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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.

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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.

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

This doctoral thesis is devoted to the study of the causal effects of the maternal smoking on the delivery cost. The interest of economic consequences of smoking in pregnancy have been studied fairly extensively in the USA, and very little is known in European context. To identify the causal relation between different maternal smoking status and the delivery cost in the Emilia-Romagna region two distinct methods were used. The first - geometric multidimensional - is mainly based on the multivariate approach and involves computing and testing the global imbalance, classifying cases in order to generate well-matched comparison groups, and then computing treatment effects. The second - structural modelling - refers to a general methodological account of model-building and model-testing. The main idea of this approach is to decompose the global mechanism into sub-mechanisms though a recursive decomposition of a multivariate distribution.

This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model phenomena in many disciplines. The definition of heavy tails based on the theory of regular variation highlights the importance of the tail index, which indicates the existence of moments and characterises the rate at which the tail decays. Two new approaches to make inference for the tail index are proposed. The first approach employs a regression technique and constructs an estimator of the tail index. It exploits the fact that the behaviour of the characteristic function near the origin reflects the behaviour of the distribution function at infinity. The main advantage of this approach is that it utilises all observations to constitute each point in the regression, not just extreme values. Moreover, the approach does not rely on prior information on the starting point of the tail behaviour of the underlying distribution and shows excellent performance in a wide range of cases: Pareto distributions, heavy-tailed distributions with a non-constant slowly varying factor, and composite distributions with heavy tails. The second approach is motivated by the asymptotic properties of a special moment statistic, the so-called partition function. This statistic considers blocks of data and is generally used in the context of multifractality. Due to the interplay between the weak law of large numbers and the generalised central limit theorem, the asymptotic behaviour of the partition function is strongly affected by the existence of moments even for weakly dependent samples. Via a quantity, the scaling function, a graphical method to identify the existence of heavy tails is proposed. Moreover, the plot of the scaling function allows one to make inference for the underlying distribution: with infinite variance, finite variance with tail index larger than two, or all moments finite. Furthermore, since the tail index is reflected at the breakpoint of the plot of the scaling function, this gives the possibility to estimate the tail index. Both these two approaches use the entire distribution, not just the tail, to analyse the tail behaviour. This sheds a new light on the analysis of heavy-tailed distributions. At the end of this thesis, these two approaches are used to detect power laws in empirical data sets from a variety of fields and contribute to the debate on whether city sizes are better approximated by a power law or a log-normal distribution.