Measuring the Economic Significance of Structural Exchange Rate Models

This paper examines both the in-sample and out-of-sample performance of three monetary fundamental models of exchange rates and compares their out-of-sample performance to that of a simple Random Walk model. Using a data-set consisting of five currencies at monthly frequency over the period January 1980 to December 2009 and a battery of newly developed performance measures, the paper shows that monetary models do better (in-sample and out-of-sample forecasting) than a simple Random Walk model.

Stability of Growth Models with Generalised Lag Structures

This paper considers the lag structures of dynamic models in economics, arguing that the standard approach is too simple to capture the complexity of actual lag structures arising, for example, from production and investment decisions. It is argued that recent (1990s) developments in the the theory of functional differential equations provide a means to analyse models with generalised lag structures. The stability and asymptotic stability of two growth models with generalised lag structures are analysed. The paper concludes with some speculative discussion of time-varying parameters.

Forward Looking and Myopic Regional Computable General Equilibrium Models. How Significant is the Distinction?

We present a stylized intertemporal forward-looking model able that accommodates key regional economic features, an area where the literature is not well developed. The main difference, from the standard applications, is the role of saving and its implication for the balance of payments. Though maintaining dynamic forward-looking behaviour for agents, the rate of private saving is exogenously determined and so no neoclassical financial adjustment is needed. Also, we focus on the similarities and the differences between myopic and forward-looking models, highlighting the divergences among the main adjustment equations and the resulting simulation outcomes.

Action of colchicine on hepatic schistosomal granuloma

Amorphous material and altered collagen fragments within dilated secretory vesicles and cisternae of fibroblast cytoplasm were the main ultrastructural changes seen in hepatic periovular granulomas formed in mice infected with Schistosoma mansoni and treated with colchicine. Despite promoting ultrastructural changes in the fibroblasts found in hepatic periovular granulomas, colchicine administration to infected mice did not significantly change the light microscopic appearance of the hepatic schistosomal lesions, did not diminish the amount of total hepatic collagen, and did not change the collagen isotypes in the granulomas, as observed after a comparative study with non-colchicine treated infected control mice. When administered to mice two weeks after curative treatment of schistosomiasis with praziquantel, colchicine did not seem to increase extracellular collagen degradation or to induce a more rapid resorption of hepatic periovular granulomas, although still promoting ultrastructura alterations in fibroblasts.

No Good Deals - No Bad Models

Faced with the problem of pricing complex contingent claims, an investor seeks to make his valuations robust to model uncertainty. We construct a notion of a model- uncertainty-induced utility function and show that model uncertainty increases the investor's eff ective risk aversion. Using the model-uncertainty-induced utility function, we extend the \No Good Deals" methodology of Cochrane and Sa a-Requejo [2000] to compute lower and upper good deal bounds in the presence of model uncertainty. We illustrate the methodology using some numerical examples.

Identification of particular groups of microRNAs that positively or negatively impact on beta cell function in obese models of type 2 diabetes.

AIMS/HYPOTHESIS: MicroRNAs are key regulators of gene expression involved in health and disease. The goal of our study was to investigate the global changes in beta cell microRNA expression occurring in two models of obesity-associated type 2 diabetes and to assess their potential contribution to the development of the disease. METHODS: MicroRNA profiling of pancreatic islets isolated from prediabetic and diabetic db/db mice and from mice fed a high-fat diet was performed by microarray. The functional impact of the changes in microRNA expression was assessed by reproducing them in vitro in primary rat and human beta cells. RESULTS: MicroRNAs differentially expressed in both models of obesity-associated type 2 diabetes fall into two distinct categories. A group including miR-132, miR-184 and miR-338-3p displays expression changes occurring long before the onset of diabetes. Functional studies indicate that these expression changes have positive effects on beta cell activities and mass. In contrast, modifications in the levels of miR-34a, miR-146a, miR-199a-3p, miR-203, miR-210 and miR-383 primarily occur in diabetic mice and result in increased beta cell apoptosis. These results indicate that obesity and insulin resistance trigger adaptations in the levels of particular microRNAs to allow sustained beta cell function, and that additional microRNA deregulation negatively impacting on insulin-secreting cells may cause beta cell demise and diabetes manifestation. CONCLUSIONS/INTERPRETATION: We propose that maintenance of blood glucose homeostasis or progression toward glucose intolerance and type 2 diabetes may be determined by the balance between expression changes of particular microRNAs.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

A toxicokinetic model to assess exposure to folpet fungicide from urinary biomarkers

Folpet is one of the most widely employed fungicides in agriculture. It is typically used in the culture of vegetables, fruits and ornamental plants. Once absorbed in the human body, it has been found to be very reactive, especially in acid conditions. According to various in vitro and in vivo experiments in animals, Folpet is first fractioned at the N-S link when in contact with aqueous solutions and thiol groups. From this non-enzymatic process a phthalimide (PI) molecule is formed, which may be used as a biomarker of exposure, along with the short-lived thiophosgene. We have built a human toxicokinetic model to account for the biotransformation of Folpet into PI and its subsequent excretion while accounting for other non-monitored metabolites. The mathematical parameters of the model were determined accordingly from best-fits to the time courses of PI in blood and urine of five volunteers administered orally 1 mg/kg and dermally 10 mg/kg of Folpet. In both cases, the mean elimination half-life of PI from the body (either through faeces, urine or metabolism) was found to be 31.6 h. The average final fractions of administered dose recovered in urine as PI were 0.025% and 0.002%, for oral and dermal administration, respectively after 96 h. According to the model, when orally administered, PI rapidly hydrolyzes to phthalamic and phthalic acids such that only 0.04% of the PI found in the gastrointestinal tract is absorbed into the blood stream. Likewise, after dermal application, model predicts that only 7.4% of the applied Folpet dose crosses the epidermis. In the model, the PI initial metabolite of Folpet is formed in the dermis and further metabolized prior to reaching systemic circulation, such that only 0.125% of PI formed at the site-of-entry reaches systemic blood. Our mathematical model is in accordance with both measures of blood (R2=0.57 for dermal and R2=0.66 for oral) and urine (R2 =0.98 for dermal and R2=0.99 for oral).

Model Uncertainty in Panel Vector Autoregressive Models.

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

Model Uncertainty in Panel Vector Autoregressive Models.

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

The convenient calculation of some test statistics in models of discrete choice

The paper considers the use of artificial regression in calculating different types of score test when the log

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

Computing Markov-Perfect Optimal Policies in Business-Cycle Models

Time-inconsistency is an essential feature of many policy problems (Kydland and Prescott, 1977). This paper presents and compares three methods for computing Markov-perfect optimal policies in stochastic nonlinear business cycle models. The methods considered include value function iteration, generalized Euler-equations, and parameterized shadow prices. In the context of a business cycle model in which a scal authority chooses government spending and income taxation optimally, while lacking the ability to commit, we show that the solutions obtained using value function iteration and generalized Euler equations are somewhat more accurate than that obtained using parameterized shadow prices. Among these three methods, we show that value function iteration can be applied easily, even to environments that include a risk-sensitive scal authority and/or inequality constraints on government spending. We show that the risk-sensitive scal authority lowers government spending and income-taxation, reducing the disincentive households face to accumulate wealth.

Hyperplasia of elastic tissue in hepatic schistosomal fibrosis

Elastic tissue hyperplasia, revealed by means of histological, immunocytochemical and ultrastructural methods, appeared as a prominent change in surgical liver biopsies taken from 61 patients with schistosomal periportal and septal fibrosis. Such hyperplasia was absent in ecperimental murine schistosomiasis, including mice with "pipe-stem" fibrosis. Displaced connective tissue cells in periportal areas, such as smooth muscle cells, more frequently observed in human material, could be the site of excessive elastin synthesis, and could explain the differences observed in human and experimental materials. Elastic tissue, sometimes represented by its microfibrillar components, also appeared to be more condensed in areas of matrix (collagen) degradation, suggesting a participation of this tissue in the remodelling of the extracellular matrix. By its rectratile properties elastic tissue hyperplasia in hepatic schistosomiasis can cause vascular narrowing and thus play a role in the pathogenesis of portal hypeertension.