This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)

This paper presents a theoretical growth model that extends the Mankiw-Romer-Weil [MRW] model by accounting for technological interdependence among regional economies. Interdependence is assumed to work through spatial externalities caused by disembodied knowledge diffusion. The transition from theory to econometrics leads to a reduced-form empirical spatial Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system of 198 regions across 22 European countries over the period from 1995 to 2004 is used to empirically test the model. Testing is performed by assessing the importance of cross-region technological interdependence, and measuring direct and indirect (spillover) effects of the MRW determinants on regional output. (author's abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. We provide an econometric derivation of the relationship and show that the presence of latent/unobservable regional knowledge capital leads to a model relationship that includes both spatial and technological dependence. This model specification accounts for both spatial and technological dependence between regions, which allows us to quantify spillover impacts arising from both types of interaction. Sample data on 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)

This paper employs a spatial Durbin model for analyzing the impact of human capital on regional productivity using for 198 NUTS-2 European regions for the sample period from 1995 to 2004. The study provides evidence for the existence of spatial externalities and interactions of the sort as emphasized by new growth theory. To interpret results meaningfully, we calculate summary measures that account for the simultaneous feedback nature of the underlying model. By sampling from the parameter distribution we present measures of dispersion, revealing that it is relative regional advantages in human capital that matter most for productivity growth. (authors' abstract)

The efficient market hypothesis states that stock prices fully reflect availablei nformation and that stocks thereby always are priced correctly. Hence, it should be impossible to predict future prices in the stock market, and investors will gain no benefits from engaging themselves into historical analyzes. This is a quantitative study which aim to investigate if there is any difference in market efficiency in Nordic stock markets during and after the financial crisis of 2008. By applying various statistical methods, such as unitroot tests, autocorrelation tests and runs test on the returns from each country’s leading market index, the study tries to find evidence for or against the weak form of market efficiency. The study finds evidence both for and against weak form market efficiency but concludes that there is no distinct difference in market efficiency during and after the financial crisis.

This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural solution, supported by formal probabilistic reasoning, is the use of Bayesian model averaging which assigns probabilities on the model space and deals with model uncertainty by mixing over models, using the posterior model probabilities as weights. This paper proposes to adopt Bayesian information criterion model weights since they have computational advantages over fully Bayesian model weights. The approach is illustrated for both identifying model covariates and unveiling spatial structures present in pan-European growth data. (authors' abstract) / Series: Department of Economics Working Paper Series

Cette thèse est consacrée aux résultats asymptotiques pour les modèles ARX en poursuite adaptative. Elle est constituée de quatre parties. La première partie est une brève introduction sur les modèles ARMAX et un état de l’art des principaux résultats de la littérature en poursuite adaptative. La seconde partie porte sur l’introduction d’un nouveau concept de contrôlabilité forte pour les modèles ARX en poursuite adaptative. Il permet de généraliser les résultats antérieurs. On montre la convergence presque sûre des algorithmes des moindres carrés ordinaires et pondérés. On établit également le théorème de la limite centrale ainsi que la loi du logarithme itéré pour ces deux algorithmes. La troisième partie est dédiée aux modèles ARX qui ne sont pas fortement contrôlables. On montre que, via un contrôle de poursuite excité, il est possible de s’affranchir de l’hypothèse de forte contrôlabilité. La quatrième partie est consacrée au comportement asymptotique de la statistique de Durbin-Watson pour les modèles ARX en poursuite adaptative via des arguments martingales. / This thesis is devoted to asymptotical results for ARX models in adaptive tracking. It is divided into four parts. The first part is a short introduction on ARMAX models together with a state of the art on the main results in the literature on adaptive tracking. The second part deals with a new concept of strong controllability for ARX models in adaptive tracking. This new notion allows us to extend the previous convergence results. We prove the almost sure convergence for both least squares and weighted least squares algorithms. We also establish a central limit theorem and a law of iterated logarithm for these two algorithms. The third part is dedicated to ARX models that are not strongly controllable. Thanks to a persistently excited adaptive tracking control, we show that it is possible to get rid of the strong controllability assumption. The fourth part deals with the asymptotic behaviour of the Durbin-Watson statistic for ARX models in adaptive tracking via a martingale approach.

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Public bureaucracy finds itself in a strange place at the intersection of political science and public administration. Political science finds that, within representative democracy, discretion granted to bureaucrats threatens the nature of democracy by subverting politicians who represent the will of the people – bureaucracy vs democracy. At the same time, public administration holds that, in the interest of promoting democracy, bureaucracy should be objective in its implementation of policy in a way that eliminates the influence of politics from decision-making – politics vs bureaucracy. Those positions are seemingly contradictory in nature. From one perspective, bureaucracy is undemocratic because it is outside of politics, yet an overreach of politics into the bureaucracy yields undemocratic outcomes. Bureaucracy can facilitate democracy outside of politics. This study looks to empirically test whether local bureaucrats, who should be willing to act in-line with influential co-partisans, might still promote democratic outcomes for their constituents with their discretionary decision-making. Florida provides an empirical backdrop for testing bureaucracy’s impact on democracy with a natural experimental scenario created with the passing of new early voting limitations in 2011. Florida’s Republican (R) lawmakers passed House Bill 1355 (HB 1355), which was signed into law by Governor Scott (R), that dramatically limited the early voting days allowed for federal elections. HB 1355 changed the early voting (EV) period from fourteen (14) days to eight (8) days and eliminated the last Sunday before Election Day as well. The move was widely seen as a political calculation aimed at stifling the participation of Democrats in the 2012 General Election. In seeming lockstep, local Supervisors of Elections (SOEs) from both parties utilized their statutory discretion over the location of early voting sites to alter the distribution of sites before the 2012 General Election. I find that Republican SOEs did not distribute early voting locations in a way that negatively impacted early voting participation rates (EVPR) for their local precincts. Furthermore, I find that, all else equal, their decisions did not statistically impact EVPR differently than the EVPR in communities managed by Democrats. Republican SOEs did not add new costs to voters in their communities. I provide new evidence that demonstrates that bureaucrats can indeed limit the impact of undue politics from their influential co-partisans to promote more democratic outcomes.

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