A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

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 model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output 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.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Dimensionality of the Rosenberg self-esteem scale and its relationships with the three- and five-factor personality models

The authors investigated the dimensionality of the French version of the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965) using confirmatory factor analysis. We tested models of 1 or 2 factors. Results suggest the RSES is a 1-dimensional scale with 3 highly correlated items. Comparison with the Revised NEO-Personality Inventory (NEO-PI-R; Costa, McCrae, & Rolland, 1998) demonstrated that Neuroticism correlated strongly and Extraversion and Conscientiousness moderately with the RSES. Depression accounted for 47% of the variance of the RSES. Other NEO-PI-R facets were also moderately related with self-esteem.

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.

Financial health, exports, and rm survival: Evidence from UK and French firms

We use firm level data to assess the role of exporting in the link between financial health and rm survival. The data are for the UK and France. We examine whether fi rms at diff erent stages of export activity (starters, exiters, continuers, switchers) react di fferently to changes in financial variables. In general, export starters and exiters experience much stronger adverse e ffects of fi nancial constraints for their survival prospects. By contrast, the exit probability of continuous exporters and export switchers is less negatively a ffected by financial characteristics. These relationships between exporting, finance and survival are broadly similar in the British and French sub-samples.

Gas6 and its receptors are implicated in sepsis as modulators of innate immunity

Gas6 downregulates the activation state of macrophages and thereby their production of proinflammatory cytokines induced by various stimuli. We aimed to determine whether Gas6 is involved in sepsis. We measured Gas6 plasma levels in 13 healthy subjects, 29 patients with severe sepsis, and 18 patients with non-infectious inflammatory diseases. Gas6 level was higher in septic patients than in control groups (P 0.0001). The sensitivity and specificity of Gas6 levels to predict fatal outcome were 83% and 88%. We next investigated whether Gas6 affects cytokine production and outcome in experimental models of endotoxemia and peritonitis in wild-type (WT) and Gas6-/- mice. Circulating levels of Gas6 after LPS 25mg/kg i.p. peaked at 1 hour (P<0.001). Similarly, TNF- was higher in Gas6-/- than in WT mice 1 hour after LPS (P<0.05). Furthermore, 62 anti- and pro-inflammatory cytokines were quantified in plasma after LPS injection. Their levels were globally higher in Gas6-/- plasma after LPS, 47/62 cytokines being at least 50% higher in Gas6-/- than in WT plasma after 1 hour. Mortality induced by 25mg/kg LPS was 25% in WT versus 87% in Gas6-/- mice (P<0.05). LPS-induced mortality in Gas6 receptors Axl-/-, Tyro3-/- and Merkd was also enhanced when compared to WT mice (P<0.001). In peritonitis models (cecal ligation and puncture, CLP, and i.p. injection of E. coli), Gas6 plasma levels increased and remained elevated at least 24 hours. CLP increased mortality in Gas6-/- mice. Finally, we explored the role of Gas6 in LPS-treated macrophages. We found that Gas6 was released by LPS-stimulated WT macrophages and that Gas6-/- macrophages produced more TNF- and IL-6 than WT macrophages. Cytokine release by Gas6-/- macrophages was higher than by WT macrophages (cytokine array). Adjunction of recombinant Gas6 to the culture medium of Gas6-/- macrophages diminished the cytokine production to WT levels. In LPS-treated Gas6-/- macrophages, Akt and Erk1/2 phosphorylation was reduced whereas p38 and NF B activation was enhanced. Thus, in septic patients, elevated Gas6 levels were associated with fatal outcome. In mice, they raised in experimental endotoxemia and peritonitis models, and correlated also with sepsis severity. However, Gas6-/- mice survival in these models was reduced compared to WT. Gas6 secreted by macrophages in response to LPS activated Akt and restrained p38 and NF B activation, thereby dampening macrophage activation. Altogether these data suggest that, during endotoxemia, Gas6-/- mice phenotype resembles that of mice which have undergone PI3K inhibition, indicating that Gas6 is a major modulator of innate immunity.

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.

Blocked autophagy sensitizes resistant carcinoma cells to radiation therapy.

Autophagy or "self eating" is frequently activated in tumor cells treated with chemotherapy or irradiation. Whether autophagy represents a survival mechanism or rather contributes to cell death remains controversial. To address this issue, the role of autophagy in radiosensitive and radioresistant human cancer cell lines in response to gamma-irradiation was examined. We found irradiation-induced accumulation of autophagosomes accompanied by strong mRNA induction of the autophagy-related genes beclin 1, atg3, atg4b, atg4c, atg5, and atg12 in each cell line. Transduction of specific target-siRNAs led to down-regulation of these genes for up to 8 days as shown by reverse transcription-PCR and Western blot analysis. Blockade of each autophagy-related gene was associated with strongly diminished accumulation of autophagosomes after irradiation. As shown by clonogenic survival, the majority of inhibited autophagy-related genes, each alone or combined, resulted in sensitization of resistant carcinoma cells to radiation, whereas untreated resistant cells but not sensitive cells survived better when autophagy was inhibited. Similarly, radiosensitization or the opposite was observed in different sensitive carcinoma cells and upon inhibition of different autophagy genes. Mutant p53 had no effect on accumulation of autophagosomes but slightly increased clonogenic survival, as expected, because mutated p53 protects cells by conferring resistance to apoptosis. In our system, short-time inhibition of autophagy along with radiotherapy lead to enhanced cytotoxicity of radiotherapy in resistant cancer cells.

Climate change effects on animal and plant phylogenetic diversity in southern Africa

Much attention has been paid to the effects of climate change on species' range reductions and extinctions. There is however surprisingly little information on how climate change driven threat may impact the tree of life and result in loss of phylogenetic diversity (PD). Some plant families and mammalian orders reveal nonrandom extinction patterns, but many other plant families do not. Do these discrepancies reflect different speciation histories and does climate induced extinction result in the same discrepancies among different groups? Answers to these questions require representative taxon sampling. Here, we combine phylogenetic analyses, species distribution modeling, and climate change projections on two of the largest plant families in the Cape Floristic Region (Proteaceae and Restionaceae), as well as the second most diverse mammalian order in Southern Africa (Chiroptera), and an herbivorous insect genus (Platypleura) in the family Cicadidae to answer this question. We model current and future species distributions to assess species threat levels over the next 70years, and then compare projected with random PD survival. Results for these animal and plant clades reveal congruence. PD losses are not significantly higher under predicted extinction than under random extinction simulations. So far the evidence suggests that focusing resources on climate threatened species alone may not result in disproportionate benefits for the preservation of evolutionary history.

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.