Quantitative genetics of zooplankton life histories

Quantitative genetic techniques are powerful tools for use in understanding the microevolutionary process. Because of their size, lifespan, and ease of culture, many zooplankton species are ideal for quantitative genetic approaches. As model systems, studies of zooplankton life histories are becoming increasingly used for examination of the central paradigms of evolutionary theory. Two of the fundamental empirical questions that zooplankton quantitative genetics studies can answer are: 1) How much genetic variance exists in natural populations for life history traits? 2) What is the empirical evidence for trade-offs that permeate life history theory based on optimality approaches? A review of existing data onDaphnia indicates substantial genetic variance for body size, clutch size, and age at first reproduction. Average broad-sense heritabilities for these three characters across 19 populations of 6 species are 0.31, 0.31, and 0.34, respectively. Although there is some discrepancy between the two pertinent studies that were designed to decompose the total genetic variance into its additive and non-additive components, a crude average seems to suggest that approximately 60% of the total genetic variance has an additive basis. The existing data are somewhat inconsistent with respect to presence/absence of trade-offs (negative genetic correlations) among life history traits. A composite of the existing data seems to argue against the existence of strong trade-offs between offspring size and offspring number, between present and future reproduction, and between developmental rate and fecundity. However, there is some evidence for a shift toward more negative (less positive) covariances in more stressful environments (e.g., low food). Zooplankton will prove to be very useful in future study in several important areas of research, including the genetics and physiology of aging, the importance of genotype-environment interaction for life history traits, and the evolution of phenotypic plasticity.     

双离合器自动变速汽车换挡过程离合器模糊控制策略仿真研究

通过对装有双离合器自动变速系统汽车挟挡过程的分析,建立了双离舍器自动变速器的换挡过程的动力学方程。在分析双离合器自动变速汽车换挡品质影响因素的基础上,设计出两离合器分离、接合速度模糊控制器,并利用挡位传动比的变化对两离合器分离、接合速度模糊系统输出量进行量化修正。仿真结果表明,所设计的双离合器自动变速汽车换挡过程两离合器分离、接合速度模糊控制器较好地满足了汽车换挡平顺性和快速性的要求。     

Finite‐sample properties of tests for equal forecast accuracy

This study examines the small‐sample properties of some commonly used tests of equal forecast accuracy. The paper considers the size and power of different tests and the performance of different heteroscedasticity and autocorrelation‐consistent (HAC) variance estimators. Monte Carlo experiments show that the tests all suffer some size distortions in small samples, with the distortions varying across tests. The experiments also show that, adjusted for size distortions, the tests have broadly similar power, although some small differences exist. Finally, the experiments indicate that the size and power performances of HAC estimators vary with the features of the data. Copyright © 1999 John Wiley & Sons, Ltd.     

Forecasting with growth curves: the effect of error structure

"The main theme of this paper is an investigation into the importance of error structure as a determinant of the forecasting accuracy of the logistic model. The relationship between the variance of the disturbance term and forecasting accuracy is examined empirically. A general local logistic model is developed as a vehicle to be used in this investigation. Some brief comments are made on the assumptions about error structure, implicit or explicit, in the literature." The results suggest that "the variance of the disturbance term, when using the logistic to forecast human populations, is proportional to at least the square of population size."     

Computing the variance of the forecast error for the holt-winters seasonal models

This paper presents expressions for the variance of the forecast error for arbitrary lead times for both the additive and multiplicative Holt-Winters seasonal forecasting models. It is shown that even when the smoothing constants are chosen to have values between zero and one, when the period is greater than four, the variance may not be finite for some values of the smoothing constants. In addition, the regions where the variance becomes infinite are almost the same for both models. These results are of importance for practitioners, who may choose values for the smoothing constants arbitrarily, or by searching on the unit cube for values which minimize the sum of the squared errors when fitting the model to a data set. It is also shown that the variance of the forecast error for the multiplicative model is nonstationary and periodic.     

Bias in the estimation of non‐linear transformations of the integrated variance of returns

Volatility models such as GARCH, although misspecified with respect to the data‐generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non‐linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non‐linear transformations of these forecasts will be biased estimators of the same non‐linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non‐linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non‐linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in‐sample and out‐of‐sample forecasts of three non‐linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial. Copyright © 2006 John Wiley & Sons, Ltd.     

Minimum variance prediction of bilinear time series: Direct and adaptive versions

Bilinear models of time series are considered. Minimum variance predictor for bilinear time series, homogeneous in the input and output, is proposed. Results of minimum variance prediction of bilinear time series are included. They are compared to the results of linear prediction of bilinear time series. A minimum variance prediction algorithm for bilinear time series of the general form is developed and an adaptive version of minimum variance algorithm is derived.     

A threshold stochastic volatility model

This article introduces a new model to capture simultaneously the mean and variance asymmetries in time series. Threshold non‐linearity is incorporated into the mean and variance specifications of a stochastic volatility model. Bayesian methods are adopted for parameter estimation. Forecasts of volatility and Value‐at‐Risk can also be obtained by sampling from suitable predictive distributions. Simulations demonstrate that the apparent variance asymmetry documented in the literature can be due to the neglect of mean asymmetry. Strong evidence of the mean and variance asymmetries was detected in US and Hong Kong data. Asymmetry in the variance persistence was also discovered in the Hong Kong stock market. Copyright © 2002 John Wiley & Sons, Ltd.     

A multivariate approach to socio-ecological development and endocrine variance in the spotted hyaena, Crocuta crocuta Erxleben.

This investigation of the socio-ecological development and endocrine variance in the spotted hyaena, Crocuta crocuta, illustrates that the complexity of a multivariate endocrine system cannot fully be understood using the concept of a class mean (mu). Any comprehensive investigation of a multivariate endocrine system should also include an analysis of variance (sigma), as it may provide additional insights into the dynamics of an endocrine hypervolume. Mean cortisol concentrations could not differentiate between various social and reproductive categories (MANOVA); however, in males an analysis of variance (principal component analysis) indicated that the contrast between cortisol and androstenedione was the principle axis of variance once the androgen secreting ability had been accounted for. On the other hand, the same contrast was found to be the principal axis of variance in the female sub-sample, suggesting that cortisol may play a significant role in regulating the endocrine dynamics of this species, despite showing little variance in mean values among various social and reproductive categories.     

Forecasting Latent Volatility through a Markov Chain Approximation Filter

We propose a new methodology for filtering and forecasting the latent variance in a two‐factor diffusion process with jumps from a continuous‐time perspective. For this purpose we use a continuous‐time Markov chain approximation with a finite state space. Essentially, we extend Markov chain filters to processes of higher dimensions. We assess forecastability of the models under consideration by measuring forecast error of model expected realized variance, trading in variance swap contracts, producing value‐at‐risk estimates as well as examining sign forecastability. We provide empirical evidence using two sources, the S&P 500 index values and its corresponding cumulative risk‐neutral expected variance (namely the VIX index). Joint estimation reveals the market prices of equity and variance risk implicit by the two probability measures. A further simulation study shows that the proposed methodology can filter the variance of virtually any type of diffusion process (coupled with a jump process) with a non‐analytical density function. Copyright © 2015 John Wiley & Sons, Ltd.     

Outliers,level shifts,and variance changes in time series

Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.     

A multivariate approach to socio-ecological development and endocrine variance in the spotted hyaena,Crocuta crocuta Erxleben

This investigation of the socio-ecological development and endocrine variance in the spotted hyaena,Crocuta crocuta, illustrates that the complexity of a multivariate endocrine system cannot fully be understood using the concept of a class mean (). Any comprehensive investigation of a multivariate endocrine system should also include an analysis of variance (), as it may provide additional insights into the dynamics of an endocrine hypervolume. Mean cortisol concentrations could not differentiate between various social and reproductive categories (MANOVA); however, in males an analysis of variance (principal component analysis) indicated that the contrast between cortisol and androstenedione was the principle axis of variance once the androgen secreting ability had been accounted for. On the other hand, the same contrast was found to be the principal axis of variance in the female sub-sample, suggesting that cortisol may play a significant role in regulating the endocrine dynamics of this species, despite showing little variance in mean values among various social and reproductive categories.     

Effect of regressor forecast error on the variance of regression forecasts

It is well understood that the standard formulation for the variance of a regression‐model forecast produces interval estimates that are too narrow, principally because it ignores regressor forecast error. While the theoretical problem has been addressed, there has not been an adequate explanation of the effect of regressor forecast error, and the empirical literature has supplied a disparate variety of bits and pieces of evidence. Most business‐forecasting software programs continue to supply only the standard formulation. This paper extends existing analysis to derive and evaluate large‐sample approximations for the forecast error variance in a single‐equation regression model. We show how these approximations substantially clarify the expected effects of regressor forecast error. We then present a case study, which (a) demonstrates how rolling out‐of‐sample evaluations can be applied to obtain empirical estimates of the forecast error variance, (b) shows that these estimates are consistent with our large‐sample approximations and (c) illustrates, for ‘typical’ data, how seriously the standard formulation can understate the forecast error variance. Copyright © 2000 John Wiley & Sons, Ltd.     

The maximum and minimum of primary forecasts

The purpose of this paper is to suggest that the maximum (or minimum) of a number of primary forecasts may make a valuable addition to the forecasting accuracy of a combination of forecasts. Such forecasts are readily computable. Theoretical results are presented for two unbiased forecasts with correlated normally distributed errors, showing that the maximum (minimum) of two forecasts can have a smaller error variance than either of the primary forecasts and the forecast error can have low correlation with the primary errors. Empirical results are obtained for two different sets of forecasts available in the literature, and it is observed that a combination forecast including the maximum and/or minimum has attractive forecasting properties.     

Forecasting realized volatility of oil futures market: A new insight

In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper.     

Modeling and forecasting aggregate stock market volatility in unstable environments using mixture innovation regressions

We perform Bayesian model averaging across different regressions selected from a set of predictors that includes lags of realized volatility, financial and macroeconomic variables. In our model average, we entertain different channels of instability by either incorporating breaks in the regression coefficients of each individual model within our model average, breaks in the conditional error variance, or both. Changes in these parameters are driven by mixture distributions for state innovations (MIA) of linear Gaussian state‐space models. This framework allows us to compare models that assume small and frequent as well as models that assume large but rare changes in the conditional mean and variance parameters. Results using S&P 500 monthly and quarterly realized volatility data from 1960 to 2014 suggest that Bayesian model averaging in combination with breaks in the regression coefficients and the error variance through MIA dynamics generates statistically significantly more accurate forecasts than the benchmark autoregressive model. However, compared to a MIA autoregression with breaks in the regression coefficients and the error variance, we fail to provide any drastic improvements.     

Analyzing Macroeconomic Forecastability

This paper estimates, using stochastic simulation and a multi‐country macroeconometric model, the fraction of the forecast error variance of output changes and the fraction of the forecast error variance of inflation that are due to unpredictable asset price changes. The results suggest that between about 25% and 37% of the forecast error variance of output growth over eight quarters is due to asset price changes and between about 33% and 60% of the forecast error variance of inflation over eight quarters is due to asset price changes. These estimates provide limits to the accuracy that can be expected from macroeconomic forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Asymmetries in conditional mean and variance: modelling stock returns by asMA‐asQGARCH

We propose a nonlinear time series model where both the conditional mean and the conditional variance are asymmetric functions of past information. The model is particularly useful for analysing financial time series where it has been noted that there is an asymmetric impact of good news and bad news on volatility (risk) transmission. We introduce a coherent framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. To this end we derive both a Wald and a Lagrange multiplier test. Some of the new asymmetric model's moment properties are investigated. Detailed empirical results are given for the daily returns of the composite index of the New York Stock Exchange. There is strong evidence of asymmetry in both the conditional mean and the conditional variance functions. In a genuine out‐of‐sample forecasting experiment the performance of the best fitted asymmetric model, having asymmetries in both conditional mean and conditional variance, is compared with an asymmetric model for the conditional mean, and with no‐change forecasts. This is done both in terms of conditional mean forecasting as well as in terms of risk forecasting. Finally, the paper presents some evidence of asymmetries in the index stock returns of the Group of Seven (G7) industrialized countries. Copyright © 2004 John Wiley & Sons, Ltd.     

Environmental and racial influences on the masticatory apparatus of mediterranean and atlantic populations ofOphryotrocha labronica (Annelida,Polychaeta)

Summary Significant differences in the length of the definitive upper jaw have been ascertained in 3 populations of the comprehensive speciesO. labronica. The ratio of the variance (F) of the 3 populations has been employed in evaluating the taxonomic distance between the populations. The density of the water and the kind of food strongly influence the length of the definitive upper jaw.     

Judgmental extrapolation and the salience of change

People may often forecast using cognitive procedures that resemble formal time-series extrapolation models. A model of judgmental extrapolation based on exponential smoothing is proposed in which the setting of the trend parameter is hypothesized to depend upon the relative salience of the successive changes. The salience hypothesis was first tested with exponential series by the use of a framing manipulation. As predicted, focusing the subjects' attention on the changes led to more accurate forecasts. In two investment simulation studies, the salience hypothesis was further examined by varying the statistical properties of the price changes. As predicted, subjects were more likely to sell as prices fell and to buy as prices rose (1) as the sample size of similar changes increased; (2) when the variance of the changes was low; and (3) when the absolute value of the mean change was high. Conditions that may influence judgmental forecasting processes are discussed.