998 resultados para Creative class
Resumo:
This paper examines sources of cyclical movements in output, inflation and the term structure of interest rates. It employs a novel identification approach which uses the sign of the cross correlation function in response to shocks to catalog orthogonal disturbances. We find that demand shocks are the dominant source output, inflation and term structure fluctuations in six of the G-7 countries. Within the class of demand disturbances, nominal shocks are dominant, but their importance declined after 1982. Furthermore, there are no significant differences in the proportion of term structure variability explained by different structural sources at different horizons.
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We present a polyhedral framework for establishing general structural properties on optimal solutions of stochastic scheduling problems, where multiple job classes vie for service resources: the existence of an optimal priority policy in a given family, characterized by a greedoid(whose feasible class subsets may receive higher priority), where optimal priorities are determined by class-ranking indices, under restricted linear performance objectives (partial indexability). This framework extends that of Bertsimas and Niño-Mora (1996), which explained the optimality of priority-index policies under all linear objectives (general indexability). We show that, if performance measures satisfy partial conservation laws (with respect to the greedoid), which extend previous generalized conservation laws, then theproblem admits a strong LP relaxation over a so-called extended greedoid polytope, which has strong structural and algorithmic properties. We present an adaptive-greedy algorithm (which extends Klimov's) taking as input the linear objective coefficients, which (1) determines whether the optimal LP solution is achievable by a policy in the given family; and (2) if so, computes a set of class-ranking indices that characterize optimal priority policies in the family. In the special case of project scheduling, we show that, under additional conditions, the optimal indices can be computed separately for each project (index decomposition). We further apply the framework to the important restless bandit model (two-action Markov decision chains), obtaining new index policies, that extend Whittle's (1988), and simple sufficient conditions for their validity. These results highlight the power of polyhedral methods (the so-called achievable region approach) in dynamic and stochastic optimization.
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The concept of antibody-mediated targeting of antigenic MHC/peptide complexes on tumor cells in order to sensitize them to T-lymphocyte cytotoxicity represents an attractive new immunotherapy strategy. In vitro experiments have shown that an antibody chemically conjugated or fused to monomeric MHC/peptide can be oligomerized on the surface of tumor cells, rendering them susceptible to efficient lysis by MHC-peptide restricted specific T-cell clones. However, this strategy has not yet been tested entirely in vivo in immunocompetent animals. To this aim, we took advantage of OT-1 mice which have a transgenic T-cell receptor specific for the ovalbumin (ova) immunodominant peptide (257-264) expressed in the context of the MHC class I H-2K(b). We prepared and characterized conjugates between the Fab' fragment from a high-affinity monoclonal antibody to carcinoembryonic antigen (CEA) and the H-2K(b) /ova peptide complex. First, we showed in OT-1 mice that the grafting and growth of a syngeneic colon carcinoma line transfected with CEA could be specifically inhibited by systemic injections of the conjugate. Next, using CEA transgenic C57BL/6 mice adoptively transferred with OT-1 spleen cells and immunized with ovalbumin, we demonstrated that systemic injections of the anti-CEA-H-2K(b) /ova conjugate could induce specific growth inhibition and regression of well-established, palpable subcutaneous grafts from the syngeneic CEA-transfected colon carcinoma line. These results, obtained in a well-characterized syngeneic carcinoma model, demonstrate that the antibody-MHC/peptide strategy can function in vivo. Further preclinical experimental studies, using an anti-viral T-cell response, will be performed before this new form of immunotherapy can be considered for clinical use.
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This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecificationof the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.
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We show that if performance measures in a stochastic scheduling problem satisfy a set of so-called partial conservation laws (PCL), which extend previously studied generalized conservation laws (GCL), then the problem is solved optimally by a priority-index policy for an appropriate range of linear performance objectives, where the optimal indices are computed by a one-pass adaptive-greedy algorithm, based on Klimov's. We further apply this framework to investigate the indexability property of restless bandits introduced by Whittle, obtaining the following results: (1) we identify a class of restless bandits (PCL-indexable) which are indexable; membership in this class is tested through a single run of the adaptive-greedy algorithm, which also computes the Whittle indices when the test is positive; this provides a tractable sufficient condition for indexability; (2) we further indentify the class of GCL-indexable bandits, which includes classical bandits, having the property that they are indexable under any linear reward objective. The analysis is based on the so-called achievable region method, as the results follow fromnew linear programming formulations for the problems investigated.
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We analyze a standard environment of adverse selection in credit markets. In our environment,entrepreneurs who are privately informed about the quality of their projects needto borrow in order to invest. Conventional wisdom says that, in this class of economies, thecompetitive equilibrium is typically inefficient.We show that this conventional wisdom rests on one implicit assumption: entrepreneurscan only access monitored lending. If a new set of markets is added to provide entrepreneurswith additional funds, efficiency can be attained in equilibrium. An important characteristic ofthese additional markets is that lending in them must be unmonitored, in the sense that it doesnot condition total borrowing or investment by entrepreneurs. This makes it possible to attainefficiency by pooling all entrepreneurs in the new markets while separating them in the marketsfor monitored loans.
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Customer choice behavior, such as 'buy-up' and 'buy-down', is an importantphe-nomenon in a wide range of industries. Yet there are few models ormethodologies available to exploit this phenomenon within yield managementsystems. We make some progress on filling this void. Specifically, wedevelop a model of yield management in which the buyers' behavior ismodeled explicitly using a multi-nomial logit model of demand. Thecontrol problem is to decide which subset of fare classes to offer ateach point in time. The set of open fare classes then affects the purchaseprobabilities for each class. We formulate a dynamic program todetermine the optimal control policy and show that it reduces to a dynamicnested allocation policy. Thus, the optimal choice-based policy caneasily be implemented in reservation systems that use nested allocationcontrols. We also develop an estimation procedure for our model based onthe expectation-maximization (EM) method that jointly estimates arrivalrates and choice model parameters when no-purchase outcomes areunobservable. Numerical results show that this combined optimization-estimation approach may significantly improve revenue performancerelative to traditional leg-based models that do not account for choicebehavior.
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This paper examines two principal categories of manipulative behaviour. The term'macro-manipulation' is used to describe the lobbying of regulators to persuadethem to produce regulation that is more favourable to the interests of preparers.'Micro-manipulation' describes the management of accounting figures to produce abiased view at the entity level. Both categories of manipulation can be viewed asattempts at creativity by financial statement preparers. The paper analyses twocases of manipulation which are considered in an ethical context. The paperconcludes that the manipulations described in it can be regarded as morallyreprehensible. They are not fair to users, they involve an unjust exercise ofpower, and they tend to weaken the authority of accounting regulators.
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We characterize the prekernel of NTU games by means of consistency,converse consistency, and five axioms of the Nash type on bilateral problems.The intersection of the prekernel and the core is also characterized with thesame axioms over the class of games where the core is nonempty.
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Kahneman and Tversky asserted a fundamental asymmetry between gains and losses, namely a reflection effect which occurs when an individual prefers a sure gain of $ pz to anuncertain gain of $ z with probability p, while preferring an uncertain loss of $z with probability p to a certain loss of $ pz.We focus on this class of choices (actuarially fair), and explore the extent to which thereflection effect, understood as occurring at a range of wealth levels, is compatible with single-self preferences.We decompose the reflection effect into two components, a probability switch effect,which is compatible with single-self preferences, and a translation effect, which is not. To argue the first point, we analyze two classes of single-self, nonexpected utility preferences, which we label homothetic and weakly homothetic. In both cases, we characterize the switch effect as well as the dependence of risk attitudes on wealth.We also discuss two types of utility functions of a form reminiscent of expected utility but with distorted probabilities. Type I always distorts the probability of the worst outcome downwards, yielding attraction to small risks for all probabilities. Type II distorts low probabilities upwards, and high probabilities downwards, implying risk aversion when the probability of the worst outcome is low. By combining homothetic or weak homothetic preferences with Type I or Type II distortion functions, we present four explicit examples: All four display a switch effect and, hence, a form of reflection effect consistent a single self preferences.
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MHC-peptide tetramers have become essential tools for T-cell analysis, but few MHC class II tetramers incorporating peptides from human tumor and self-antigens have been developed. Among limiting factors are the high polymorphism of class II molecules and the low binding capacity of the peptides. Here, we report the generation of molecularly defined tetramers using His-tagged peptides and isolation of folded MHC/peptide monomers by affinity purification. Using this strategy we generated tetramers of DR52b (DRB3*0202), an allele expressed by approximately half of Caucasians, incorporating an epitope from the tumor antigen NY-ESO-1. Molecularly defined tetramers avidly and stably bound to specific CD4(+) T cells with negligible background on nonspecific cells. Using molecularly defined DR52b/NY-ESO-1 tetramers, we could demonstrate that in DR52b(+) cancer patients immunized with a recombinant NY-ESO-1 vaccine, vaccine-induced tetramer-positive cells represent ex vivo in average 1:5,000 circulating CD4(+) T cells, include central and transitional memory polyfunctional populations, and do not include CD4(+)CD25(+)CD127(-) regulatory T cells. This approach may significantly accelerate the development of reliable MHC class II tetramers to monitor immune responses to tumor and self-antigens.
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Creative accounting is a growing issue of interest in Spain. In this article we argue that the concept true and fair view can limit or promote the use of creative accounting depending upon its interpretation. We review the range of meanings that true and fair view can take at an international level and compare the experience of the United Kingdom with the Australian one by analysing the use of true and fair view to limit creative accounting. Finally, we suggest lines of action to be considered by the Spanish accounting standards-setting institutions.
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This paper deals whit the dynamics of the Catalan textile labour market (theSpanish region that concentrated most of the industrial and factory activity duringthe 19 Century) and offers hypotheses and results on the impact it had on livingstandards and fertility levels. We observe the formation of an uneven labourmarket in which male supply for labour (excluding women and children) grewmuch faster than the demand. We stress the fact that labour supply is verydependant on institutional factors liked to the transmition of household propertybetween generations. Instead the slow path of growth of adult males demand forlabour is witnessing the limits of this industry to expand and to compete ininternational markets. The strategy of working class families to adapt to scarceopportunities of employment we document here is the diminution of legitimatefertility levels. Fertility control is the direct instrument we think workers have tocontrol their number in a situation that was likely to create labour surpluses in theshort and mid run.
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Recent research shows that financial reports are losing relevance. Mainly thisis due to the growing strategic importance of intangible assets in theperformance of a company. A possible solution is to modify accounting standardsso that statements include more self-generated intangible assets, taking intoaccount with their inherent risk and difficulty of valuation. We surveyed loanofficers who were asked to assess the credit-worthiness of a hypotheticalcompany. The only information given was a simplified version of financialstatements. Half the group got statements where research and development costshad been capitalized. The other half got statements in which these costs hadbeen treated as an expense. The findings show that capitalization wassignificantly more likely to attract a positive response to a loan request. Thepaper raises the question of whether accounting for intangibles might providemanagers with one more creative accounting technique and, in consequence, itsethical implications.
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Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.