (CCSM4). Ensemble forecasts initialized with two different historical ocean and sea-ice states are evaluated and compared to an ensemble of uninitialized coupled simulations. Both experiments are subject to identical twentieth century historical radiative forcings. Each forecast consists of a 10-member ensemble integrated over a 10-year period. One set of historical ocean and sea-ice conditions used for initialization comes from a forced ocean-ice simulation driven by the Coordinated Ocean-ice Reference Experiments interannually varying atmospheric dataset. Following the Coordinated Model Intercomparison Project version 5 (CMIP5) protocol, these forecasts are initialized every 5 years from 1961 to 1996, and every year from 2000 to 2006. A second set of initial conditions comes from historical ocean state estimates obtained through the assimilation of in-situ temperature and salinity data into the CCSM4 ocean model. These forecasts are only available over a limited subset of the CMIP5 recommended start dates. Both methods result in retrospective SST prediction skill over broad regions of the Indian Ocean, western Pacific Ocean and North Atlantic Ocean that are significantly better than reference skill levels from a spatio-temporal auto-regressive statistical model of SST. However the subpolar gyre region of the North Atlantic stands out as the only region where the CCSM4 initialized predictions outperform uninitialized simulations. Some features of the ocean state estimates used for initialization and their impact on the forecasts are discussed.

Retrospective Analysis of the Dwelling Price by Means of STAR Models with Neighborhood Effects

The need of retrospective predictions arises in situations such as litigations, inheritances or fiscal purposes to provide the fair market value of a dwelling of given characteristics and specific location. In this paper, a Bayesian method to carry out this retrospective analysis is proposed by using a Spatio-Temporal Autoregressive (STAR) model with neighborhood effects and heteroscedastic errors. Moreover, the developed methodology allows the construction of a retrospective price index that shows the evolution of the dwelling price level in a specific geographical area. The methodology is illustrated with an application to the real estate market in the Spanish city of Zaragoza.

The role of borders, languages, and currencies as obstacles to labor market integration

Based on a modified spatiotemporal autoregressive (STAR) model, we analyze whether borders still constitute significant impediments to labor market integration in the European Union, despite the formal law of free movement of labor. Using regional data from the EU-15 countries over 21 years, we find that this is the case. We further investigate whether the abolishment of border checks through the Schengen agreement or the introduction of the Euro improved our measure of labor market integration across borders, and do not find evidence in favor. Last, we investigate the role of languages, and potentially cultures, as obstacles to labor market integration. We find that indeed language borders play a larger role than country borders in explaining the lack of labor market integration across borders.

Spatial and Temporal Dependence in House Price Prediction

This paper incorporates spatial and temporal dependence among housing transactions in predicting future house prices. We employ the spatiotemporal autoregressive model and structure the spatial and temporal weighting matrices as in Pace et al. (1998). We control for the time variation of both the attribute prices and the spatial and temporal dependence parameters through performing the analysis on an annual basis. Spatial heterogeneity is accounted for using experience-based definition of submarkets by real estate professionals. Using a comprehensive housing transaction data set from the Dutch Randstad region, we show that integrating the spatial and temporal dependence within the hedonic modeling improves the prediction power as compared to traditional hedonic model that neglects these effects.

Analysis of housing price by means of STAR models with neighbourhood effects: a Bayesian approach

In this paper, we extend the Bayesian methodology introduced by Beamonte et al. (Stat Modelling 8:285–311, 2008) for the estimation and comparison of spatio-temporal autoregressive models (STAR) with neighbourhood effects, providing a more general treatment that uses larger and denser nets for the number of spatial and temporal influential neighbours and continuous distributions for their smoothing weights. This new treatment also reduces the computational time and the RAM necessities of the estimation algorithm in Beamonte et al. (Stat Modelling 8:285–311, 2008). The procedure is illustrated by an application to the Zaragoza (Spain) real estate market, improving the goodness of fit and the outsampling behaviour of the model thanks to a more flexible estimation of the neighbourhood parameters.

Robust Bayesian inference in STAR models with neighbourhood effects

In this paper we propose a robust Bayesian procedure of estimation, testing, validation and selection of spatio-temporal autoregressive models (STAR) with neighbourhood effects applied to the appraisal of dwelling prices. The methodology does not depend on asymptotic results and, unlike previously procedures proposed in the literature, takes into account the uncertainty associated to the estimation of the neighbourhood parameters of the model, giving more realism to the analysis. Moreover, a sequential algorithm to elaborate fast on-line forecast, is provided. The methodology is illustrated by means of a practical case of the real estate market of Zaragoza.

Variable selection in STAR models with neighbourhood effects using genetic algorithms

In this paper we deal with the problem of variable selection in spatiotemporal autoregressive (STAR) models with neighbourhood effects. We propose a procedure to carry out the selection process, taking into account the uncertainty associated with estimation of the parameters and the predictive behaviour of the compared models, in order to give more realism to the analysis. We set up a multi-objective programming problem that combines the use of different criteria to measure both these aspects. We use genetic algorithms which are very flexible and suitable for our multicriteria decision problem. In particular, the procedure allows us to estimate the number of spatial and temporal nearest neighbours as well as their relative effects. The methodology is illustrated through an application to the real estate market of Zaragoza. Copyright © 2010 John Wiley & Son, Ltd.

A spatial model incorporating dynamic, endogenous network interdependence: A political science application

We propose applying the multiparametric spatiotemporal autoregressive (m-STAR) model as a simple approach to estimating jointly the pattern of connectivity and the strength of contagion by that pattern, including the case where connectivity is endogenous to the dependent variable ( selection). We emphasize substantively-theoretically guided (i.e., structural) specifications that can support analyses of estimated spatiotemporal responses to stochastic or covariate shocks and that can distinguish the possible sources of spatial association: common exposure, contagion, and selection (e.g., homophily). We illustrate this approach to dynamic, endogenous interdependence–which parallels models of network–behavior co-evolution in the longitudinal networks literature–with an empirical application that aims to disentangle the roles of economic interdependence, correlated external and internal stimuli, and EU membership in shaping labor market policies in developed democracies in recent years.

A Spatio-Temporal Auto Regressive Model for Frame Rate Upconversion

This paper proposes a spatio-temporal auto regressive (STAR) model for frame rate upconversion. In the STAR model, each pixel in the interpolated frame is approximated as the weighted combination of a sample space including the pixels within its two temporal neighborhoods from the previous and following original frames as well as the available interpolated pixels within its spatial neighborhood in the current to-be-interpolated frame. To derive accurate STAR weights, an iterative self-feedback weight training algorithm is proposed. In each iteration, first the pixels of each training window in the interpolated frames are approximated by the sample space from the previous and following original frames and the to-be-interpolated frame. And then the actual pixels of each training window in the original frame are approximated by the sample space from the previous and following interpolated frames and the current original frame with the same weights. The weights of each training window are calculated by jointly minimizing the distortion between the interpolated frames in the current and previous iterations as well as the distortion between the original frame and its interpolated one. Extensive simulation results demonstrate that the proposed STAR model is able to yield the interpolated frames with high performance in terms of both subjective and objective qualities.

A Spatiotemporal Autoregressive Price Index for the Paris Office Property Market

This article applies the spatiotemporal hedonic approach to the analysis of office transaction prices in the Paris property market (i.e., central Paris and its inner suburbs). The analysis focuses primarily on the market's two main business districts (the Central Business District and the La Défense District). We find that spatial and temporal dependence effects are strongly present in these submarkets. Additionally, we propose a hybrid method for incorporating a temporal regime switch into the spatiotemporal autoregressive model. The regime switching around 1997 (i.e., in the presence of temporal heterogeneity) substantially affects the significance of spatial and temporal dependences. Finally, we build a new price index that incorporates both spatiotemporal dependences and temporal heterogeneity. This index differs strongly from the usual hedonic price index.

Bayesian inference in STAR models using neighbourhood effects

In this paper we propose a semi-parametric Bayesian analysis of a spatio-temporal autoregressive model (STAR) with neighbourhood effects similar to those of Pace et al. (1998, 2000). This approach allows us to make inferences about the parameters of the model and, more particularly, about the number of neighbours, without having to appeal to asymptotic results. In addition, the procedure used to obtain the out-sample predictions takes into account the uncertainty associated to the estimation of the model parameters in a more realistic way. The methodology is illustrated by means of an application to the real estate market in the city of Zaragoza (Spain).

The m-STAR Model as an Approach to Modeled, Dynamic, Endogenous Interdependence in Comparative & International Political Economy

Even casual observation reveals obvious spatial patterns in labor-market outcomes and policies across the developed democracies, and within the European Union particularly. Labor-market policies entail significant cross-border spillovers, so strategic interdependence among developed democracies might explain this. However, these countries also faced common or very similar exogenous-external conditions and internal trends, which would also tend to generate spatial patterns in the domestic responses thereto, even without any interdependence. Likewise, membership in the EU itself presents both a series of common external stimuli and a set of strategic interdependencies in common and individual-country labor-market-relevant actions. Additionally, however, labor-market policies will themselves shape the patterns of economic interchange by which some of the interdependencies arise, and entry into the European Union typically presupposes a certain baseline set of shared national characteristics and orientations, raising the possibility that labor-market policies and the pattern of interdependence via institutional co-membership have common origin. That is, the policies of interest may also shape the patterns of connectivity affecting those outcomes, a complex sort of endogeneity known as selection in the dynamic networks literature. We have discussed elsewhere the severe empirical-methodological challenges in distinguishing the first two of these possible sources of spatial correlation (Galton's Problem). This paper extends those analyses, applying the multiparametric spatiotemporal autoregressive (m-STAR) model as a simple approach to modeling the patterns of interdependence simultaneously with its effects, while recognizing their possible endogeneity (i.e., selection). We do so in an empirical application attempting to disentangle the roles of economic interdependence, correlated external and internal stimuli, and EU membership in shaping labor-market policies in recent years.

A new image prediction model based on spatio-temporal techniques

This paper addresses an image prediction problem focused on images with no identifiable objects. In it, we present several approaches to predict the next image of a given sequence, when the image lacks the well-defined objects, such as meteorological maps or satellite imagery. In these images no clear borders are present, and any object candidate moves, changes, appears and disappears in any image. Nevertheless, this evolution, though unrestricted, is gradual and, hence, prediction looks feasible. One of the approaches presented here, based on a spatio-temporal autoregressive (STAR) model, offers good results for these kinds of images. The main contribution of this paper is to adapt spatio-temporal models to an image prediction problem. As a byproduct of this research, we have achieved a new image compression method, suitable for images without defined shapes.

A Spatio-Temporal Autoregressive Model for Multi-Unit Residential Market Analysis*

By splitting the spatial effects into building and neighborhood effects, this paper develops a two order spatio-temporal autoregressive model to deal with both the spatio-temporal autocorrelations and the heteroscedasticity problem arising from the nature of multi-unit residential real estate data. The empirical results based on 54,282 condominium transactions in Singapore between 1990 and 1999 show that in the multi-unit residential market, a two order spatio-temporal autoregressive model incorporates more spatial information into the model, thus outperforming the models originally developed in the market for single-family homes. This implies that the specification of a spatio-temporal model should consider the physical market structure as it affects the spatial process. It is found that the Bayesian estimation method can produce more robust coefficients by efficiently detecting and correcting heteroscedasticity, indicating that the Bayesian estimation method is more suitable for estimating a real estate hedonic model than the conventional OLS estimation. It is also found that there is a trade off between the heteroscedastic robustness and the incorporation of spatial information into the model estimation. The model is then used to construct building-specific price indices. The results show that the price indices for different condominiums and the buildings within a condominium do behave differently, especially when compared with the aggregate market indices.

Transaction‐Based Office Price Indexes: A Spatiotemporal Modeling Approach

This study examines the potential of a two‐order spatiotemporal autoregressive model with a Bayesian heteroskedasticity robust procedure in modeling strata‐titled Singapore office unit transaction prices and in constructing transaction‐based disaggregate office price indexes. The model reduces the problems caused by the infrequent trading of individual commercial properties. However, for those office properties that are located outside the CBD and also for those less frequently transacted, the power of the model in capturing these particular office buildings' price dynamics is limited. The significant differences of the office prices across the various office buildings and submarkets show that the model can capture the variation in office prices and track the timing of capital gains and losses that investors may accrue on spatially distributed office properties more accurately than hedonic or weighted least squares estimates.

Using Temporal Texture for Content-Based Video Retrieval

Textures evolving over time are called temporal textures and are very common in everyday life. Examples are the smoke flowing or the wavy water of a river. The idea explored in this paper is that image features based on temporal texture could allow a better performance of current content-based video retrieval systems that are mainly based on static characteristics of representative frames, like color and texture. To this aim we analyze the spatio-temporal nature of texture and its application in content-based access to video databases. In particular, we represent temporal texture using the spatio-temporal autoregressive (STAR) model and a variation of self-organizing maps (SOM) where each node is an autoregressive model. These representation schemes have been implemented in a query by example framework to analyze the weaknesses and the strengths of the different approaches. Preliminary experimental results are reported.