Convergence in TFP among Italian Regions - Panel Unit Roots with Heterogeneity and Cross Sectional Dependence

This paper performs a number of tests to estimate convergence in total factor productivity (TFP) among Italian regions during the period 1970-2001. We generate the regional TFP series using growth accounting methodologies, and then apply a range of panel unit root tests to analyse the process of convergence. We extend the existing literature by incorporating three main improvements. Firstly, we control for the heterogeneity arising from the different economic structure of each region. Secondly, we account for the cross-sectional dependence due to common shocks or spillovers among different regions at the same time. Finally, we look for clubs of convergence using tests of poolability both on economic and statistical grounds.

Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Primary commodity prices: co-movements, common factors and fundamentals

The behavior of commodities is critical for developing and developed countries alike. This paper contributes to the empirical evidence on the co-movement and determinants of commodity prices. Using nonstationary panel methods, we document a statistically significant degree of co-movement due to a common factor. Within a Factor Augmented VAR approach, real interest rate and uncertainty, as postulated by a simple asset pricing model, are both found to be negatively related to this common factor. This evidence is robust to the inclusion of demand and supply shocks, which both positively impact on the co-movement of commodity prices.Commodity Prices, Panel Estimation, Factor Models

The Global Side of the Investments-Savings Puzzle

In this paper we re-examine the long standing and puzzling correlation between national savings and investment in industrial countries. We apply an econometric methodology that allows us to separate idiosyncratic correlation at the country level from correlation at the global level. In a major break with the existing literature, we find no evidence of a long run relationship in the idiosyncratic components of savings and investment. We also find that the global components in savings and investments commove, indicating that they react to shocks of a global nature.Savings, Investment, Feldstein-Horioka Puzzle, Panel Nonstationarity, Principal Components.

Interest Rate Co-movements, Global Factors and the Long End of the Term Spread

The disconnect between rising short and low long interest rates has been a distinctive feature of the 2000s. Both research and policy circles have argued that international forces, such as global monetary policy (e.g. Rogoff, 2006); international business cycles (e.g. Borio and Filardo, 2007); or a global savings glut (e.g Bernanke, 2005) may be responsible. In this paper, we employ recent advances in panel data econometrics to document the disconnect and link it explicitly to the existence of a global latent factor that dominates the long end of the term spread for the recent period; the saving glut story emerges as the most likely contender for the global factor.Short and Long Interest Rates, Financial Globalization, Panel Data, Factor Models

Domestic vs. International Correlations of Interest Rate Maturities

The association between long and short interest rates is traditionally envisaged from a purely domestic perspective, where it is believed an empirical regularity. Hence, the weakening of this relationship in the first half of the 2000s has represented a conundrum, calling for a reassessment of the term structure and the conduct of monetary policy. Some commentators have called for investigations into the international dimension of this puzzle. Hence, in this paper we employ recent advances in panel data econometrics to investigate the co-movement of interest rate maturities both at the domestic and international levels for a sample of industrial countries. Specifically, we use the Ng (2006) spacings correlations approach to examine interest rates correlations between and within countries. Compared to alternatives, this method does not just estimate bivariate correlations, but also assesses the degree of panel correlation without being restricted by the assumption of either zero or complete panel correlation. We find very small correlations between the different maturities of domestic rates and much higher correlations of international rates. Moreover, international correlations between long rates are significantly higher than those between short rates. These findings suggest a scenario for national monetary policy, where financial globalization may have changed the transmission mechanism, advocating searches for the “missing” yield curve in its international dimension.Financial globalization, yield spread, interest rates, spacings correlations

Multi-objective thermo-economic optimization of biomass retrofit for an existing solar organic Rankine cycle power plant based on NSGA-II

Non-dominated sorting genetic algorithm (NSGA-II) was deployed in this paper for multi-objective thermo-economic optimization of biomass retrofit for an existing solar organic Rankine cycle (ORC) power plant. The existing plant consists of a field of linear Fresnel collectors (LFC), integrated directly with two-tank thermal energy storage (TES) system, which interfaces with ORC power block. The real solar-ORC plant currently runs at Ottana, Italy, albeit with some technical challenges basically due to inconsistent availability of solar irradiation. In order to upgrade the plant, a novel scheme had been proposed to install a biomass unit in parallel to the solar field, such that both LFC/TES and biomass furnace could directly and independently satisfy fractional thermal input requirement of the ORC. Being a retrofit system, existing design parameters of all the already operating units were imposed as equality constraints in this study, and the combustion excess air, as well as pinch point temperature difference of furnace heat exchangers that optimize the hybrid plant were investigated. Results showed that biomass mass flow rate of 0.133 kg/s and investment cost rate of 57 €/h are optimal for the studied biomass retrofit scheme. At this optimum point, excess air was obtained as 56%, furnace heater pinch point temperature difference as 28.8 °C and air pre-heater pinch point temperature difference as 38.5 °C. More generally, results showed that excess air value of less than 100%, furnace heater pinch point temperature difference of less than 80 °C, and air pre-heater pinch point temperature difference of less than 80 °C would optimize the studied biomass retrofit scheme. Keywords: Solar-Biomass power plant, Organic Rankine cycle, Hybrid renewable energy, Multi-objective optimization, Non-dominated sorting genetic algorithm (NSGA-II), Power plant retrofi

Use ofcis- andtrans-Acting Viral Regulatory Sequences to Improve Expression of Human Immunodeficiency Virus Vectors in Human Lymphocytes

AbstractWe compared the efficiency of human immunodeficiency virus (HIV-1) vectors that express a marker gene (chloramphenicol acetyltransferase, CAT) using different promoter elements. In one vector, CAT was expressed under the control of an internal murine leukemia virus (MuLV) long terminal repeat (LTR). In other vectors, CAT production was regulated by the HIV-1 LTR; these vectors also contained the HIV-1tatgene andpolsequences reported to exertcis-acting positive effects on reverse transcription or gene expression. Vectors employing the Tat-driven HIV-1 LTR exhibited up to 500-fold greater CAT expression in Jurkat lymphocytes or human peripheral blood mononuclear cells compared with vectors using the internal MuLV LTR element as a promoter. This difference was not due to improved packaging of the vector RNA into virions, but to an improved level of gene expression in the target cells. Target cell CAT expression was two- to threefold higher for the vector containing thepolsequences and was only slightly less than that seen for atrans-complementedenv-deleted provirus. These results indicate that defective HIV-1 vectors with efficiencies of gene transfer and expression comparable with that of HIV-1 itself are feasible