1,721,403 research outputs found
Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects
No full textThe present paper suggests an estimation procedure for a Cliff and Ord type spatial panel data model with random effects. Building on existing literature, the paper suggests an estimation procedure that (i) considers all the moment conditions in Kapoor et al. (2007) and (ii) allows for the presence of explanatory variables that do not vary over time. Our Monte Carlo results demonstrate that the estimation procedure proposed in this paper is very effective
Spillover effects in spatial models: Generalizations and extensions
No full textIn this paper, we consider two different cases of spillover effects: the first is a case where the model does not contain any additional endogenous regressors. Related to this, we give a variance formula that can be used to make inference for estimated spillover effects. In the second case, we extend the discussion of spillover effects to a model containing additional endogenous variables. We suggest approximations to the spillover effects that according to the results of our Monte Carlo experiment are quite accurate. However, our variance formula can not be determined unless the equations of the entire system are known. Suggestions for approximations in this case are left for future research
Splm: Spatial panel data models in R
Full text linksplm is an R package for the estimation and testing of various spatial panel data specifications. We consider the implementation of both maximum likelihood and generalized moments estimators in the context of fixed as well as random effects spatial panel data models. This paper is a general description of splm and all functionalities are illustrated using a well-known example taken from Munnell (1990) with productivity data on 48 US states observed over 17 years. We perform comparisons with other available software; and, when this is not possible, Monte Carlo results support our original implementation
splm: Econometric analysis of panel data
No full textWe illustrate the new splm package, aimed at providing a comprehensive resource for spatial panel econometrics. The package fills a gap in applied practice, as the relevant estimators and tests are well established in the literature but to date they lack user-friendly and widely available software implementations.
Building on the infrastructure for spatially referenced data in package spdep, we provide estimators for the standard panel models in the spatial econometrics literature: fixed and random effects with either a
spatial lag or spatial correlation in the error term, based on both the concurrent approaches prevailing in the literature, i.e. the Maximum Likelihood framework pioneered by Anselin (1988) and the Generalized Moments framework of Kapoor, Kelejian and Prucha (2007).
Some of the model estimation procedures are generalized to the case of spatially and serially correlated
error terms. GM estimators for systems of equations are also available.
We also provide the Lagrange Multiplier joint, marginal and conditional specification tests from the work
of Baltagi et al. (2003, 2007).
The user interface aims at consistency w.r.t. the spatial (non-panel) estimators in package spdep and the panel (non-spatial) estimators in package plm.
We briefly discuss code optimization aspects of the computationally heavy Maximum Likelihood routines
that have up to now hindered the practical implementation of these estimators. The GM approach, on its part, yields very fast estimators that can be applied to comparatively big datasets.
We conclude with an empirical illustration on a well-known data set from the panel data literature
Ethnobotanical knoweledge in the agro-pastoral world of Putifigari village (Sardinia, Italy)
No full textSpatial J-test: Some Monte Carlo evidence
No full textResearchers using spatial econometric methods generally assume a known structure for the process being modeled embedded in a spatial weights matrix. The present paper evaluates the performance of the J-test in selecting the most appropriate spatial structure in the context of a Monte Carlo study. Results suggest that the J-test performs well when used to select between different weights matrices. Increases in power are associated with the use of the full set of instruments. © 2010 Springer Science+Business Media, LLC
Environmental product declarations as a data source for the assessment of environmental impacts during the use phase of photovoltaic modules: critical issues and potential
Full text linkIn the context of policies promoting renewable energies for decarbonization, energy transition and the development of energy communities, photovoltaic systems require special attention. Even for these systems, it is legitimate to inquire about the correlation, currently carried out through life cycle analysis, between benefits and environmental impacts. To maintain long-term productivity levels and ensure the proper functioning of the system, maintenance interventions are necessary. While these interventions guarantee performance, they also have repercussions for the environment. This study aims to assess the environmental impacts caused by ordinary and extraordinary maintenance interventions, taking into account specific factors, during the 30-year operational phase. To evaluate these impacts, this study verifies the feasibility of using data from Environmental Product Declaration (EPD) and the Product Category Rules (PCR) as reference. The initial results highlight, on the one hand, among the main issues, the importance that all EPDs attribute to the impacts caused by water consumption during the use phase of the PV modules, and on the other hand, some critical issues mainly due to the lack of data relating to the installation site necessary for the correct planning of maintenance activities. Finally, the study presents some reflections for a potential recalibration of the PCR and their associated EPDs
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