Determining the Level of Security Culture of the Entity Using the Midified Shalom Schwartz Value Model

Each consciously existing entity has its own unique, hierarchical set of values, helping to make decisions and set directions for action. This power is a security culture, also defined as a set of universal values, attitudes and beliefs. The foundation upon which the security culture grows is the three pillars of security – the three energy streams of the security culture. The objective is to estimate the level of the entity’s security culture and present its structure. The determination of the empirical size of a security culture is made by adapting Shalom Schwartz’s value theory and his ten types of fundamental values. The values placed on the Schwartz circle model are aggregated to the appropriate energy streams of the security culture, and this move allows for empirical determination of the level of the security culture of an individual. The obtained results show the structure of energy streams and determine the empirical level of the subject’s security culture. The approach taken shows that the level of security culture is computable.

More on the Supremum Statistic to Test Multivariate Skew-Normality

This review is about verifying and generalizing the supremum test statistic developed by Balakrishnan et al. Exhaustive simulation studies are conducted for various dimensions to determine the effect, in terms of empirical size, of the supremum test statistic developed by Balakrishnan et al. to test multivariate skew-normality. Monte Carlo simulation studies indicate that the Type-I error of the supremum test can be controlled reasonably well for various dimensions for given nominal significance levels 0.05 and 0.01. Cut-off values are provided for the number of samples required to attain the nominal significance levels 0.05 and 0.01. Some new and relevant information of the supremum test statistic are reported here.

A Robust Test for Monotonicity in Asset Returns

In this paper, we propose a robust test of monotonicity in asset returns that is valid under a general setting. We develop a test that allows for dependent data and is robust to conditional heteroskedasticity or heavy-tailed distributions of return differentials. Many postulated theories in economics and finance assume monotonic relationships between expected asset returns and certain underlying characteristics of an asset. Existing tests in literature fail to control the probability of a type 1 error or have low power under heavy-tailed distributions of return differentials. Monte Carlo simulations illustrate that our test statistic has a correct empirical size under all data-generating processes together with a similar power to other tests. Conversely, alternative tests are nonconservative under conditional heteroskedasticity or heavy-tailed distributions of return differentials. We also present an empirical application on the monotonicity of returns on various portfolios sorts that highlights the usefulness of our approach.

An Introduction to Bootstrap Theory in Time Series Econometrics

While often simple to implement in practice, application of the bootstrap in econometric modeling of economic and financial time series requires establishing validity of the bootstrap. Establishing bootstrap asymptotic validity relies on verifying often nonstandard regularity conditions. In particular, bootstrap versions of classic convergence in probability and distribution, and hence of laws of large numbers and central limit theorems, are critical ingredients. Crucially, these depend on the type of bootstrap applied (e.g., wild or independently and identically distributed (i.i.d.) bootstrap) and on the underlying econometric model and data. Regularity conditions and their implications for possible improvements in terms of (empirical) size and power for bootstrap-based testing differ from standard asymptotic testing, which can be illustrated by simulations.

The effects of trawling and primary production on size-structured food webs in seabed ecosystems

Understanding how different drivers shape relationships between abundance and body mass (size spectra) is important for understanding trophic and competitive interactions in food webs and for predicting the effects of human pressures. Here, we sample seabed communities from small polychaetes (<0.001 g) to large fish (>1 kg) in the Celtic Sea and the western English Channel to examine how bottom trawling and primary production affect their size spectra and to compare these with predictions from a model that couples predator and detritivore communities. Size spectra were not well approximated by linear fits because of truncation of the size spectra of detritivores. Low primary production resulted in lower abundance of benthic fauna. Bottom trawling reduced the abundance of predators and large detritivores but allowed small detritivores to increase in abundance. These empirical size spectra were partly consistent with predictions from the size spectra model, showing that understanding the structuring of benthic communities requires a consideration of both size and functional group. The findings highlight the need for an ecosystem approach to understanding the effects of exploitation and climate change on marine ecosystems.

Testing financial time series for autocorrelation: Robust Tests

Two modified Portmanteau statistics are studied under dependence assumptions common in financial applications which can be used for testing that heteroskedastic time series are serially uncorrelated without assuming independence or Normality. Their asymptotic distribution is found to be null and their small sample properties are examined via Monte Carlo. The power of the tests is studied under the MA and GARCH-in-mean alternatives. The tests exhibit an appropriate empirical size and are seen to be more powerful than a robust Box-Pierce to the selected alternatives. Real data on daily stock returns and exchange rates is used to illustrate the tests.

A robust score test of homogeneity for zero-inflated count data

In many applications of zero-inflated models, score tests are often used to evaluate whether the population heterogeneity as implied by these models is consistent with the data. The most frequently cited justification for using score tests is that they only require estimation under the null hypothesis. Because this estimation involves specifying a plausible model consistent with the null hypothesis, the testing procedure could lead to unreliable inferences under model misspecification. In this paper, we propose a score test of homogeneity for zero-inflated models that is robust against certain model misspecifications. Due to the true model being unknown in practical settings, our proposal is developed under a general framework of mixture models for which a layer of randomness is imposed on the model to account for uncertainty in the model specification. We exemplify this approach on the class of zero-inflated Poisson models, where a random term is imposed on the Poisson mean to adjust for relevant covariates missing from the mean model or a misspecified functional form. For this example, we show through simulations that the resulting score test of zero inflation maintains its empirical size at all levels, albeit a loss of power for the well-specified non-random mean model under the null. Frequencies of health promotion activities among young Girl Scouts and dental caries indices among inner-city children are used to illustrate the robustness of the proposed testing procedure.

WEAK-IDENTIFICATION ROBUST WILD BOOTSTRAP APPLIED TO A CONSISTENT MODEL SPECIFICATION TEST

We present a new robust bootstrap method for a test when there is a nuisance parameter under the alternative, and some parameters are possibly weakly or nonidentified. We focus on a Bierens (1990, Econometrica 58, 1443–1458)-type conditional moment test of omitted nonlinearity for convenience. Existing methods include the supremum p-value which promotes a conservative test that is generally not consistent, and test statistic transforms like the supremum and average for which bootstrap methods are not valid under weak identification. We propose a new wild bootstrap method for p-value computation by targeting specific identification cases. We then combine bootstrapped p-values across polar identification cases to form an asymptotically valid p-value approximation that is robust to any identification case. Our wild bootstrap procedure does not require knowledge of the covariance structure of the bootstrapped processes, whereas Andrews and Cheng’s (2012a, Econometrica 80, 2153–2211; 2013, Journal of Econometrics 173, 36–56; 2014, Econometric Theory 30, 287–333) simulation approach generally does. Our method allows for robust bootstrap critical value computation as well. Our bootstrap method (like conventional ones) does not lead to a consistent p-value approximation for test statistic functions like the supremum and average. Therefore, we smooth over the robust bootstrapped p-value as the basis for several tests which achieve the correct asymptotic level, and are consistent, for any degree of identification. They also achieve uniform size control. A simulation study reveals possibly large empirical size distortions in nonrobust tests when weak or nonidentification arises. One of our smoothed p-value tests, however, dominates all other tests by delivering accurate empirical size and comparatively high power.

GrowChinook: an optimized multimodel and graphic user interface for predicting juvenile Chinook salmon growth in lentic ecosystems

Linked foraging and bioenergetics models allow for increased understanding of fish growth potential and behavior by incorporating prey availability coupled to environmental conditions including temperature and prey visibility. To inform our understanding of growth and vertical migration patterns of Chinook salmon (Oncorhynchus tshawytscha) inhabiting lentic ecosystems, we linked foraging and bioenergetics models to create GrowChinook ( http://growchinook.fw.oregonstate.edu/ ). This multimodel design and optimization routine has broad applications in examining growth potential and predicting habitat use in stratified environments. We demonstrate the use of GrowChinook for the spring–summer rearing period in three Willamette River basin reservoirs, Oregon, USA. These reservoirs support juvenile spring Chinook salmon that exhibit a novel reservoir-reared life history that includes larger juvenile fish compared with nearby stream-reared subyearlings. Model outputs of predicted growth and depth use patterns based on observed prey distributions and environmental conditions were corroborated by observed empirical size and growth data from other years. Our simulations support diel vertical migration as a tactic that increases growth potential and contribute to understanding juvenile Chinook salmon growth in stratified systems.