Retrocausal models for EPR

This paper takes up Huw Price׳s challenge to develop a retrocausal toy model of the Bell-EPR experiment. I develop three such models which show that a consistent, local, hidden-variables interpretation of the EPR experiment is indeed possible, and which give a feel for the kind of retrocausation involved. The first of the models also makes clear a problematic feature of retrocausation: it seems that we cannot interpret the hidden elements of reality in a retrocausal model as possessing determinate dispositions to affect the outcome of experiments. This is a feature which Price has embraced, but Gordon Belot has argued that this feature renders retrocausal interpretations “unsuitable for formal development”, and the lack of such determinate dispositions threatens to undermine the motivation for hidden-variables interpretations in the first place. But Price and Belot are both too quick in their assessment. I show that determinate dispositions are indeed consistent with retrocausation. What is more, I show that the ontological economy allowed by retrocausation holds out the promise of a classical understanding of spin and polarization.     

On multistep prediction error methods for time series models

Multistep prediction error methods for linear time series models are considered from both a theoretical and a practical standpoint. The emphasis is on autoregressive moving-average (ARMA) models for which a multistep prediction error estimation method (PEM) is developed. The results of a Monte Carlo simulation study aimed at establishing the possible merits of the multistep PEM are presented.     

On estimation and prediction procedures for ar(1) models with power transformation

The power transformation of Box and Cox (1964) has been shown to be quite useful in short-term forecasting for the linear regression model with AR(1) dependence structure (see, for example, Lee and Lu, 1987, 1989). It is crucial to have good estimates of the power transformation and serial. correlation parameters, because they form the basis for estimating other parameters and predicting future observations. The prediction of future observations is the main focus of this paper. We propose to estimate these two parameters by minimizing the mean squared prediction errors. These estimates and the corresponding predictions compare favourably, via revs and simulated data, with those obtained by the maximum likelihood method. Similar results are also demonstrated in the repeated measurements setting.     

On two transverse nonlinear models of axially moving beams

Nonlinear models of transverse vibration of axially moving beams are computationally investigated.A partial-differential equation is derived from the governing equation of coupled planar motion by omitting its longitudinal terms.The model can be reduced to an integro-partial-differential equation by averaging the beam disturbed tension.Numerical schemes are respectively presented for the governing equations of coupled planar and the two governing equations of transverse motion via the finite difference meth...     

On the use of power transformations in CAViaR models

Value at risk (VaR) is a risk measure widely used by financial institutions in allocating risk. VaR forecast estimation involves the conditional evaluation of quantiles based on the currently available information. Recent advances in VaR evaluation incorporate a proxy for conditional variance, yielding the conditional autoregressive VaR (CAViaR) models. However, early work in finance literature has shown that the introduction of power transformations has resulted in improvements in volatility forecasting. Having a direct association between volatility and conditional VaR, we adopt power-transformed CAViaR models. We investigate whether the flexible conditional VaR dynamics associated with power-transformed CAViaR models can result in better forecasting results than those assumed by the nontransformed CAViaR models. Estimation in CAViaR models is based on an early-rejection Markov chain Monte Carlo algorithm. We illustrate our forecasting evaluation results using simulated and financial daily return data series. The results demonstrate that there is strong evidence that supports the use of power-transformed CAViaR models when forecasting VaR.     

Bayesian forecasts for cointegrated models

This paper investigates Bayesian forecasts for some cointegrated time series data. Suppose data are derived from some cointegrated model, but, an unrestricted vector autoregressive model, without including cointegrated conditions, is fitted; the implication of using an incorrect model will be investigated from the Bayesian forecasting viewpoint. For some special cointegrated data and under the diffuse prior assumption, it can be analytically proven that the posterior predictive distributions for both the true model and the fitted model are asymptotically the same for any future step. For a more general cointegrated model, examinations are performed via simulations. Some simulated results reveal that a reasonably unrestricted model will still provide a rather accurate forecast as long as the sample size is large enough or the forecasting period is not too far in the future. For a small sample size or for long‐term forecasting, more accurate forecasts are expected if the correct cointegrated model is actually applied. Copyright © 2002 John Wiley & Sons, Ltd.     

On the usefulness of macroeconomic forecasts as inputs to forecasting models

A forecasting model for y_t based on its relationship to exogenous variables (e.g. x?_t) must use x?_t, the forecast of x?_t. An example is given where commercially available x?_t's are sufficiently inaccurate that a univariate model for y_t appears preferable. For a variety of types of models inclusion of an exogenous variable x?_t is shown to worsen the y_t forecasts whenever x?_t must itself be forecast by x?_t and MSE (x?_t) > Var (x?_t). Tests with forecasts from a variety of sources indicate that, with a few notable exceptions, MSE (x?_t) > Var (x?_t) is common for macroeconomic forecasts more than a quarter or two ahead. Thus, either:

• (a) available medium range forecasts for many macroeconomic variables (e.g. the GNP growth rate) are not an improvement over the sample mean (so that such variables are not useful explanatory variables in forecasting models), and/or
• (b) the suboptimization involved in directly replacing x?_t by x?_t is a luxury that we cannot afford.

     

On trend extraction models: Interpretation,empirical evidence and forecasting performance

This paper deals with the economic interpretation of the unobserved components model in the light of the apparent problem posed by previous work in that several practiced methodologies seem to lead to very different models of certain economic variables. A detailed empirical analysis is carried out to show how the failure in obtaining quasi-orthogonal components can seriously bias the interpretation of some decomposition procedures. Finally, the forecasting performance (in both the short and long run) of these decomposition models is analyzed in comparison with other alternatives.     

Cell culture models and animal models for studying the patho-physiological role of renal aquaporins

Aquaporins (AQPs) are key players regulating urinary-concentrating ability. To date, eight aquaporins have been characterized and localized along the nephron, namely, AQP1 located in the proximal tubule, thin descending limb of Henle, and vasa recta; AQP2, AQP3 and AQP4 in collecting duct principal cells; AQP5 in intercalated cell type B; AQP6 in intercalated cells type A in the papilla; AQP7, AQP8 and AQP11 in the proximal tubule. AQP2, whose expression and cellular distribution is dependent on vasopressin stimulation, is involved in hereditary and acquired diseases affecting urine-concentrating mechanisms. Due to the lack of selective aquaporin inhibitors, the patho-physiological role of renal aquaporins has not yet been completely clarified, and despite extensive studies, several questions remain unanswered. Until the recent and large-scale development of genetic manipulation technology, which has led to the generation of transgenic mice models, our knowledge on renal aquaporin regulation was mainly based on in vitro studies with suitable renal cell models. Transgenic and knockout technology approaches are providing pivotal information on the role of aquaporins in health and disease. The main goal of this review is to update and summarize what we can learn from cell and animal models that will shed more light on our understanding of aquaporin-dependent renal water regulation.     

The first molecular models for an electromagnetic

The first models for an electromagnetic theory of dispersion are presented and an attempt is made to demonstrate the important role played by study of this phenomenon at the end of the nineteenth century. As well as indicating the need to have a better understanding of the microscopic properties of matter, dispersion also contributed (through the results obtained by Helmholtz) to the discussion over the nature of X-rays and was fundamental for introduction of Lorentz's electron theory.     

Variance estimation for multivariate dynamic linear models

The problem of estimating unknown observational variances in multivariate dynamic linear models is considered. Conjugate procedures are possible for univariate models and also for special very restrictive common components models but they are not generally applicable. However, for clarity of operation and in order to avoid numerical integration, it is desirable to have conjugacy or approximate conjugacy. Such an approximate procedure is proposed based upon a simple analytic approximation. It is exact for the sub-class of conjugate models and improves on a previous procedure based upon the Robust filter.     

Estimation procedures for structural time series models

A univariate structural time series model based on the traditional decomposition into trend, seasonal and irregular components is defined. A number of methods of computing maximum likelihood estimators are then considered. These include direct maximization of various time domain likelihood function. The asymptotic properties of the estimators are given and a comparison between the various methods in terms of computational efficiency and accuracy is made. The methods are then extended to models with explanatory variables.     

Alternative regime switching models for forecasting inflation

US inflation appears to undergo shifts in its mean level and variability. We evaluate the performance of three useful models for capturing such shifts. The models studied are the Markov switching models, state space models with heavy‐tailed errors, and state space models with compound error distributions. Our study shows that all three models have very similar performance when evaluated in terms of the mean squared or mean absolute forecast errors. However, the latter two models are considerably more parsimonious, and easily beat the more profligately parameterized Markov switching models in terms of model selection criteria, such as the AIC or the SBC. Thus, these may serve as useful continuous alternatives to the popular discrete Markov switching models for capturing shifts in time series. Copyright © 2001 John Wiley & Sons, Ltd.     

Computational models: a modest role for content

The computational theory of mind construes the mind as an information-processor and cognitive capacities as essentially representational capacities. Proponents of the view (hereafter, ‘computationalists’) claim a central role for representational content in computational models of these capacities. In this paper I argue that the standard view of the role of representational content in computational models is mistaken; I argue that representational content is to be understood as a gloss on the computational characterization of a cognitive process.     

Trichomes as models for studying plant cell differentiation

Trichomes, originating from epidermal cells, are present on nearly all terrestrial plants. They exist in diverse forms, are readily accessible, and serve as an excellent model system for analyzing the molecular mechanisms in plant cell differentiation, including cell fate choices, cell cycle control, and cell morphogenesis. In Arabidopsis, two regulatory models have been identified that function in parallel in trichome formation; the activator–inhibitor model and the activator–depletion model. Cotton fiber, a similar unicellular structure, is controlled by some functional homologues of Arabidopsis trichome-patterning genes. Multicellular trichomes, as in tobacco and tomato, may form through a distinct pathway from unicellular trichomes. Recent research has shown that cell cycle control participates in trichome formation. In this review, we summarize the molecular mechanisms involved in the formation of unicellular and multicellular trichomes, and discuss the integration of the cell cycle in its initiation and morphogenesis.     

Evaluation of correlation forecasting models for risk management

Reliable correlation forecasts are of paramount importance in modern risk management systems. A plethora of correlation forecasting models have been proposed in the open literature, yet their impact on the accuracy of value‐at‐risk calculations has not been explicitly investigated. In this paper, traditional and modern correlation forecasting techniques are compared using standard statistical and risk management loss functions. Three portfolios consisting of stocks, bonds and currencies are considered. We find that GARCH models can better account for the correlation's dynamic structure in the stock and bond portfolios. On the other hand, simpler specifications such as the historical mean model or simple moving average models are better suited for the currency portfolio. Copyright © 2007 John Wiley & Sons, Ltd.     

Molecular recognition: models for drug design.

The review takes examples, mostly from the recent literature, to illustrate how an understanding of physico-chemical properties and an appreciation of the molecular shape and electronic properties can lead to a better insight into molecular recognition processes. The techniques used to generate 3-dimensional structures of molecules and the influence this information has had on the drug design cycle, are briefly discussed.