The activated product of the c-MYC proto-oncogene is one of the strongest known activators of carcinogenesis. It has been estimated that as many as one-seventh of all cancer deaths are associated with alterations in the c-MYC gene or its expression [1]. Therefore, understanding the regulation of c-MYC expression is a key factor in understanding carcinogenesis in many histologic classes of malignancy. The nuclease hypersensitive element (NHE) III₁ region of the c-MYC promoter has been shown to be particularly important in regulating c-MYC expression. Specifically, the formation of a G-quadruplex structure appears to promote repression of c-MYC transcription. In this dissertation, we investigate the role that nucleolin, a critical player in ribosome biogenesis and cell stress sensing, plays on the transcriptional regulation of the c-MYC promoter through its interaction with the c-MYC G-quadruplex structure. Our studies initiated with the design of a c-MYC G-quadruplex affinity column intended to trap potential c-MYC G-quadruplex-binding proteins that were then identified by LC-MS/MS. After careful examination of the literature of the list of potential c-MYC G-quadruplexbinding proteins, we realized that several of the proteins identified had been previously reported to interact directly with nucleolin. Consequently, we chose to focus our studies on nucleolin, as it could be a central regulator of the (NHE) III region. By performing chromatin immunoprecipitation in HeLa cells, we found that nucleolin indeed interacts with the c-MYC promoter region containing the NHE III₁ element. This binding activity was confirmed by both electromobility shift assay and polymerase stop assay. We provide evidence that nucleolin can induce the formation of the c-MYC G-quadruplex structure from single-stranded DNA, both in linear and circular DNA forms. We show that upon binding, nucleolin increases the stability of the c-MYC G-quadruplex structure leading to repression of c-MYC promoter activity. We also show that nucleolin binds with much higher affinity to G-quadruplex structures with topology similar to that of the parallel c-MYC G-quadruplex, such as those found in the VEGF and PDGF-A promoters; in comparison to G-quadruplexes found in telomeres or the c-MYB promoter, whose have significantly different topology. Interestingly, we also demonstrate that nucleolin binds with higher affinity to the c-MYC G-quadruplex than to its consensus RNA substrate, the nucleolin recognition element (NRE). Furthermore, we show that the C-terminal domain of nucleolin is critical for its interaction and stabilization of the c-MYC G-quadruplex structure. Lastly, we show that the binding of nucleolin to the (NHE) III region causes repression of c-MYC transcription. On the basis of these results, we propose that nucleolin may play an important role in the transcriptional regulation of c-MYC in vivo by inducing the formation of the c-MYC G-quadruplex structure.

The focus here is on the log-normal version of the spatial interaction model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination-flows with formal approaches that allow for two different types of spatial dependence in flow magnitudes. Endogenous interaction reflects situations where there is reaction to feedback regarding flow magnitudes from regions neighboring origin and destination regions. This type of interaction can be modeled using specifications proposed by LeSage and Pace (2008) who use spatial lags of the dependent variable to quantify the magnitude and extent of feedback effects, hence the term endogenous interaction. Exogenous interaction represents a situation where spillover arise from nearby (or perhaps even distant) regions, and these need to be taken into account when modeling observed variation in flows across the network of regions. In contrast to endogenous interaction, these contextual effects do not generate reaction to the spillovers, leading to a model specification that can be interpreted without considering changes in the long-run equilibrium state of the system of flows. We discuss issues pertaining to interpretation of estimates from these two types of model specification, and provide an empirical illustration. (authors' abstract)

Panel data analysis has become an important tool in applied econometrics and the respective statistical techniques are well described in several recent textbooks. However, for an analyst using these methods there remains the task of choosing a reasonable model for the behavior of the panel data. Of special importance is the choice between so-called fixed and random coefficient models. This choice can have a crucial effect on the interpretation of the analyzed phenomenon, which is demonstrated by an application on research and development spillover. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

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This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step involves identifying the number and composition of clubs using a space-time panel data model for annual income growth rates in conjunction with Bayesian model comparison methods. A second step uses a Bayesian space-time panel data model to assess how changes in the initial endowments of variables (that explain growth) impact regional income levels over time. These dynamic trajectories of changes in regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in the initial conditions for the clubs that have been identified in the first step. This is in contrast to conventional practice that involves setting the number of clubs ex ante, selecting the composition of the potential convergence clubs according to some a priori criterion (such as initial per capita income thresholds for example), and using cross-sectional growth regressions for estimation and interpretation purposes. (authors' abstract)

This paper concentrates on the central link between productivity and knowledge capital, and shifts attention from firms and industries to regions. The objective is to measure knowledge elasticity effects within a regional Cobb- Douglas production function framework, with an emphasis on knowledge spillovers. The analysis uses a panel of 203 European regions to estimate the effects over the period 1997-2002. The dependent variable is total factor productivity (TFP). We use a region-level relative TFP index as an approximation to the true TFP measure. This index describes how efficiently each region transforms physical capital and labour into outputs. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of interregional knowledge spillovers. We use patents to measure knowledge capital. Patent stocks are constructed such that patents applied at the European Patent Office in one year add to the stock in the following and then depreciate throughout the patents effective life according to a rate of knowledge obsolescence. A random effects panel data spatial error model is advocated and implemented for analyzing the productivity effects. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and adding an important dimension to the discussion, showing that knowledge spillover effects increase with geographic proximity. (authors' abstract)

This article assesses the empirical relationship between per capita income growth fluctuations and the age-structured human capital variations across four groups of geographically clustered developed and developing countries from spatial perspective. We estimate a spatial Vector Autoregressive (VAR) model of income dynamics where the distance between countries is defined on relational space based on their similarity in appropriation tendency of human capital in the production processes. These distances are computed using a newly developed human capital data set which fully characterizes the demographic structure of human capital, and thus underlines the joint relevance of demography and human capital in economic growth. Spatial effects on growth interdependence and complementarity are then explored with respect to the proposed distance metrics. Our results imply that significant cross-country growth interdependence based on human capital distances exists among defined country groups suggesting the need for a cooperative policy programme among them. We also find that the relationship between economic growth and human capital is highly nonlinear as a function of the proposed human capital distance.

Agricultural support levels are at a crossroad with reduced distortions in OECD countries and increasing support for agricultural producers in emerging economies over the last decades. This paper studies the determinants of distortions in the agricultural markets by putting a specific focus on the role of trade policy. Applying various different dynamic panel data estimators and explicitly accounting for potential endogeneity of trade policy agreements, we find that an increase in the number of bilateral free trade agreements exhibits significant short- and long-run distortion reducing effects. By contrast, WTO's Uruguay Agreement on Agriculture has not been able to systematically contribute to a reduction in agriculture trade distortions. From a policy point of view our findings thus point to a lack of effectiveness of multilateral trade negotiations. / Series: Department of Economics Working Paper Series

El objetivo del presente trabajo es analizar la existencia de trampas de pobreza en Argentina. Para ello se estima la dinámica de ingresos a partir de un pseudo panel construido con información brindada por la Encuesta Permanente de Hogares. Esta metodología permite superar los problemas econométricos que enfrenta la estimación de trampas de pobreza: carencia de un panel para un periodo largo de tiempo, attrition y la presencia de errores no clásicos de medición en los ingresos. Los resultados encontrados descartan la existencia de trampas de pobreza debido a no linealidades en la dinámica de ingresos. / The aim of this paper is to analyze the existence of poverty traps in Argentina. In order to do it so, the income dynamic was estimated by using a pseudo panel built from the Encuesta Permanente de Hogares. This methodology allows to overcome econometric challenges involved in testing for the presence of poverty traps: lack of long duration panels, attrition, and measurement error in income. The results do find no evidence for the existence of poverty traps due to nonlinearities in income dynamics.

This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity. (authors' abstract)