Benchmarking corresponds to a combination of two sources of information on a given variable. In many situations, the problem consists of combining a series of frequent data with a series of less frequent but more accurate data for producing more accurate estimates of the former series. For example, estimates of population characteristics are derived from the last census and researchers re-estimate the values for the time gap between two censuses using more regular information. In what follows we focus in the .' problem of benchmarking monthly data with annual estimates; then, the benchmarking consists of forcing the sum of the monthly signals to equal the signal of the benchmark. Alternative estimators have been proposed in the literature for benchmarking. When the adjusted series agrees exactly with these benchmarks, the benchmarking is called binding. The binding process is implemented by setting the variance of. the annual survey errors to zero. However, it is necessary to account for the variance of the annual survey errors when computing the variances of the benchmarked estimators. In this thesis, we develop the theoretical expression of the correct variance as well as an expression for the excess in the variance due to the binding process. The results are extended to the most known bepchmarking methods proposed in the literature. An application to business surveys used for official statistics in the UK is presented, illustrating some particular issues regarding the state space modelling. Finally, the problem of how to prepare tabular data classified by attributes as columns and points in time as rows is analyzed. This multivariate extension of the benchmarking problem distinguishes two basic type of problems: when only marginal totals are available (contemporaneous disaggregation) and when the aggregates do not correspond with the sum of the disaggregated values by year and/or by attributes (reconciliation). The scope of this thesis is based basically in a state space model approach.

This thesis examines the principal economic factors explaining firms' foreign direct investment (FDI) location decisions into 13 Central and Eastern European countries (CEECs) between 1997 and 2007 using discrete choice econometric methods. The first part employs Meta-analysis to systematically summarise, integrate and synthesise the results of empirical studies that analyse two main reasons why multinational enterprises (MNEs) locate their investment abroad: access to foreign markets and reducing productions costs. A large number of factors related to model specifications, dataset characteristics and methodologies in the primary studies explain the variation in the estimates of the market size and labour costs effects on FDI across the studies. Furthermore, the existing empirical literature on the market size effect on FDI is prone to publication bias more than the literature on the labour costs effect on FDI, as papers with statistically significant and larger market size effect on FDI are more inclined to be published in international journals. The second part employs four alternative discrete choice methodologies, including the Mixed logit (ML) model and the Latent Class (LC) model approaches to capture the main locational determinants of over a 1000 individual firm-level FDI location decisions in 13 CEECs between 1997 and 2007. The results show that the choice where abroad to invest does not only depend on the opportunities offered by foreign markets and industries but also on investing firms' individual characteristics. These results support the presence of heterogeneity in the investment location decisions, which is not only revealed by statistically significant interaction terms, but also by statistically significant standard deviations of the random parameters in the ML model and statistically significant class-specific explanatory variables in the LC model.

The global growth of sport and major sporting events as tourism and mass entertainment in both single and multi sport formats has prompted the desire for a greater understanding of event attendees and the implications of their motivations and decisions to travel and attend major sporting events. However, research into major sporting events has generally focused on the Olympic Games and/or attendance of a single major sporting event. Currently, the major sporting event community sees the value of measuring the economic impact of major sporting events but do not understand the decisions taken by individuals that travel to and attend major sporting events due to the lack of research in the area. Thus, more robust and comprehensive research needs to be carried out to improve the understanding of individuals that travel to and attend a range of major sporting events. The purpose of this research was to develop a better understanding of the total spend and trip duration decisions of individuals that travel to and attend major sporting events with commercial companies. The research used a positivist quantitative strategy to empirically assess research questions surrounding repeat major sporting event attendance, motivations for major sporting event attendance, variables affecting total spending and trip duration decisions and relationships the between motivations, trip duration and total spending, and to econometrically model findings. Independent variables for analysis were identified through a review of literature, which informed the construction of both a conceptual model and online survey focusing on demographics, event related motivations, major sporting event profile and sporting involvement. Variable-based data collected from individual respondents on nine different major sporting events then underwent a two stage descriptive and statistical analysis. The descriptive analysis consisted of a quantitative breakdown of survey results and the statistical analysis allowed the data to be econometrically modeled and assessed through regression analysis. The research provided significant findings towards understanding the decisions taken by individuals that travel to and attend major sporting events and in doing so led to a greater understanding of total spend and trip duration decisions. Findings indicated that the demographic variables and event related motivations determined total spend decisions whilst demographic variables, event related motivations and major sporting event profile variables determined trip duration decisions. Directly implicated in these findings were relevant key variables for commercial companies to consider in the packaging and sale of major event sport travel to an existing and committed customer base. Furthermore, the results can be extended and applied to populations within a broader sport event community such as managers, planners and evaluators to enhance the economic impact of major sporting events through a better understanding of event attendees.

Globally manufacturing based industries are typically transforming operations to enhance the delivery of services throughout equipment use. Within the defence industry, Contracting for Availability (CfA) has emerged as an approach that is increasingly dominating the interation between the customer and the manufacturers. This application serves as an example for an Industrial Product-Service System, and sets the context to this research. Predicting the delivery of services, particularly at the bidding stage, creates enhanced complexity and unpredictability in costs due to uncertainties. Driven by this contextual challenge the aim of this research is to develop a framework for cost uncertainty management and modelling at the bidding stage of CfA in the defence industry. The thesis presents the existing literature associated to uncertainty in cost estimation, whilst the current practice is demonstrated based on interaction with seven organisations involved in the defence industry. A software prototype, Uncertainty Tool for Assessment and Simulation of Cost (U-TASC), has been developed to implement an integrated cost uncertainty management and modelling framework. The cost uncertainty management framework offers a systematic procedure at the bidding stage to guide subject matter experts to focus the attention on influential uncertainties, while also proposing suitable mitigation strategies. In contrast, the cost uncertainty modelling framework involves a step by step procedure to make use of subjective opinion collated from subject matter experts to reflect the influence of uncertainty in cost estimates. The thesis also presents an agent based model that takes into account the influence of dynamic uncertainty (e.g. failure rate) on cost estimates over time. This is applied within a service supply network, where the interaction between the stakeholders represents a typical CfA with incentives and risk sharing scenarios. The frameworks embedded in U-TASC are validated and verified through three case studies including, a naval radar, aircraft carrier, and naval electronic system. The outcomes indicate that reliable and useful results are generated and the tool is highly applicable. On the other hand, the framework for the agent based model is validated through expert opinion and a pilot case study in the naval domain.

This thesis documents the research and findings in the following three related areas of financial econometrics: The first essay examines whether volatility contains information to predict the likelihood of a price jump during the next trading day. It is motivated by the theoretical model of Bansal & Shaliastovich (2008) who develop a long-run learning model, arguing that market volatility should be able to predict the likelihood of jumps. I use S&P 500 futures prices and extensions of the GARCH jump model of Maheu & McCurdy (2004) to relate jump probabilities to conditional volatility. Since volatility is a latent variable, which can be measured using different variables, I consider predictions based upon squared daily return, at-the-money implied volatility, model-free im- plied volatility and high-frequency realized volatility. I find evidence that volatility can predict jump likelihood and the best predictive variable is the model-free implied volatility: which is constructed using cross-section of option prices. Therefore, this thesis contributes to the current literature by documenting the information efficiency of option prices when predicting the future likelihood of jumps. In addition. I also develop a new approach based on Poisson regression which compares the jump intensity obtained from the GARCH jump model with the intraday jump numbers counted using the method of Andersen et al. (2007b). I find the two measures of jumps match fairly well with each other in the period from 1990 to 1997. However, any such relationship seems to disappear in the later period from 1998 to 2004. The second essay is motivated by the affine jump-diffusion model of Duffie et al. (2000), which allows jump intensity to be an affine function of state variables. I examine whether volatility can predict the intensity of price jumps in stochastic volatility jump models, estimated using Markov Chain Monte Carlo simulation. Comparing implied volatility with high-frequency realized volatility, I find allowing the jump intensity to be an affine function of model-free implied volatility yields the best model, based on either the Deviance Information Criterion or on diagnostic tests. Further comparison are made for candidate AR(l) process which specify the stochastic volatility. I find a jump model with the log variance an AR( 1) process performs better than a jump model with Ornstein-Uhlenbeck stochastic volatility. In a Monte Carlo simulation, I find the Deviance Information Criterion is a reliable criterion to differentiate between competing equity price dynamics when there are price jumps and volatility is stochastic. In addition to examining univariate equity return models, in the third essay I also develop a bivariate equity return model which simultaneously captures time-varying correlation and volatility spillovers in the international equity markets. This model is calibrated using the weekly equity index returns from the US. UK, Germany, India and Brazil stock markets and it is compared with simplier model specifications. I find evidence that supports time varying correlation between equity markets in both developed and developing economics. How- ever, the volatility spillovers mainly exist from US equity returns to equity returns in other economies. This thesis concludes with a short discussion of its limitations and future research directions.

This study aims to examine the usefulness of econometric models with stochastic volatility and long memory in the application of macroeconomic and financial time series. An ARFIMA-FIAPARCH process is used to estimate the two main parameters driving the degree of persistence in the US real interest rate and its uncertainty. It provides evidence that the US real interest rates exhibit dual long memory and suggests that much more attention needs to be paid to the degree of persistence and its consequences for the economic theories which are still inconsistent with the finding of either near-unit-root or long memory mean-reverting behavior. A bivariate GARCH-type of model with/without long-memory is constructed to concern the issue of temporal ordering of inflation, output growth and their respective uncertainties as well as all the possible causal relationships among the four variables in the US/UK, allowing several lags of the conditional variances/levels used as regressors in the mean/variance equations. Notably, the findings are quite robust to changes in the specification of the model. The applicability and out-of-sample forecasting ability of a multivariate constant conditional correlation FIAPARCH model are analysed through a multi-country study of national stock market returns. This multivariate specification is generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, both the optimal fractional differencing parameter and power transformation are remarkably similar across countries.

The aim of this thesis is to contribute to the understanding of demand for physical activity. Given the government‟s target to increase the proportion of the population who are physically active, we need to know the determinants of demand for physical activity in order to identify target areas for policy. The relevant components of the demand function for physical activity, which were identified from reviews of theoretical and empirical literature on physical activity behaviour, established the need to account for costs (i.e. time and money costs) and perceived benefits among other factors in explaining physical activity behaviour. To date, there is a paucity of studies looking at this issue particularly from an economic perspective, mainly due to the lack of such data. This thesis therefore focussed on fitting varied econometric models (sample selection, count, linear, and probit) to understand how costs and perceived benefits explain indicators of physical activity behaviour (total time spent, number of days, and meeting the recommended level of participation or not); controlling for socio-economic, demographic and psychological variables. Data was sourced from the Health Survey for England (2006), Health Education Authority National Survey of Activity and Health (1991), and face-face interviews conducted in 2008 using a purposive sample. The findings suggest that time and money prices (costs per occasion of participation) of physical activity are inversely correlated with physical activity, and this is mitigated where the perceived benefits of physical activity, both health and non-health, are high. Indicators of demand were price inelastic except for meeting the recommended level of participation, which was highly responsive to changes in time price. Based on the findings, various policies including the use of economic instruments such as subsidies, particularly at the point of consumption, and mass media campaigns to increase awareness about the benefits of physical activity are discussed.

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This study explores the magnitude of size and value premium in the UK using the various methods of estimating SMB and HML used in the past on UK data. I identify nine distinct methods that previous researchers have used to estimate the factors. I then estimate them, following as closely as possible the descriptions given in the relevant papers, on data from July 1980 to April 2003. I find that the method used to construct the size and value premium affects their magnitude and significance. I, then, use a variety of asset-pricing tests to examine whether the estimated factors capture risk effects and, combined with the market factor, explain returns for sets of portfolios. Consistent with the US evidence, I show that the Fama and French model outperforms the CAPM model in the UK: Moreover, I find that the HML is the dominant risk factor in the UK while the 5MB is generally insignificant. Next, I consider the macroeconomic story for the size and BM effects. My purpose is to clarify the extent to which Fama-French factors contain information about the macroeconomy. I find that three out of four sets of Fama-French factors - that come with positive and significant risk premium on HML from the cross-sectional regressions support the U.S. evidence that HML contains default-related information, consistent with Vassalou and Xing (2004). However, only one set of factors shows that shocks to IP growth is a significant risk factor but contains different information from the HML factor, in contrast to the Vassalou (2003) and Liew and Vassalou (2000) findings for the U.S. Further, I compare the pricing performance of four asset pricing models with respect to their abilities to explain the cross-sectional variation in different sets of portfolios' excess returns. I find that the Fama-French model explains the cross-section of average returns on portfolios sorted on size and BM ratio better than both the Vassalou model and the Macroeconomic model for the 16 intersected size and BM portfolios and for the set of 36 combined portfolios. However, the result is mixed regarding the 20 industry portfolios. I conclude that given the absence of 'industry standards' for 5MB and HML construction in the UK and my findings of a real difference between the outcomes of applying different sets of factors, caution needs to be taken when adopting just one of the combinations of Fama-French factors and then drawing inferences accordingly. Moreover, I stress the need for more research to uncover the full information content of Fama-French factors.