We present results from an ensemble of eight climate models, each
of which has carried out simulations
of the early Eocene climate optimum (EECO, ∼ 50 million years
ago). These simulations have been ...carried out in the framework of the Deep-Time Model Intercomparison Project
(DeepMIP; http://www.deepmip.org, last access: 10 January 2021); thus, all models have been configured with the same
paleogeographic and vegetation boundary conditions. The results indicate that
these non-CO2 boundary conditions contribute between 3 and
5 ∘C to Eocene warmth. Compared with
results from previous studies, the DeepMIP simulations generally show a reduced spread of the global mean surface temperature response across the ensemble for a given atmospheric CO2 concentration as well as an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with the preindustrial period mostly arises from decreases in emissivity due to the elevated CO2 concentration (and associated water vapour and long-wave cloud feedbacks), whereas the reduction in the Eocene in terms of the meridional temperature gradient is primarily due to emissivity and albedo changes owing to the non-CO2 boundary conditions (i.e. the removal of the Antarctic ice sheet and changes in vegetation). Three of the models (the Community Earth System Model, CESM; the Geophysical Fluid Dynamics Laboratory, GFDL, model; and the Norwegian Earth System Model, NorESM) show results that are consistent with the proxies in terms of the global mean temperature, meridional SST gradient, and CO2, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale, the models lack skill. In particular, the modelled anomalies are substantially lower than those indicated by the proxies in the southwest Pacific; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxies or models in this region. Our aim is that the documentation of the large-scale features and model–data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean
circulation, hydrological cycle, and modes of variability, and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols.
This paper examines a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation for this model and analyze asymptotic properties of the estimator based on ...the spatial near-epoch dependence of the dependent variable process generated from the model structure. We show that the maximum likelihood estimator is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of the estimator.
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This paper studies model selection consistency for high dimensional sparse regression when data exhibits both cross-sectional and serial dependency. Most commonly-used model selection methods fail to ...consistently recover the true model when the covariates are highly correlated. Motivated by econometric and financial studies, we consider the case where covariate dependence can be reduced through the factor model, and propose a consistency strategy named Factor-Adjusted Regularized Model Selection (FarmSelect). By learning the latent factors and idiosyncratic components and using both of them as predictors, FarmSelect transforms the problem from model selection with highly correlated covariates to that with weakly correlated ones via lifting. Model selection consistency, as well as optimal rates of convergence, are obtained under mild conditions. Numerical studies demonstrate the nice finite sample performance in terms of both model selection and out-of-sample prediction. Moreover, our method is flexible in the sense that it pays no price for weakly correlated and uncorrelated cases. Our method is applicable to a wide range of high dimensional sparse regression problems. An R-package FarmSelect is also provided for implementation.
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We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the ...random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when this assumption is not met.
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Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under ...time-homogeneity conditions that are like "time is randomly assigned" or "time is an instrument." Partial-identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial-identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations.
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A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing ...Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood- based, weighted GEE, multiple imputation, and Bayesian methods. The book's subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: * Multiple data sets that can be replicated using the SAS®, Stata®, R, and WinBUGS software packages * Numerous examples of case studies in the field of biostatistics to illustrate real-world scenarios and demonstrate applications of discussed methodologies * Detailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an idealresource for health science researchers and applied statisticians.
Economic Geography is the most complete, up-to-date textbook available on the important new field of spatial economics. This book fills a gap by providing advanced undergraduate and graduate students ...with the latest research and methodologies in an accessible and comprehensive way. It is an indispensable reference for researchers in economic geography, regional and urban economics, international trade, and applied econometrics, and can serve as a resource for economists in government. The book presents advances in economic theory that explain why, despite the increasing mobility of commodities, ideas, and people, the diffusion of economic activity is very unequal and remains agglomerated in a limited number of spatial entities. The book complements theoretical analysis with detailed discussions of the empirics of the economics of agglomeration, offering a mix of theoretical and empirical research that gives a unique perspective on spatial disparities. It reveals how location continues to matter for trade and economic development, yet how economic integration is transforming the global economy into an economic space in which activities are performed within large metropolitan areas exchanging goods, skills, and information.
The book describes an integrated theory that links estuary shape to tidal hydraulics, tidal mixing and salt intrusion. The shape of an alluvial estuary is characterised by exponentially varying width ...and the absence of bottom slope. This topography is closely related to tidal parameters, hydraulic parameters and parameters that describe 1-dimensional mixing and salt intrusion. Starting from the fundamental equations for conservation of mass and momentum, analytical equations are derived that relate the topography to tidal parameters (tidal excursion, phase lag, tidal damping, tidal amplification), wave celerity, lateral and vertical mixing and salt intrusion. The book presents a review of the state of the art, a comprehensive theoretical background and ample case illustrations from all over the world. It provides tools with which human interference in estuary dynamics can be described and predicted, resulting from, for instance: upstream fresh water abstraction, dredging, climate change or sea-level rise. In describing the interactions between tide, topography, water quality and river discharge, it provides useful information for hydraulic engineers, morphologists, ecologists and people concerned with water quality in alluvial estuaries. Although the book can be used as a text book, it is mainly a monograph aimed at graduate students and researchers.* Provides new integrated theory for tidal hydraulics, tidal mixing and salt intrusion in alluvial estuaries * Presents a consistent set of analytical equations to compute tidal movement, tidal mixing and salt intrusion, derived from the fundamental laws of conservation of mass and momentum * Serves as a practical guide with many illustrations of applications in real estuaries
There exist two alternative assumptions to identify local average treatment effects (LATE) in fuzzy regression discontinuity (RD) designs: local independence (LI) and local smoothness (LS). Together ...with the usual LATE assumptions requiring existence of a first‐stage and treatment monotonicity, either of these two assumptions is sufficient to identify RD LATE. I discuss the practical (and testable) implications of these alternative assumptions, and show that weakening LI by LS might be empirically relevant. However, when LI does hold, there are some practical implications one may explore. Numerical and empirical examples are briefly presented.
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This paper places the key issues and implications of the new 'introductory' book on spatial econometrics by James LeSage & Kelley Pace (2009) in a broader perspective: the argument in favour of the ...spatial Durbin model, the use of indirect effects as a more valid basis for testing whether spatial spillovers are significant, the use of Bayesian posterior model probabilities to determine which spatial weights matrix best describes the data, and the book's contribution to the literature on spatio-temporal models. The main conclusion is that the state of the art of applied spatial econometrics has taken a step change with the publication of this book.
Relever le niveau de l'économetrie spatial appliquée
RÉSUMÉ La présente communication place les principales questions et implications du nouvel ouvrage d'introduction sur l'économétries spatiale de James LeSage & Kelley Pace (2009) dans un contexte plus général: l'argument favorisant le modèle spatial de Durbin, l'emploi d'effets indirects comme base plus valable pour évaluer l'aspect significatif des déversements spatiaux, l'emploi des probabilités d'un modèle baysien postérieur pour évaluer laquelle des matrices de poids spatiaux décrit le mieux les donnes, et la contribution de l'ouvrage la documentation sur les modèles spatio-temporels. La principale conclusion est qu'avec la publication de cet ouvrage, l'état de l'art de l'économétries spatiale applique a effectué un grand pas en avant.
Alzar el nivel de la econometría espacial aplicada
RÉSUMÉ Este trabajo plantea las cuestiones e implicaciones clave del nuevo libro introductorio sobre económetra espacial de James LeSage & Kelley Pace (2009) dentro de una perspectiva más amplia: el argumento a favor del modelo espacial Durbin, el uso de efectos indirectos como una base más válida para poner a prueba si los desbordamientos espaciales son significativos, el uso de probabilidades posteriores bayesianas para descubrir que matriz de pesos espaciales describe mejor los datos, y la contribución del libro a la bibliógrafa sobre modelos espaciotemporales. La principal conclusión es que la econometría espacial aplicada más avanzada ha experimentado un cambio radical con la publicación de este libro.
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