Multinational Firms’ Heterogeneity in Tax Responsiveness: the Role of Transfer Pricing

In this paper we show that the ability of multinational firms to manipulate transfer prices affects the tax sensitivity of foreign direct investment (FDI). We offer a model of international capital allocation where firms are heterogeneous in their ability to manipulate transfer prices. Perhaps paradoxically, we show that the ability to shift profits can make parent companies' investment more sensitive to host-country tax rates, as long as investors expect fisscal authorities to use price and profit detection methods. We then offer a comprehensive empirical study to test our predictions in the case of Japanese FDI. We exploit the finding that the unobservable ability to manipulate transfer prices is correlated with whole ownership of a±liates and R&D expenditure. Based on country, parent firm and sector characteristics, we estimate an investment equation on a sample of 3614 Japanese affiliates in 49 emerging countries. We obtain a greater semi-elasticity of investment to the statutory tax rate in a±liates that are wholly-owned and that have R&D intensive parents. We interpret these results as indirect evidence that abusive transfer pricing is one of the determinants of FDI activity.

The intra-industry trade for a high-income country: new empirical evidence for Italy

This paper examines the determinants of Italian intra-industry trade in horizontally and vertically differentiated products, using a dataset which eliminates the effects linked to the hypothesis of homogeneity both between countries - when specific industry characteristics are analysed - and between sectors - within the same country. In this way, within limits, we have tried to address an issue raised by Greenaway et al. in 1999 which, in the light of the current state of the literature on intra-industry trade, does not seem to have been explicitly dealt with. Our paper highlights how strong the impact of this hypothesis could be in the analyses of the determinants of intraindustry trade in the case of Italy.

Microsatellites can be misleading: an empirical and simulation study.

It has been long recognized that highly polymorphic genetic markers can lead to underestimation of divergence between populations when migration is low. Microsatellite loci, which are characterized by extremely high mutation rates, are particularly likely to be affected. Here, we report genetic differentiation estimates in a contact zone between two chromosome races of the common shrew (Sorex araneus), based on 10 autosomal microsatellites, a newly developed Y-chromosome microsatellite, and mitochondrial DNA. These results are compared to previous data on proteins and karyotypes. Estimates of genetic differentiation based on F- and R-statistics are much lower for autosomal microsatellites than for all other genetic markers. We show by simulations that this discrepancy stems mainly from the high mutation rate of microsatellite markers for F-statistics and from deviations from a single-step mutation model for R-statistics. The sex-linked genetic markers show that all gene exchange between races is mediated by females. The absence of male-mediated gene flow most likely results from male hybrid sterility.

Choice of Contracts in the British National Health Service: An Empirical Study

Following major reforms of the British National Health Service (NHS) in 1990, the roles of purchasing and providing health services were separated, with the relationship between purchasers and providers governed by contracts. Using a mixed multinomial logit analysis, we show how this policy shift led to a selection of contracts that is consistent with the predictions of a simple model, based on contract theory, in which the characteristics of the health services being purchased and of the contracting parties influence the choice of contract form. The paper thus provides evidence in support of the practical relevance of theory in understanding health care market reform.

Valuing American Derivatives by Least Squares Methods

Least Squares estimators are notoriously known to generate sub-optimal exercise decisions when determining the optimal stopping time. The consequence is that the price of the option is underestimated. We show how variance reduction methods can be implemented to obtain more accurate option prices. We also extend the Longsta¤ and Schwartz (2001) method to price American options under stochastic volatility. These are two important contributions that are particularly relevant for practitioners. Finally, we extend the Glasserman and Yu (2004b) methodology to price Asian options and basket options.

Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.

Technology shocks and aggregate fluctuations in an estimated hybrid RBC model

This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model’s posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland’s (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model’s errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model’s fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

NEW METHODS FOR DETECTION, SUSCEPTIBILITY TESTING AND BIOFILM ERADICATION OF DIFFICULT ORGANISMS

Aujourd'hui, les problèmes des maladies infectieuses concernent l'émergence d'infections difficiles à traiter, telles que les infections associées aux implants et les infections fongiques invasives chez les patients immunodéprimés. L'objectif de cette thèse était de développer des stratégies pour l'éradication des biofilms bactériens (partie 1), ainsi que d'étudier des méthodes innovantes pour la détection microbienne, pour l'établissement de nouveaux tests de sensibilité (partie 2). Le traitement des infections associées aux implants est difficile car les biofilms bactériens peuvent résister à des niveaux élevés d'antibiotiques. A ce jour, il n'y a pas de traitement optimal défini contre des infections causées par des bactéries de prévalence moindre telles que Enterococcus faecalis ou Propionibacterium acnés. Dans un premier temps, nous avons démontré une excellente activité in vitro de la gentamicine sur une souche de E. faecalis en phase stationnaire de croissance Nous avons ensuite confirmé l'activité de la gentamicine sur un biofilm précoce en modèle expérimental animal à corps étranger avec un taux de guérison de 50%. De plus, les courbes de bactéricidie ainsi que les résultats de calorimétrie ont prouvé que l'ajout de gentamicine améliorait l'activité in vitro de la daptomycine, ainsi que celle de la vancomycine. In vivo, le schéma thérapeutique le plus efficace était l'association daptomycine/gentamicine avec un taux de guérison de 55%. En établissant une nouvelle méthode pour l'évaluation de l'activité des antimicrobiens vis-à-vis de micro-organismes en biofilm, nous avons démontré que le meilleur antibiotique actif sur les biofilms à P. acnés était la rifampicine, suivi par la penicilline G, la daptomycine et la ceftriaxone. Les études conduites en modèle expérimental animal ont confirmé l'activité de la rifampicine seule avec un taux de guérison 36%. Le meilleur schéma thérapeutique était au final l'association rifampicine/daptomycine avec un taux de guérison 63%. Les associations de rifampicine avec la vancomycine ou la levofloxacine présentaient des taux de guérisons respectivement de 46% et 25%. Nous avons ensuite étudié l'émergence in vitro de la résistance à la rifampicine chez P. acnés. Nous avons observé un taux de mutations de 10"9. La caractérisation moléculaire de la résistance chez les mutant-résistants a mis en évidence l'implication de 5 mutations ponctuelles dans les domaines I et II du gène rpoB. Ce type de mutations a déjà été décrit au préalable chez d'autres espèces bactériennes, corroborant ainsi la validité de nos résultats. La deuxième partie de cette thèse décrit une nouvelle méthode d'évaluation de l'efficacité des antifongiques basée sur des mesures de microcalorimétrie isotherme. En utilisant un microcalorimètre, la chaleur produite par la croissance microbienne peut être-mesurée en temps réel, très précisément. Nous avons évalué l'activité de l'amphotéricine B, des triazolés et des échinocandines sur différentes souches de Aspergillus spp. par microcalorimétrie. La présence d'amphotéricine Β ou de triazole retardait la production de chaleur de manière concentration-dépendante. En revanche, pour les échinochandines, seule une diminution le pic de « flux de chaleur » a été observé. La concordance entre la concentration minimale inhibitrice de chaleur (CMIC) et la CMI ou CEM (définie par CLSI M38A), avec une marge de 2 dilutions, était de 90% pour l'amphotéricine B, 100% pour le voriconazole, 90% pour le pozoconazole et 70% pour la caspofongine. La méthode a été utilisée pour définir la sensibilité aux antifongiques pour d'autres types de champignons filamenteux. Par détermination microcalorimétrique, l'amphotéricine B s'est avéré être l'agent le plus actif contre les Mucorales et les Fusarium spp.. et le voriconazole le plus actif contre les Scedosporium spp. Finalement, nous avons évalué l'activité d'associations d'antifongiques vis-à-vis de Aspergillus spp. Une meilleure activité antifongique était retrouvée avec l'amphotéricine B ou le voriconazole lorsque ces derniers étaient associés aux échinocandines vis-à-vis de A. fumigatus. L'association échinocandine/amphotéricine B a démontré une activité antifongique synergique vis-à-vis de A. terreus, contrairement à l'association échinocandine/voriconazole qui ne démontrait aucune amélioration significative de l'activité antifongique. - The diagnosis and treatment of infectious diseases are today increasingly challenged by the emergence of difficult-to-manage situations, such as infections associated with medical devices and invasive fungal infections, especially in immunocompromised patients. The aim of this thesis was to address these challenges by developing new strategies for eradication of biofilms of difficult-to-treat microorganisms (treatment, part 1) and investigating innovative methods for microbial detection and antimicrobial susceptibility testing (diagnosis, part 2). The first part of the thesis investigates antimicrobial treatment strategies for infections caused by two less investigated microorganisms, Enterococcus faecalis and Propionibacterium acnes, which are important pathogens causing implant-associated infections. The treatment of implant-associated infections is difficult in general due to reduced susceptibility of bacteria when present in biofilms. We demonstrated an excellent in vitro activity of gentamicin against E. faecalis in stationary growth- phase and were able to confirm the activity against "young" biofilms (3 hours) in an experimental foreign-body infection model (cure rate 50%). The addition of gentamicin improved the activity of daptomycin and vancomycin in vitro, as determined by time-kill curves and microcalorimetry. In vivo, the most efficient combination regimen was daptomycin plus gentamicin (cure rate 55%). Despite a short duration of infection, the cure rates were low, highlighting that enterococcal biofilms remain difficult to treat despite administration of newer antibiotics, such as daptomycin. By establishing a novel in vitro assay for evaluation of anti-biofilm activity (microcalorimetry), we demonstrated that rifampin was the most active antimicrobial against P. acnes biofilms, followed by penicillin G, daptomycin and ceftriaxone. In animal studies we confirmed the anti-biofilm activity of rifampin (cure rate 36% when administered alone), as well as in combination with daptomycin (cure rate 63%), whereas in combination with vancomycin or levofloxacin it showed lower cure rates (46% and 25%, respectively). We further investigated the emergence of rifampin resistance in P. acnes in vitro. Rifampin resistance progressively emerged during exposure to rifampin, if the bacterial concentration was high (108 cfu/ml) with a mutation rate of 10"9. In resistant isolates, five point mutations of the rpoB gene were found in cluster I and II, as previously described for staphylococci and other bacterial species. The second part of the thesis describes a novel real-time method for evaluation of antifungals against molds, based on measurements of the growth-related heat production by isothermal microcalorimetry. Current methods for evaluation of antifungal agents against molds, have several limitations, especially when combinations of antifungals are investigated. We evaluated the activity of amphotericin B, triazoles (voriconazole, posaconazole) and echinocandins (caspofungin and anidulafungin) against Aspergillus spp. by microcalorimetry. The presence of amphotericin Β or a triazole delayed the heat production in a concentration-dependent manner and the minimal heat inhibition concentration (MHIC) was determined as the lowest concentration inhibiting 50% of the heat produced at 48 h. Due to the different mechanism of action echinocandins, the MHIC for this antifungal class was determined as the lowest concentration lowering the heat-flow peak with 50%. Agreement within two 2-fold dilutions between MHIC and MIC or MEC (determined by CLSI M38A) was 90% for amphotericin B, 100% for voriconazole, 90% for posaconazole and 70% for caspofungin. We further evaluated our assay for antifungal susceptibility testing of non-Aspergillus molds. As determined by microcalorimetry, amphotericin Β was the most active agent against Mucorales and Fusarium spp., whereas voriconazole was the most active agent against Scedosporium spp. Finally, we evaluated the activity of antifungal combinations against Aspergillus spp. Against A. jumigatus, an improved activity of amphotericin Β and voriconazole was observed when combined with an echinocandin. Against A. terreus, an echinocandin showed a synergistic activity with amphotericin B, whereas in combination with voriconazole, no considerable improved activity was observed.

Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Corruption and the Military in Politics: Theory and Evidence from around the World

Recent theoretical developments and case study evidence suggests a relationship between the military in politics and corruption. This study contributes to this literature by analyzing theoretically and empirically the role of the military in politics and corruption for the first time. By drawing on a cross sectional and panel data set covering a large number of countries, over the period 1984-2007, and using a variety of econometric methods substantial empirical support is found for a positive relationship between the military in politics and corruption. In sum, our results reveal that a one standard deviation increase in the military in politics leads to a 0.22 unit increase in corruption index. This relationship is shown to be robust to a variety of specification changes, different econometric techniques, different sample sizes, alternative corruption indices and the exclusion of outliers. This study suggests that the explanatory power of the military in politics is at least as important as the conventionally accepted causes of corruption, such as economic development.

Estimating Phillips Curves in Turbulent Times using the ECB’s Survey of Professional Forecasters

This paper uses forecasts from the European Central Bank's Survey of Professional Forecasters to investigate the relationship between inflation and inflation expectations in the euro area. We use theoretical structures based on the New Keynesian and Neoclassical Phillips curves to inform our empirical work. Given the relatively short data span of the Survey of Professional Forecasters and the need to control for many explanatory variables, we use dynamic model averaging in order to ensure a parsimonious econometric speci cation. We use both regression-based and VAR-based methods. We find no support for the backward looking behavior embedded in the Neo-classical Phillips curve. Much more support is found for the forward looking behavior of the New Keynesian Phillips curve, but most of this support is found after the beginning of the financial crisis.

Regime-Switching Cointegration

We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of cointegrating relationships to change when the regime changes. We show how Bayesian model averaging or model selection methods can be used to deal with the high-dimensional model space that results. Our methods are used in an empirical study of the Fisher effect.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.