if this message does not display correctly, click here | Table of Contents Federico Belotti, University of Rome, Tor Vergata - Faculty of Economics
Gordon Hughes, University of Edinburgh
Andrea Piano Mortari, CEIS Tor Vergata Martyna Marczak, University of Hohenheim
Tommaso Proietti, University of Rome II - Department of Economics and Finance
Stefano Grassi, University of Kent, Canterbury Leopoldo Catania, University of Rome, Tor Vergata - Department of Economics and Finance
Anna Gloria Billé, University of Rome, Tor Vergata - Department of Economics and Finance | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Vincenzo Atella - Director "Spatial Panel Data Models Using Stata" 
CEIS Working Paper No. 373 FEDERICO BELOTTI, University of Rome, Tor Vergata - Faculty of Economics
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GORDON HUGHES, University of Edinburgh
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ANDREA PIANO MORTARI, CEIS Tor Vergata
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Xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fixed- and random-effects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite of commands, to use spatial weight matrices in the form of both Stata matrices and spmat objects, to compute direct, indirect and total effects according to the procedure outlined in LeSage and Pace (2009), and to exploit a wide range of postestimation features, extending to the panel data case the predictors proposed by Kelejian and Prucha (2007). This paper describes the command and all its functionalities using both simulated and real data. "A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models"
CEIS Working Paper No. 374 MARTYNA MARCZAK, University of Hohenheim
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TOMMASO PROIETTI, University of Rome II - Department of Economics and Finance
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STEFANO GRASSI, University of Kent, Canterbury
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This article presents a robust augmented Kalman filter that extends the data – cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M–type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series. "Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances"
CEIS Working Paper No. 375 LEOPOLDO CATANIA, University of Rome, Tor Vergata - Department of Economics and Finance
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ANNA GLORIA BILLÉ, University of Rome, Tor Vergata - Department of Economics and Finance
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We propose a new class of models specifi
cally tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. 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 coeffi
cients as well as time-varying regressor coe
fficients 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 exibility in explaining several dynamic spatial dependence processes. The new proposed class of models are found to be economically preferred by rational investors through an application in portfolio optimization. | | ^top
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