This thesis presents a creative and practical approach to dealing with the problem of selection bias. Selection bias may be the most important vexing problem in program evaluation or in any line of research that attempts to assert causality. Some of the greatest minds in economics and statistics have scrutinized the problem of selection bias, with the resulting approaches – Rubin’s Potential Outcome Approach(Rosenbaum and Rubin,1983; Rubin, 1991,2001,2004) or Heckman’s Selection model (Heckman, 1979) – being widely accepted and used as the best fixes. These solutions to the bias that arises in particular from self selection are imperfect, and many researchers, when feasible, reserve their strongest causal inference for data from experimental rather than observational studies. The innovative aspect of this thesis is to propose a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The approach involves the construction of a multi-dimensional conditional space of the X matrix in which the bias associated with the treatment assignment has been eliminated. Specifically, we propose the use of a partial dependence analysis of the X-space as a tool for investigating the dependence relationship between a set of observable pre-treatment categorical covariates X and a treatment indicator variable T, in order to obtain a measure of bias according to their dependence structure. The measure of selection bias is then expressed in terms of inertia due to the dependence between X and T that has been eliminated. Given the measure of selection bias, we propose a multivariate test of imbalance in order to check if the detected bias is significant, by using the asymptotical distribution of inertia due to T (Estadella et al. 2005) , and by preserving the multivariate nature of data. Further, we propose the use of a clustering procedure as a tool to find groups of comparable units on which estimate local causal effects, and the use of the multivariate test of imbalance as a stopping rule in choosing the best cluster solution set. The method is non parametric, it does not call for modeling the data, based on some underlying theory or assumption about the selection process, but instead it calls for using the existing variability within the data and letting the data to speak. The idea of proposing this multivariate approach to measure selection bias and test balance comes from the consideration that in applied research all aspects of multivariate balance, not represented in the univariate variable- by-variable summaries, are ignored. The first part contains an introduction to evaluation methods as part of public and private decision process and a review of the literature of evaluation methods. The attention is focused on Rubin Potential Outcome Approach, matching methods, and briefly on Heckman’s Selection Model. The second part focuses on some resulting limitations of conventional methods, with particular attention to the problem of how testing in the correct way balancing. The third part contains the original contribution proposed , a simulation study that allows to check the performance of the method for a given dependence setting and an application to a real data set. Finally, we discuss, conclude and explain our future perspectives.

The intensity of regional specialization in specific activities, and conversely, the level of industrial concentration in specific locations, has been used as a complementary evidence for the existence and significance of externalities. Additionally, economists have mainly focused the debate on disentangling the sources of specialization and concentration processes according to three vectors: natural advantages, internal, and external scale economies. The arbitrariness of partitions plays a key role in capturing these effects, while the selection of the partition would have to reflect the actual characteristics of the economy. Thus, the identification of spatial boundaries to measure specialization becomes critical, since most likely the model will be adapted to different scales of distance, and be influenced by different types of externalities or economies of agglomeration, which are based on the mechanisms of interaction with particular requirements of spatial proximity. This work is based on the analysis of the spatial aspect of economic specialization supported by the manufacturing industry case. The main objective is to propose, for discrete and continuous space: i) a measure of global specialization; ii) a local disaggregation of the global measure; and iii) a spatial clustering method for the identification of specialized agglomerations.

No description available.

PROBLEM In the last few years farm tourism or agritourism as it is also referred to has enjoyed increasing success because of its generally acknowledged role as a promoter of economic and social development of rural areas. As a consequence, a plethora of studies have been dedicated to this tourist sector, focusing on a variety of issues. Nevertheless, despite the difficulties of many farmers to orient their business towards potential customers, the contribution of the marketing literature has been moderate. PURPOSE This dissertation builds upon studies which advocate the necessity of farm tourism to innovate itself according to the increasingly demanding needs of customers. Henceforth, the purpose of this dissertation is to critically evaluate the level of professionalism reached in the farm tourism market within a marketing approach. METHODOLOGY This dissertation is a cross-country perspective incorporating the marketing of farm tourism studied in Germany and Italy. Hence, the marketing channels of this tourist sector are examined both from the supply and the demand side by means of five exploratory studies. The data collection has been conducted in the timeframe of 2006 to 2009 in manifold ways (online survey, catalogues of industry associations, face-to-face interviews, etc.) according to the purpose of the research of each study project. The data have been analyzed using multivariate statistical analysis. FINDINGS A comprehensive literature review provides the state of the art of the main differences and similarities of farm tourism in the two countries of study. The main findings contained in the empirical chapters provide insights on many aspects of agritourism including how the expectations of farm operators and customers differ, which development scenarios of farm tourism are more likely to meet individuals’ needs, how new technologies can impact the demand for farm tourism, etc. ORIGINALITY/VALUE The value of this study is in the investigation of the process by which farmers’ participation in the development of this sector intersects with consumer consumption patterns. Focusing on this process should allow farm operators and others including related businesses to more efficiently allocate resources.

The thesis studies the economic and financial conditions of Italian households, by using microeconomic data of the Survey on Household Income and Wealth (SHIW) over the period 1998-2006. It develops along two lines of enquiry. First it studies the determinants of households holdings of assets and liabilities and estimates their correlation degree. After a review of the literature, it estimates two non-linear multivariate models on the interactions between assets and liabilities with repeated cross-sections. Second, it analyses households financial difficulties. It defines a quantitative measure of financial distress and tests, by means of non-linear dynamic probit models, whether the probability of experiencing financial difficulties is persistent over time. Chapter 1 provides a critical review of the theoretical and empirical literature on the estimation of assets and liabilities holdings, on their interactions and on households net wealth. The review stresses the fact that a large part of the literature explain households debt holdings as a function, among others, of net wealth, an assumption that runs into possible endogeneity problems. Chapter 2 defines two non-linear multivariate models to study the interactions between assets and liabilities held by Italian households. Estimation refers to a pooling of cross-sections of SHIW. The first model is a bivariate tobit that estimates factors affecting assets and liabilities and their degree of correlation with results coherent with theoretical expectations. To tackle the presence of non normality and heteroskedasticity in the error term, generating non consistent tobit estimators, semi-parametric estimates are provided that confirm the results of the tobit model. The second model is a quadrivariate probit on three different assets (safe, risky and real) and total liabilities; the results show the expected patterns of interdependence suggested by theoretical considerations. Chapter 3 reviews the methodologies for estimating non-linear dynamic panel data models, drawing attention to the problems to be dealt with to obtain consistent estimators. Specific attention is given to the initial condition problem raised by the inclusion of the lagged dependent variable in the set of explanatory variables. The advantage of using dynamic panel data models lies in the fact that they allow to simultaneously account for true state dependence, via the lagged variable, and unobserved heterogeneity via individual effects specification. Chapter 4 applies the models reviewed in Chapter 3 to analyse financial difficulties of Italian households, by using information on net wealth as provided in the panel component of the SHIW. The aim is to test whether households persistently experience financial difficulties over time. A thorough discussion is provided of the alternative approaches proposed by the literature (subjective/qualitative indicators versus quantitative indexes) to identify households in financial distress. Households in financial difficulties are identified as those holding amounts of net wealth lower than the value corresponding to the first quartile of net wealth distribution. Estimation is conducted via four different methods: the pooled probit model, the random effects probit model with exogenous initial conditions, the Heckman model and the recently developed Wooldridge model. Results obtained from all estimators accept the null hypothesis of true state dependence and show that, according with the literature, less sophisticated models, namely the pooled and exogenous models, over-estimate such persistence.

No description available.

Nella presente analisi si è avuta l’eccezionale disponibilità di dati longitudinali su molti individui (6000studenti frequentanti le scuole superiori bolognesi). Per ottenere un modello che al meglio spiegasse e riproducesse l’andamento degli esiti scolastici (promozione alla classe successiva) tenendo conto del percorso scolastico nel tempo, si è scelto il modello a curva latente condizionato. La variabile risposta è combinazione lineare dell’esito di fine anno (Promosso/Non promosso); riassume, per ogni studente/anno scolastico, classe frequentata ed esito finale. Le variabili esplicative sono state selezionate tra le informazioni disponibili per gli individui. Vengono presentati alcuni dati aggregati, poi descritti i dati individuali che entreranno nel modello, evidenziando la composizione degli studenti. La prima fase è la stima del modello logistico, con specificazione delle criticità, che hanno indotto alla scelta successiva del modello dipendente dal tempo. Dopo la descrizione della metodologia principale utilizzata, la teoria del conditionalLCM, e la selezione degli indicatori di fitting, viene delineata la procedura di stima, e raggiunto il modello ottimale. Le variabili significative per spiegare l’andamento delle promozioni individuali nel tempo risultano: cittadinanza (italiani con risultati significativamente migliori degli stranieri), sesso (ragazze con percorso scolastico mediamente migliore dei ragazzi: la differenza risulta però significativa soltanto negli istituti tecnici e professionali), tipologia di scuola frequentata (studenti del liceo con risultati significativamente migliori di chi frequenta altri tipi di istituto). I risultati risultano fortemente dipendenti dai dati impiegati, specie riguardo al limite territoriale. Precedenti analisi evidenziavano una forte differenziazione dei risultati scolastici tra studenti del nord e del sud Italia, oltre che tra studenti dei comuni maggiormente popolati e studenti dei comuni di provincia. Sarebbe interessante disporre di dati individuali analoghi a quelli qui utilizzati, ma riferiti all’intero territorio nazionale, oppure ad un zona maggiormente vasta dell’Italia, onde saggiare l’entità dell’influenza sul percorso scolastico,ed in particolare sulla regolarità, della differenza territoriale.

Il modello gravitazionale e' ormai diventato un "cavallo da battaglia" in economia internazionle ed e' comunemente utilizzato nella determinazione dei flussi commerciali. Recentemente, molti studi hanno mostrato l'importanza della dipendenza spaziale, che va' a considerare quegli effetti dovuti al cosiddetto "third country". Intervengono a questo scopo la modellistica e le tecniche di stima di Econometria Spaziale. Verra' fatto uso di tali tecniche allo scopo di stimare con un modello gravitazionale spaziale il commercio internazionale tra paesi dell'OCSE per un panel di 22 anni. L'obiettivo e' quindi duplice: da un lato, si andra' ad applicare le piu' moderne tecniche di Econometria Spaziale, in un campo in cui tali contributi scarseggiano. Dall'altro lato,verra' fornita una interpretazione del comportamento del commercio internazionale tra paesi dell'OCSE, approfondendo gli aspetti relativi all'effetto del"third country" e del fenomeno migratorio. Inoltre , viene proposta un'analisi che ha lo scopo di validare l'ipotesi di omissione della distanza dal modello gravitazione strutturale. / The Gravity Model is the "workhorse for empirical studies" in International Economies and it is commonly used in explaining the trade flow between countries. Recently, several studies have showed the importance of taking into account the spatial effect. Spatial Econometric techniques meet this matter, proposing the specification of a set of models and estimators. We will make use of these Spatial Econometric techniques in order to estimate a Spatial Gravity of Trade for a 22-year-long panel of the OECD countries. The aim, therefore, is twofold: on one hand, we are going to use the newest Spatial Econometric techniques in a field where they aren't widely applicated. On the other hand, we provide an updated interpretation of the behaviour of the International Trade in an OECD context, going deeply on the explanation of the spatial spillover effect due to the third country dependence, and of the migratory phenomenon. Moreover, we propose an economically-based analysis whose aim is to avoid the use of the "distance" variable in the Gravity Model. The empirical results confirm the importance of taking into account the spatial dependence and they allow us to estimate the model wirhout the "distance", if properly replaced by a set of fixed effects.

L'obiettivo principale della tesi è lo sviluppo di un modello empirico previsivo di breve periodo che sia in grado di offrire previsioni precise ed affidabili dei consumi di energia elettrica su base oraria del mercato italiano. Questo modello riassume le conoscenze acquisite e l'esperienza fatta durante la mia attuale attività lavorativa presso il Romagna Energia S.C.p.A., uno dei maggiori player italiani del mercato energetico. Durante l'ultimo ventennio vi sono stati drastici cambiamenti alla struttura del mercato elettrico in tutto il mondo. Nella maggior parte dei paesi industrializzati il settore dell'energia elettrica ha modificato la sua originale conformazione di monopolio in mercato competitivo liberalizzato, dove i consumatori hanno la libertà di scegliere il proprio fornitore. La modellazione e la previsione della serie storica dei consumi di energia elettrica hanno quindi assunto un ruolo molto importante nel mercato, sia per i policy makers che per gli operatori. Basandosi sulla letteratura già esistente, sfruttando le conoscenze acquisite 'sul campo' ed alcune intuizioni, si è analizzata e sviluppata una struttura modellistica di tipo triangolare, del tutto innovativa in questo ambito di ricerca, suggerita proprio dal meccanismo fisico attraverso il quale l'energia elettrica viene prodotta e consumata nell'arco delle 24 ore. Questo schema triangolare può essere visto come un particolare modello VARMA e possiede una duplice utilità, dal punto di vista interpretativo del fenomeno da una parte, e previsivo dall'altra. Vengono inoltre introdotti nuovi leading indicators legati a fattori meteorologici, con l'intento di migliorare le performance previsive dello stesso. Utilizzando quindi la serie storica dei consumi di energia elettrica italiana, dall'1 Marzo 2010 al 30 Marzo 2012, sono stati stimati i parametri del modello dello schema previsivo proposto e valutati i risultati previsivi per il periodo dall'1 Aprile 2012 al 30 Aprile 2012, confrontandoli con quelli forniti da fonti ufficiali. / The subject of this thesis is forecasting daily electricity consumption in Italy, at an hourly basis. The analyses presented in this work summarize my working experience at Romagna Energia S.c.P.A. Although several approaches have been proposed in the last two decades, forecasting energy consumption is a recent research subject, especially as concerns the Italian case. Historically, the power sector was characterized by a highly vertically integrated market structure with limited competition. However, during the last two decades, revolutionary changes to the structure of the electricity business have taken place around the world. The electric power industry in many countries all over the world is moving from a centralized operational approach to a liberalized one. Italy makes not exception. Modeling and predicting electricity consumption play an important role in developed and developing countries for policy makers and related organizations. In this thesis I provide a twofold contribution. First, I examine in detail the institutional features of the Italian Energy Market and its stylized facts. Second, since electricity prices are formed at an hourly basis, I focus on daily load dynamics at hourly frequency and propose a novel time series empirical forecasting model which: • features a ‘triangular scheme’, i.e. a VARMA-type structure which is suggested by the physical mechanism through which electricity is produced and delivered over the 24 hours which allows one to exploit the possible inefficiencies that might characterize the grid; • includes new leading indicators related to weather conditions. The sample used for parameters estimation is the time series of italian electricity consumptions from March 1st, 2010 to March 31st, 2012, corresponding to 18285 observations. Finally, the forecasting models results for the load of April, 2012, are compared with the official Italian authority forecast.

The concept of competitiveness, for a long time considered as strictly connected to economic and financial performances, evolved, above all in recent years, toward new, wider interpretations disclosing its multidimensional nature. The shift to a multidimensional view of the phenomenon has excited an intense debate involving theoretical reflections on the features characterizing it, as well as methodological considerations on its assessment and measurement. The present research has a twofold objective: going in depth with the study of tangible and intangible aspect characterizing multidimensional competitive phenomena by assuming a micro-level point of view, and measuring competitiveness through a model-based approach. Specifically, we propose a non-parametric approach to Structural Equation Models techniques for the computation of multidimensional composite measures. Structural Equation Models tools will be used for the development of the empirical application on the italian case: a model based micro-level competitiveness indicator for the measurement of the phenomenon on a large sample of Italian small and medium enterprises will be constructed.