Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances

Abstract

We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score-driven (SD) models typically used in time series econometrics. In particular, we allow for time-varying spatial autoregressive coefficients as well as time-varying regressor coefficients and cross-sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite-sample properties of the maximum likelihood estimator for the new class of models as well as its flexibility in explaining a misspecified dynamic spatial dependence process. The new proposed class of models is found to be economically preferred by rational investors through an application to portfolio optimization.1 See Lee and Yu (2016) for identification issues in spatial Durbin panel models. 2 For the incidental parameter problem within spatial panel data models with fixed effects see Lee and Yu (2010a).

The Time-Spatial Dimension of Eurozone Banking Systemic Risk

“…Usually, most of the works assume the space (network) as a pure geographical distance. However, in finance, the neighbourhood is an immaterial concept (Catania and Billé (2017)).…”

“…Therefore, the work contributes to the debate between intervention and non-intervention of monetary policy, in particular, between the "leaning against the wind" approach, which believes that central banks should use monetary stance also to management financial imbalances, and the "modified Jackson Hole consensus", which argues that the central banks have to focus only on price stability (Smets (2014)). Fourth, our work contributes to different branches of literature: (i) the researches on contagion and risk spillovers (Giglio (2016); De Bruyckere et al (2013); Battiston et al (2012)) 3 ; (ii) the application spatial econometrics models in the financial contest (Catania and Billé (2017)); (iii) the study of the determinants of CDS spread 4 (Annaert et al (2013); Samaniego-Medina et al (2016)); and (iv) the study of the bank risk-taking channel (Buch et al (2014); Angeloni et al (2015)).…”

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

“…The results of the Spatial Error model (SEM) in Table 4 show a better fit of the model when the spatial dependence was managed with the use of the spatial weight matrix and the introduction of a coefficient, the disturbance (λ), in the explanatory variables. The spatial autoregressive model with spatial error dependence consists of a linear relationship between a conditional expectation of the dependent variable and its values with spatial dependent error terms in the rest of the system [76]. The R−squared value of 0.64 highlights that NeSI had a significant and positive influence on the LP variance.…”

Generalized Autoregressive Score Models in R: The GAS Package

“…It does not matter whether they are real-valued, integer-valued, (0, 1)-bounded or strictly positive, as long as there is a conditional density for which the score function and the Hessian are well-defined. The practical relevance of the GAS framework has been illustrated in the case of financial risk forecasting (see e.g., Harvey and Sucarrat (2014) for market risk, Oh and Patton (2016) for systematic risk, and Creal, Schwaab, Koopman, and Lucas (2014) for credit risk analysis), dependence modeling (see e.g., Harvey and Thiele (2016) and Janus, Koopman, and Lucas (2014)), and spatial econometrics (see e.g., Blasques, Koopman, Lucas, and Schaumburg (2016b) and Catania and Billé (2017)). For a more complete overview of the work on GAS models, we refer the reader to the GAS community page at http://www.gasmodel.com/.…”