Stationarity tests in time series model building

Optimum non-parametric tests for stationarity of a stochastic process against location and scale shift alternatives are explored. Usefulnesss of these tests in detecting a suitable differencing transformation that reduces a non-stationary time series to a stationary one is illustrated with a number of previously analysed real life data.     

Combining forecasts based on multiple encompassing tests in a macroeconomic core system

This paper investigates whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test‐based procedure, which assigns non‐zero weights to candidate models that add information not covered by other models. The potential benefits of this procedure are explored in extensive Monte Carlo simulations using realistic designs that are adapted to UK and to French macroeconomic data, to which trivariate vector autoregressions (VAR) are fitted. Thus simulations rely on potential data‐generating mechanisms for macroeconomic data rather than on simple but artificial designs. We run two types of forecast ‘competitions’. In the first one, one of the model classes is the trivariate VAR, such that it contains the generating mechanism. In the second specification, none of the competing models contains the true structure. The simulation results show that the performance of test‐based averaging is comparable to uniform weighting of individual models. In one of our role model economies, test‐based averaging achieves advantages in small samples. In larger samples, pure prediction models outperform forecast averages. Copyright © 2010 John Wiley & Sons, Ltd.     

Mechanical agitation of very dilute antiserum against IgE has no effect on basophil staining properties

A previously reported² effect of mechanically agitated dilutions of antiserum raised against IgE was investigated using the loss of metachromatic staining properties of human basophil leukocytes as a model. A series of 24 blind experiments was performed in which we determined the number of toluidine blue-stainable basophils after incubating with vortexed or non-vortexed dilutions of anti-IgE. Tenfold serial dilutions were used, in the range 10²¹ to 10³⁰ (6.6×10^–26 to 6.6×10^–35 M anti-IgE). We found no evidence for a different effect of strongly agitated dilutions, compared to dilutions made with minimal physical agitation. In fact, in our hands no effect of extreme dilutions was shown at all. We conclude that the effect of extreme dilutions of anti-IgE, reported by Davenas et al.², needs further clarification and that in this process the reproducibility of results between experimenters should be carefully determined.Acknowledgment. This study was partly funded by Homint.     

Forecast Combinations in a DSGE‐VAR Lab

We explore the benefits of forecast combinations based on forecast‐encompassing tests compared to simple averages and to Bates–Granger combinations. We also consider a new combination algorithm that fuses test‐based and Bates–Granger weighting. For a realistic simulation design, we generate multivariate time series samples from a macroeconomic DSGE‐VAR (dynamic stochastic general equilibrium–vector autoregressive) model. Results generally support Bates–Granger over uniform weighting, whereas benefits of test‐based weights depend on the sample size and on the prediction horizon. In a corresponding application to real‐world data, simple averaging performs best. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities. Copyright © 2016 John Wiley & Sons, Ltd.     

On a robust test for SETAR‐type nonlinearity in time series analysis

There has been growing interest in exploiting potential forecast gains from the nonlinear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type nonlinearities in observed time series. However, previous studies show that classical nonlinearity tests are not robust to additive outliers. In practice, time series outliers are not uncommonly encountered. It is important to develop a more robust test for SETAR‐type nonlinearity in time series analysis and forecasting. In this paper we propose a new robust nonlinearity test and the asymptotic null distribution of the test statistic is derived. A Monte Carlo experiment is carried out to compare the power of the proposed test with other existing tests under the influence of time series outliers. The effects of additive outliers on nonlinearity tests with misspecification of the autoregressive order are also studied. The results indicate that the proposed method is preferable to the classical tests when the observations are contaminated with outliers. Finally, we provide illustrative examples by applying the statistical tests to three real datasets. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecast Evaluation of Nonlinear Models: The Case of Long‐Span Real Exchange Rates

This paper deals with the nonlinear modeling and forecasting of the dollar–sterling and franc–sterling real exchange rates using long spans of data. Our contribution is threefold. First, we provide significant evidence of smooth transition dynamics in the series by employing a battery of recently developed in‐sample statistical tests. Second, we investigate the small‐sample properties of several evaluation measures for comparing recursive forecasts when one of the competing models is nonlinear. Finally, we run a forecasting race for the post‐Bretton Woods era between the nonlinear real exchange rate model, the random walk, and the linear autoregressive model. The nonlinear model outperforms all rival models in the dollar–sterling case but cannot beat the linear autoregressive in the franc–sterling. Copyright © 2011 John Wiley & Sons, Ltd.     

Monthly data and short‐term forecasting: an assessment of monthly data in a VAR model

Use of monthly data for economic forecasting purposes is typically constrained by the absence of monthly estimates of GDP. Such data can be interpolated but are then prone to measurement error. However, the variance matrix of the measurement errors is typically known. We present a technique for estimating a VAR on monthly data, making use of interpolated estimates of GDP and correcting for the impact of measurement error. We then address the question how to establish whether the model estimated from the interpolated monthly data contains information absent from the analogous quarterly VAR. The techniques are illustrated using a bivariate VAR modelling GDP growth and inflation. It is found that, using inflation data adjusted to remove seasonal effects and the impacts of changes to indirect taxes, the monthly model has little to add to a quarterly model when projecting one quarter ahead. However, the monthly model has an important role to play in building up a picture of the current quarter once one or two months' hard data becomes available. Copyright © 1999 John Wiley & Sons, Ltd.     

Accumulation of drastic mutants in selection lines for resistance to the insecticides dichlorvos and malathion inDrosophila melanogaster

Summary Viability tests were performed on second and third chromosomes from lines ofDrosophila melanogaster selected for increased resistance to the organophosphorus insecticides dichlorvos and malathion, in order to evaluate the accumulation of drastic alleles. Our results show that malathion reduces significantly the relative viability of chromosome 3 and also increases the frequency of drastic alleles in this chromosome, while dichlorvos increases significnatly the frequency of drastic alleles in chromosome 2.Work supported in part by the Spanish Ministry of Education and Science (Grant No. 0577/84 CAICYT).—We are grateful to Productos Cruz Verde S.A. and Agrocrós S.A. for generously supplying the insecticides dichlorvos and malathion, respectively.     

A Study of Value‐at‐Risk Based on M‐Estimators of the Conditional Heteroscedastic Models

In this paper, we investigate the performance of a class of M‐estimators for both symmetric and asymmetric conditional heteroscedastic models in the prediction of value‐at‐risk. The class of estimators includes the least absolute deviation (LAD), Huber's, Cauchy and B‐estimator, as well as the well‐known quasi maximum likelihood estimator (QMLE). We use a wide range of summary statistics to compare both the in‐sample and out‐of‐sample VaR estimates of three well‐known stock indices. Our empirical study suggests that in general Cauchy, Huber and B‐estimator have better performance in predicting one‐step‐ahead VaR than the commonly used QMLE. Copyright © 2011 John Wiley & Sons, Ltd.     

On tests for non-linearity in time series analysis

We have developed a new test for non-linearity in time series data in discrete time. A comparative study has been conducted on Subba Rao, Gabr and Hinich's test, Keenan's test, Petruccelli and Davies test, and the new test. Both simulated and real data are used in the study. The implication for forecasting is briefly discussed.     

A bayesian decision approach to model monitoring and cusums

Cumulative Sum techniques are widely used in quality control and model monitoring. A single-sided cusum may be regarded essentially as a sequence of sequential tests which, in many cases, such as those for the Exponential Family, is equivalent to a Sequence of Sequential Probability Ratio Tests. The relationship between cusums and Bayesian decisions is difficult to establish using conventional methods. An alternative approach is proposed which not only reveals a relation but also offers a very simple formulation of the decision process involved in model monitoring. This is first illustrated for a Normal mean and then extended to other important practical cases including Dynamic Models. For V-mask cusum graphs a particular feature is the interpretation of the distance of the V vertex from the latest plotted point in terms of the prior precision as measured in ‘equivalent’ observations.     

Estimation and testing of time-varying coefficient regression models in the presence of linear restrictions

A linear regression model with random walk coefficients is extended to allow for linear restrictions between the coefficients to be satisfied at each point in time. Estimation in this model is shown to be no more involved than estimation in the standard model. It is also demonstrated how, after a slight modification to the testing problem, classical test procedures may be applied to the problem of testing for such restrictions. The performance of the Lagrange Multiplier test for a variety of different restrictions is then investigated via simulation. An empirical application involving testing for homogeneity in a random walk coefficient version of the AIDS model is given.     

Radiation embryology

Summary Prenatal development, characterized by intensive cell proliferation, cell differentiation and cell migration, shows a high radiosensitivity. Therefore, radiation exposure of embryos and fetuses is of great concern for radiological protection and human health. Irradiation during gestation can cause death, growth disorders, malformations, functional impairment and malignant diseases in childhood. These effects are strongly dependent on the developmental stage at exposure and on the radiation dose. The first trimester of pregnancy is regarded as the period with the highest risk for malformation and cancer induction. The developing nervous system shows a special susceptibility to ionizing radiation over a long period and is therefore of great significance for risk estimation. Knowledge about radiation effects on prenatal development has been derived from animal experimentation and from the exposure of human embryos. There is evidence that doses between 1 and 10 cGy may lead to developmental anomalies and that the radiation response can be modified by additional factors.     

Modelling exchange rate dynamics new perspectives from the frequency domain

In this paper the dynamics of foreign exchange rates is sought to be studied via new frequency domain techniques. Stationarity properties of the rates are analysed via a unit root test as well as a test based on the evolutionary spectrum. Linearity and Gaussianity are analysed via bispectral tests and compared with the more frequently employed time domain tests, such as the McLeod-Li and Tsay tests. Finally, an evaluation of the out-of-sample forecasting properties for eight methods—Random Walk, ARMA, Bilinear, State dependent model, dynamic linear model, ARCH, GARCH, and Garch-in-mean—is made. The methods used here seem to shed a great deal of light on hitherto neglected aspects of exchange rate dynamics.     

Nature in the laboratory — nature as a laboratory. Considerations about the ethics of release experiments

Field tests with genetically modified organisms go beyond the boundaries of the politically and morally neutralized space that normally surrounds scientific experiments. They enter public areas. As a social process of shaping nature they are political in a fundamental sense. Consequences of this observation concern the legitimacy of decisions and the legitimacy of deciding procedures. The political rights of citizens and their human rights can only be respected if these procedures are democratic. Without a more serious exploration of the specific circumstances of release tests — for example, the precise ecological context, the consequences for the future development of the affected ecosystem, the social consequences, and the possible institutional ways of establishing gene technology in agriculture — we do not really know what we are doing when we release transgenic organisms. Moral judgements today can therefore only be prima facie, not free from shortcomings. As responsible judges we must confess that we are still morally blind.     

Price–Dividend Ratios and Stock Price Predictability

A long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time‐varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend‐adjusted PD ratio. Furthermore, this paper rejects the non‐predictability hypothesis of stock prices statistically based on in‐ and out‐of‐sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd.     

The dynamic and stochastic instability of betas: Implications for forecasting stock returns

The purpose of this paper is to simultaneously investigate several important issues that feature the dynamic and stochastic behavior of beta coefficients for individual stocks and affect the forecasting of stock returns. The issues include randomness, nonstantionarity, and shifts in the mean and variance parameters of the beta coefficient, and are addressed within the framework of variable-mean-response (VMR) random coefficients models in which the problem of heteroscedasticity is present. Estimation is done using a four-step generalized least squares method. The hypotheses concerning randomness and nonstationarity of betas are tested. The time paths, sizes, and marginal rates of mean shifts are determined. The issue of variance shift is examined on the basis of five special tests, called T*, B, S', G and W. Then the impacts of the dynamic and stochastic instability on the estimation of betas is tested by a nonparametric procedure. Finally, the VMR models' ability of forecasting stock returns is evaluated against the standard capital asset pricing model. The empirical findings shed new light on the continuing debate as to whether the beta coefficient is random and nonstationary and have important implications for modeling and forecasting the measurement of performance and the determination of stock returns.     

Implications of toxins in the ecology and evolution of plant pathogenic microorganisms: Bacteria

This review attempts to rationalise what is known about bacterial phytotoxins and associate it with the ecology and possible evolution of the producing organisms. Study of non-toxin producing variants gives insight into the ecological role of the toxin. Elucidation of chemical structures of phytotoxins has shown that many exist as families of analogous compounds. Studies on the variation of chemical structures and how they are distributed across species and genera can lead to development of hypotheses on evolutionary relationships. Knowledge on biosynthetic pathways to tosins allows recognition of specific enzymatic steps involved in developing the characteristic features of the structures. Phytotoxins often have a potent biochemical activity, and in some cases the producing organism has associated mechanisms to prevent action of the toxin upon itself; in such cases toxigenesis is clearly not a chance event. The various aspects of bacterial toxigenesis indicate that bacterial phytotoxins are special secondary metabolic products that play beneficial roles to the producing organisms in their various ecological niches.     

Inherent properties and statistics with individual particles in quantum mechanics

This paper puts forward the hypothesis that the distinctive features of quantum statistics are exclusively determined by the nature of the properties it describes. In particular, all statistically relevant properties of identical quantum particles in many-particle systems are conjectured to be irreducible, ‘inherent’ properties only belonging to the whole system. This allows one to explain quantum statistics without endorsing the ‘Received View’ that particles are non-individuals, or postulating that quantum systems obey peculiar probability distributions, or assuming that there are primitive restrictions on the range of states accessible to such systems. With this, the need for an unambiguously metaphysical explanation of certain physical facts is acknowledged and satisfied.     

Instar-specific defensive secretions of stink bugs (Heteroptera: Pentatomidae)

Defensive secretions (allomones) from first-instar nymphs of stink bugs in the subfamily Pentatominae contain (E)-4-oxo-2-decenal as a major constituent, whereas this compound is absent from later instars. In contrast, first instars ofEdessa meditabunda (Edessinae) produce allomones like those of later instars. The C₆ and C₈ (E)-4-oxo-2-alkenals are common, characteristic exocrine compounds of nymphal and adult Heteroptera, but (E)-4-oxo-2-decenal is previously unknown as a major natural product for which a biological role has yet to be established.