Detecting Common Dynamics in Transitory Components

This paper considers VAR/VECM models for variables exhibiting cointegration and common features in the transitory components. While the presence of cointegration reduces the rank of the long-run multiplier matrix, other types of common features lead to rank reduction in the short-run dynamics. These common transitory components arise when linear combination of the first differenced variables in a cointegrated VAR are white noise. This paper offers a reinterpretation of the traditional approach to testing for common feature dynamics, namely checking for a singular covariance matrix for the transitory components. Instead, the matrix of short-run coefficients becomes the focus of the testing procedure thus allowing a wide range of tests for reduced rank in parameter matrices to be potentially relevant tests of common transitory components. The performance of the different methods is illustrated in a Monte Carlo analysis which is then used to reexamine an existing empirical study. Finally, this approach is applied to analyze whether one would observe common dynamics in standard DSGE models.Transitory components, common features, reduced rank, cointegration.

The Devil is in the Detail: Hints for Practical Optimisation

Finding the minimum of an objective function, such as a least squares or negative log-likelihood function, with respect to the unknown model parameters is a problem often encountered in econometrics. Consequently, students of econometrics and applied econometricians are usually well-grounded in the broad differences between the numerical procedures employed to solve these problems. Often, however, relatively little time is given to understanding the practical subtleties of implementing these schemes when faced with illbehaved problems. This paper addresses some of the details involved in practical optimisation, such as dealing with constraints on the parameters, specifying starting values, termination criteria and analytical gradients, and illustrates some of the general ideas with several instructive examples.gradient algorithms, unconstrained optimisation, generalised method of moments.

Discrete time-series models when counts are unobservable

Count data in economics have traditionally been modeled by means of integer-valued autoregressive models. Consequently, the estimation of the parameters of these models and their asymptotic properties have been well documented in the literature. The models comprise a description of the survival of counts generally in terms of a binomial thinning process and an independent arrivals process usually specified in terms of a Poisson distribution. This paper extends the existing class of models to encompass situations in which counts are latent and all that is observed is the presence or absence of counts. This is a potentially important modification as many interesting economic phenomena may have a natural interpretation as a series of 'events' that are driven by an underlying count process which is unobserved. Arrivals of the latent counts are modeled either in terms of the Poisson distribution, where multiple counts may arrive in the sampling interval, or in terms of the Bernoulli distribution, where only one new arrival is allowed in the same sampling interval. The models with latent counts are then applied in two practical illustrations, namely, modeling volatility in financial markets as a function of unobservable 'news' and abnormal price spikes in electricity markets being driven by latent 'stress'.Integer-valued autoregression, Poisson distribution, Bernoulli distribution, latent factors, maximum likelihood estimation

Imagery enhancements increase the effectiveness of cognitive behavioural group therapy for social anxiety disorder: A benchmarking study

Emerging evidence suggests that imagery-based techniques may enhance the effectiveness of traditional verbal-linguistic cognitive interventions for emotional disorders. This study extends an earlier pilot study by reporting outcomes from a naturalistic trial of an imagery-enhanced cognitive behavioural group therapy (IE-CBGT, n=53) protocol for social anxiety disorder (SAD), and comparing outcomes to historical controls who completed a predominantly verbally-based group protocol (n=129). Patients were consecutive referrals from health professionals to a community clinic specialising in anxiety and mood disorders. Both treatments involved 12, two-hour group sessions plus a one-month follow-up. Analyses evaluated treatment adherence, predictors of dropout, treatment effect sizes, reliable and clinically significant change, and whether self-reported tendencies to use imagery in everyday life and imagery ability predicted symptom change. IE-CBGT patients were substantially more likely to complete treatment than controls (91% vs. 65%). Effect sizes were very large for both treatments, but were significantly larger for IE-CBGT. A higher proportion of the IE-CBGT patients achieved reliable change, and better imagery ability was associated with larger symptom change. Outcomes compared very favourably to published group and individual treatments for SAD, suggesting that IE-CBGT may be a particularly effective and efficient mode of treatment delivery

Loudly sing cuckoo : More-than-human seasonalities in Britain

This research was funded by a grant from the Arts and Humanities Research Council, grant number AH/E009573/1.Peer reviewedPostprin

Group metacognitive therapy for repetitive negative thinking in primary and non-primary generalized anxiety disorder: An effectiveness trial

Background Generalized anxiety disorder (GAD) is a common and highly comorbid anxiety disorder characterized by repetitive negative thinking (RNT). Treatment trials tend to exclude individuals with non-primary GAD, despite this being a common presentation in real world clinics. RNT is also associated with multiple emotional disorders, suggesting that it should be targeted regardless of the primary disorder. This study evaluated the acceptability and effectiveness of brief group metacognitive therapy (MCT) for primary or non-primary GAD within a community clinic. Methods Patients referred to a specialist community clinic attended six, two-hour weekly sessions plus a one-month follow-up (N=52). Measures of metacognitive beliefs, RNT, symptoms, positive and negative affect, and quality of life were completed at the first, last, and follow-up sessions. Results Attrition was low and large intent-to-treat effects were observed on most outcomes, particularly for negative metacognitive beliefs and RNT. Treatment gains increased further to follow-up. Benchmarking comparisons demonstrated that outcomes compared favorably to longer disorder-specific protocols for primary GAD. Limitations No control group or independent assessment of protocol adherence. Conclusions Brief metacognitive therapy is an acceptable and powerful treatment for patients with primary or non-primary GAD

Classification of molecular sequence data using Bayesian phylogenetic mixture models

Rate variation among the sites of a molecular sequence is commonly found in applications of phylogenetic inference. Several approaches exist to account for this feature but they do not usually enable the investigator to pinpoint the sites that evolve under one or another rate of evolution in a straightforward manner. The focus is on Bayesian phylogenetic mixture models, augmented with allocation variables, as tools for site classification and quantification of classification uncertainty. The method does not rely on prior knowledge of site membership to classes or even the number of classes. Furthermore, it does not require correlated sites to be next to one another in the sequence alignment, unlike some phylogenetic hidden Markov or change-point models. In the approach presented, model selection on the number and type of mixture components is conducted ahead of both model estimation and site classification; the steppingstone sampler (SS) is used to select amongst competing mixture models. Example applications of simulated data and mitochondrial DNA of primates illustrate site classification via ‘augmented’  Bayesian phylogenetic mixtures. In both examples, all mixtures outperform commonly-used models of among-site rate variation and models that do not account for rate heterogeneity. The examples further demonstrate how site classification is readily available from the analysis output. The method is directly relevant to the choice of partitions in Bayesian phylogenetics, and its application may lead to the discovery of structure not otherwise recognised in a molecular sequence alignment. Computational aspects of Bayesian phylogenetic model estimation are discussed, including the use of simple Markov chain Monte Carlo (MCMC) moves that mix efficiently without tempering the chains. The contribution to the field of Bayesian phylogenetics is in (1) the use of mixture models augmented with allocation variables as tools for site classification and quantification of classification uncertainty, (2) the successful application of SS for selection of phylogenetic mixtures, and (3) the development of novel MCMC aspects of relevance to Bayesian phylogenetic models—whether mixtures or not.1&nbsp

Animal-Computer Interaction (ACI): a manifesto

Although we have involved animals in machine and computer interactions for a long time, their perspective has seldom driven the design of interactive technology meant for them and animal-computer interaction is yet to enter mainstream user-computer interaction research. This lack of animal perspective can have negative effects on animal users and on the purposes for which animal technology is developed. Not only could an Animal-Computer Interaction (ACI) agenda mitigate those effects, it could also yield multiple benefits, by enhancing our inter-species relationships with the animals we live or work with, leading to further insights into animal cognition, rendering conservation efforts more effective, improving the economical and ethical sustainability of food production, expanding the horizon of user-computer interaction research altogether and benefiting different groups of human users too. Advances in both our understanding of animal cognition and computing technology make the development of ACI as a discipline both possible and timely, while pressing environmental, economic and cultural changes make it desirable. But what exactly is ACI about and how could we develop such a discipline? This Manifesto describes the scientific aims, methodological approach and ethical principles of ACI and proposes a research agenda for its systematic development