Panel VAR Models with Spatial Dependence

I consider a panel vector autoregressive (panel VAR) model with cross sectional dependence of the model disturbances that can be characterized by a first order spatial autoregressive process. I derive asymptotic properties of a constrained maximum likelihood estimator that uses a consistent estimate of the degree of the spatial autocorrelation to concentrate the likelihood function. The asymptotic properties are derived taking the time dimension of the panel as fixed and letting the cross-sectional dimension tend to infinity.

Panel VAR Models with Spatial Dependence

I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation. In the second step, the estimated disturbances are used in a multivariate spatial generalized moments estimation to infer the degree of spatial correlation. The final step of the procedure uses transformed data and applies standard techniques for estimation of panel vector-autoregressive models. I compare the small-sample performance of various estimation strategies in a Monte Carlo study.Spatial PVAR, Multivariate dynamic panel data model, Spatial GM, Spatial Cochrane-Orcutt transformation, Constrained maximum likelihood estimation

Consistent Estimation of Global VAR Models

In this paper, I propose an instrumental variable (IV) estimation procedure to estimate global VAR (GVAR) models and show that it leads to consistent and asymptotically normal estimates of the parameters. I also provide computationally simple conditions that guarantee that the GVAR model is stable.Global VAR, GVAR, Consistent estimation, Instrumental variables

Testing Nonlinear New Economic Geography Models

We test a New Economic Geography (NEG) model for U.S. counties, employing a new strategy that allows us to bring the full NEG model to the data, and to assess selected elements of this model separately. We find no empirical support for the full NEG model. Regional wages in the U.S. do not respond to local wage shocks in the way predicted by the model. We show that the main reason for this is that the model does not predict either the migration patterns induced by local wage shocks or the repercussions of this migration for regional wages correctly.New economic geography, spatial econometrics

The Spatial Random Effects and the Spatial Fixed Effects Model. The Hausman Test in a Cliff and Ord Panel Model

This paper studies the spatial random effects and spatial fixed effects model. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one-way error component structure, accounting for both heterogeneity and spatial correlation across units. We discuss instrumental variable estimation under both the fixed and the random effects specification and propose a spatial Hausman test which compares these two models accounting for spatial autocorrelation in the disturbances. We derive the large sample properties of our estimation procedures and show that the test statistic is asymptotically chi-square distributed. A small Monte Carlo study demonstrates that this test works well even in small panels.Spatial econometrics, Panel data, Random effects estimator, Within estimator, Hausman test

DYNAMIC PANEL DATA MODELS WITH SPATIALLY CORRELATED DISTURBANCES

This thesis considers a dynamic panel data model with error components that are correlated both spatially (cross-sectionally) and time-wise. The model extends the literature on dynamic panel data models with cross-sectionally independent error components. The model for spatial dependence is a Cliff-Ord type model. We introduce a three step estimation procedure and give formal large sample results for the case of a finite time dimension. In particular, we show that a simple first stage instrumental variable (IV) estimator, that ignores the spatial correlation of the errors, is consistent and √N-consistent, where N denotes the cross-sectional dimension. We then extend the generalized moments estimator introduced by Kelejian and Prucha (1999) for estimating the spatial autoregressive parameter and show that if it is based on a √N-consistently estimated disturbances, it will also be consistent. Finally, we derive a large sample distribution of a second stage generalized method of moments (GMM) estimator based on a consistent estimator of the spatial autoregressive parameter. We also present results from a small Monte Carlo study to illustrate the small sample performance of the proposed estimation procedure

Going Beyond Buildings: Mindfulness and Real Estate User Behavior

Purpose – Building performance does not only depend on its efficiency but also on the behaviors of its occupants. Occupant behaviors can more than offset technological efficiency gains so that corporate real estate (CRE) managers have to go beyond sustainable buildings. CRE managers need to understand occupants in order to effectively reduce the environmental impact their building portfolio. This study investigates the effects of environmental attitudes and mindfulness on occupant behaviors at home and at the office. Thereby, we address numerous calls for research regarding the drivers of more environmental real estate user behaviors (EREUB). Design/methodology/approach – The authors employ partial least squares structural equation modeling based on self-report data obtained for a representative German sample. Findings – The results show that environmental attitudes as well as mindfulness have both positive effects on occupant behaviors. However, the effects tend to be weaker in the office context. Research limitations/implications – This study relies on self-reports as indicator of actual behaviors. Besides, the findings are limited by the cross-sectional nature of the data. Practical implications – Environmental education as well as mindfulness training may be an effective way to promote more environmental occupant behaviors and help CRE managers to further reduce the environmental impact of their building portfolio. Originality/value – The paper contributes to prior research about the antecedents of environmental behaviors and provides evidence for the positive impact of environmental attitudes and mindfulness on occupant behaviors. We provide a new approach for CRE managers, which may improve occupant behaviors

Steigende Immobilienpreise und steigende Wohnungsnot: Wohnungsmarkt aus dem Gleichgewicht?

Obwohl immer mehr Geld in den Immobilienmarkt fließt, steigt die Wohnungsnot in Ballungsräumen. Vor allem wächst die Diskrepanz zwischen dem Angebot an und der Nachfrage nach »bezahlbarem« Wohnraum. In den Großstädten und hochpreisigen Regionen konzentriert sich die Bautätigkeit in erster Linie auf die Fertigstellung von Eigentumswohnungen bzw. teuren Mietwohnungen, während zu wenige preiswerte Wohnungen gebaut werden. Sind eine größere staatliche Förderung oder eine Lockerung bestehender baulicher Vorschriften Strategien zur Sicherung eines ausreichenden Angebots an bezahlbarem Wohnraum