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22 pages ; International audience ; There is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so. ; La littérature économétrique récente fait une place croissante à l'étude des propriétés asymptotiques des différentes méthodes d'estimation des modèles de données de panel spatio-temporels. Toutefois, force est de constater que peu d'attention est consacrée à l'interprétation économique de tels modèles malgré leur grand intérêt pour la modélisation des phénomènes économiques dans une dimension spatio-temporelle et le rôle qu'ils pourraient jouer dans l'évaluation des politiques économiques dans cette même dimension. Nous montrons dans ce papier que les coefficients estimés de ces modèles permettent d'expliciter non seulement la dynamique temporelle des impacts mais également leur dynamique spatiale et surtout de quantifier la diffusion spatio-temporelle de l'impact d'une variation d'une ...

22 pages ; International audience ; There is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so. ; La littérature économétrique récente fait une place croissante à l'étude des propriétés asymptotiques des différentes méthodes d'estimation des modèles de données de panel spatio-temporels. Toutefois, force est de constater que peu d'attention est consacrée à l'interprétation économique de tels modèles malgré leur grand intérêt pour la modélisation des phénomènes économiques dans une dimension spatio-temporelle et le rôle qu'ils pourraient jouer dans l'évaluation des politiques économiques dans cette même dimension. Nous montrons dans ce papier que les coefficients estimés de ces modèles permettent d'expliciter non seulement la dynamique temporelle des impacts mais également leur dynamique spatiale et surtout de quantifier la diffusion spatio-temporelle de l'impact d'une variation d'une variable explicative. La méthode proposée est illustrée par une étude de la demande de cigarettes dans 46 Etats américains sur la période 1963-1992 en utilisant une base de données bien connue dans la littérature économétrique. La présence d'autocorrélation spatiale est ici motivée par un effet de " contrebande ". Les consommateurs proches des frontières d'un état achèteront en effet leurs cigarettes dans les états voisins si le prix des cigarettes y est inférieur à celui pratiqué dans leur propre Etat.

The Clean Development Mechanism (CDM) allows emission reduction (or emission removal) projects in developing countries to earn Certified Emission Reduction (CER) credits, each equivalent to one tonne of CO2. These CERs can be traded and sold, and used by industrialized countries to meet a part of their emission reduction targets under the Kyoto Protocol. The mechanism stimulates sustainable development and emission reductions, while giving industrialized countries some flexibility in how they meet their emission reduction limitation targets. Accepted projects must qualify through a rigorous and public registration and issuance process designed to ensure real, measurable and verifiable emission reductions that are additional to what would have occurred without the project. Between November 2004 and May 2009, the mechanism has registered 1,653 projects and is anticipated to currently produce CERs amounting to 303 106 tonnes of CO2 equivalent yearly. The mechanism is extremely interesting since it is the first global, environmental investment and credit scheme of its kind, providing a standardized emissions offset instrument. However the geographical distribution of the CDM projects is revealing very large differences in between developing countries since China, India, Brazil and South-Korea totalise 82 % of the CERs while Africa only represents 3,3% of the total.

The task of this work is to discuss issues conceming the specification, estimation, inference and forecasting in multivariate dynamic heterogeneous panel data models from a Bayesian perspective. Three essays linked by a few conraion ideas compose the work. Multivariate dynamic models (mainly VARs) based on micro or macro panel data sets have become increasingly popular in macroeconomics, especially to study the transmission of real and monetary shocks across economies. This great use of the panel VAR approach is largely justified by the fact that it allows the docimientation of the dynamic impact of shocks on key macroeconomic variables in a framework that simultaneously considers shocks emanating from the global enviromnent (world interest rate, terms of trade, common monetary shock) and those of domestic origin (supply shocks, fiscal and monetary policy, etc.). Despite this empirical interest, the theory for panel VAR is somewhat underdeveloped. The aim of the thesis is to shed more light on the possible applications of the Bayesian framework in discussing estimation, inference, and forecasting using multivariate dynamic models where, beside the time series dimensión we can also use the information contained in the cross sectional dimensión. The Bayesian point of view provides a natural environment for the models dlscussed in this work, due to its flexibility in combining diíferent sources of information. Moreover, it has been recently shown that Bayes estimates of hierachical dynamic panel data models have a reduced small sample bias, and help in improving the forecasting performance of these models.

Non-linearities and dynamic interactions between state variables are characteristic of complex social systems and processes. In this thesis, we present a new methodology to model these non-linearities and interactions from the large panel datasets available for some of these systems. We build macro-level statistical models that can verify theoretical predictions, and use polynomial basis functions so that each term in the model represents a specific mechanism. This bridges the existing gap between macro-level theories supported by statistical models and micro-level mechanistic models supported by behavioural evidence. We apply this methodology to two important problems in the social sciences, the demographic transition and the transition to democracy. The demographic transition is an important problem for economists and development scientists. Research has shown that economic growth reduces mortality and fertility rates, which reduction in turn results in faster economic growth. We build a non-linear dynamic model and show how this data-driven model extends existing mechanistic models. We also show policy applications for our models, especially in setting development targets for the Millennium Development Goals or the Sustainable Development Goals. The transition to democracy is an important problem for political scientists and sociologists. Research has shown that economic growth and overall human development transforms socio-cultural values and drives political institutions towards democracy. We model the interactions between the state variables and find that changes in institutional freedoms precedes changes in socio-cultural values. We show applications of our models in studying development traps. This thesis comprises the comprehensive summary and seven papers. Papers I and II describe two similar but complementary methodologies to build non-linear dynamic models from panel datasets. Papers III and IV deal with the demographic transition and policy applications. Papers V and VI describe the transition to democracy and applications. Paper VII describes an application to sustainable development.

Awarding Certified Emission Reductions to Clean Development Mechanism (CDM) projects under the Kyoto Protocol involves comparing the actual emissions of the project to a counterfactual "baseline" emissions level. The baseline of a project is intended to represent the emissions that would have occurred in the absence of the project. Accordingly, there is a high degree of uncertainty in determining CDM baselines. In the years since the emergence of the CDM, scientific and political debates have occurred about how best to cut through this uncertainty and arrive at the fixed, quantitative figures necessary for the quantification and commodification of greenhouse gas emissions. My research explores the decision-making process by which practical quantifying conventions are adopted in order to narrow the operational realm of uncertainty. It focuses on the role of scientists and the use of scientific rhetoric in the negotiation process and also explores risk and the perceptions of risk held by a range of important actors. This exploratory paper is intended to serve as a springboard for future work by providing a description of the nature of the uncertainty in CDM baselines, and by exploring the ramifications of decision-making in the face of this uncertainty. It concludes with a list of future questions that probe the ways in which different actors may be expected to maneuver within this uncertainty.

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics ; The increasing global attention to greenhouse emissions and the recent creation of EU Emission Trading Scheme has clearly suggested the need of consistent methods to value projects aimed to reduce gases. This need particularly concerns companies that have to find a way to both remain profitable and conform to new legal requirements. Multiple ways of cutting emission costs are available nowadays: short term abatement measures, which primary involve switching production machinery from coal to gas; long term abatement measures, which envisage the implementation of new types of projects .e.g Clean Development Mechanism or Joint Implementation Mechanism suggested by Kyoto Protocol -. In this work we study the impact of the introduction of both kinds of policy in a pricing model for CO2 allowances.

The article aims at examination of the shape of relationship between income inequality and the level of economic development measured by GDP per capita in 27 European Union countries in the period of 2004-2014. It also aims at identification of determinants of income inequality. Specifically, we test for the existence of an inverted U-shaped curve, as it is predicted by the standard Kuznets hypothesis, and J-shaped curve following the approach adopted by Deutsch and Silber (2004) and Anand and Kanbur (1993). The data come from Eurostat EU-SILC database (European Union Statistics on Income and Living Conditions), World Bank and International Monetary Fund. In the EU-27 group of countries we contradict the Kuznets hypothesis – our results provide evidence for a U-shaped, rather than the inverted U relationship. It also follows from our analysis that our data cover only the descending part of the U, that is a shape of inverted J.

This article considers the determinants of Portuguese tourism demand for the period 2004-2013. The econometric methodology uses a panel unit root test and the dynamic panel data (GMM-system estimator). The different techniques of panel unit root (Levin, Lin and Chu; Im, Pesaran and Shin W-stat and augmented Dickey-Fuller - Fisher Chi-square) show that the variables used in this panel are stationary. The dynamic model proves that tourism demand is a dynamic process. The variables relative prices, income per capita, human capital and government spending encourage international tourism demand for Portugal. ; info:eu-repo/semantics/publishedVersion

This study examined the influence of government corporate tax policy on the performance of 54 randomly selected listed companies that cut across 17 categories of non-fi nancial fi rms in Nigeria over a period of 1990-2002. Using Generalised Method of Moment (GMM) and contrary to the expectation, the study found positive and significant relationship between corporate tax policy and the output performance of quoted manufacturing fi rms in Nigeria. This may be an indication that government revenue from corporate tax was judiciously expended on productive government expenditure such as road, security and power as nearly all fi rms selected are located in Lagos State. The study therefore, suggested that corporate tax if judiciously used in the provision of physical infrastructures and other public goods would reduce the cost of production of the private sector in Nigeria.

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai (2009) and Moon and Weidner (2015), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. Further, we demonstrate that our shrinkage estimators of the nonzero slope parameters exhibit some oracle property. We conduct Monte Carlo simulations to demonstrate the superb finite-sample performance of the proposed method. We apply our method to study the determinants of economic growth and find that in addition to three common unobserved factors selected by our method, government consumption share has negative effects, whereas investment share and lagged economic growth have positive effects on economic growth.

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon and Weidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. Further, we demonstrate that our shrinkage estimators of the nonzero slope parameters exhibit some oracle property. We conduct Monte Carlo simulations to demonstrate the superb finite-sample performance of the proposed method. We apply our method to study the determinants of economic growth and find that in addition to three common unobserved factors selected by our method, government consumption share has negative effects, whereas investment share and lagged economic growth have positive effects on economic growth.

In diesem Diskussionspapier wird die Möglichkeit dargestellt, durch eine maßgebliche Nutzung des Clean Development Mechanism (CDM) die vorgegebenen Klimaschutzziele möglichst effizient, also mit möglichst geringen Kosten zu erreichen. Eine effiziente Reduzierung der CO2-Emissionen hilft, die für die Erreichung umweltpolitischer Ziele verbundenen Kosten auf das unvermeidbare Maß zu begrenzen und somit die Zustimmung der Bevölkerung zu ehrgeizigen Klimaschutzzielen nicht leichtfertig zu gefährden.

Der Clean Development Mechanism (CDM) des Kyoto-Protokolls hat eine zweifache Zielsetzung. Erstens soll damit den Industrieländern erlaubt werden, in emissionssparende Projekte in Entwicklungsländern zu investieren und sich die so erzielte Emissionsreduktion auf ihre Kyoto-Ziele anzurechnen. Denn dort sind Emissionsreduktionen oft kostengünstiger zu erzielen als in Industrieländern. Zweitens sollen die CDM-Projekte zur nachhaltigen Entwicklung der Gastgeberländer beitragen. Inzwischen sind weltweit über 2.000 CDM-Projekte genehmigt und weitere Tausende sind beantragt. Der Mechanismus hat damit eine Dimension erreicht, in der er wesentliche Wirkungen auslösen kann - zum Positiven oder zum Negativen. Laut Kyoto-Protokoll sind die beiden oben genannten Ziele gleichrangig. Es wird jedoch nur der Aspekt der erzielten Emissionsreduktionen durch ein internationales Verfahren reguliert und überwacht. Die Entscheidung, ob ein Projekt zur nachhaltigen Entwicklung beiträgt, obliegt allein den Gastgeberländern, die bisher jegliche Vorschläge, auch für diese Frage ein internationales Verfahren einzurichten, abgelehnt hatten. Jedes Entwicklungsland hat daher ganz eigene Kriterien für die Bewertung der Nachhaltigkeit seiner Projekte entwickelt, doch meist ohne die erforderlichen Kapazitäten, um Projekte wirklich zu prüfen. Die hier vorgestellte Diplomarbeit von Bea Wittger untersucht diese Problematik am Beispiel Brasilien. Brasilien beheimatet weltweit die drittmeisten CDM-Projekte und hat den Ruf, ein besonders gründliches Genehmigungsverfahren durchzuführen. Doch auch in der brasilianischen Zivilgesellschaft wird teils harsche Kritik am CDM geübt. Die Diplomarbeit wurde an der Universität zu Köln vorgelegt und von Wolfgang Sterk betreut. Sie nimmt eine detaillierte Analyse der brasilianischen Genehmigungsverfahren und -praxis vor, erstens anhand ihrer inhärenten Konsistenz und Stringenz und zweitens anhand von zwei Projektbeispielen. ; The Clean Development Mechanism (CDM) of the Kyoto Protocol has two targets: Firstly to enable the industrialised countries to invest in emission saving projects in developing countries thus influencing the achievement account of the Kyoto targets at home. Secondly the CDM-projects are to contribute to the sustainable development of the host countries. However each developing country has developed its own individual system of assessing the sustainability of its projects, mostly without the capacity needed to effectively survey the projects. The here presented diploma thesis written by Bea Wittger analyses this problem taking Brazil as an example. Focusing on two project examples and taking into account the inherent consistency and stringency the analysis explores in detail the Brazilian approval procedures and practices.

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.