In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Converging evidence now indicates that the amygdala plays a crucial role in the development and expression of conditioned fear. Conditioned fear is a hypothetical construct used to explain the cluster of behavioral effects produced when an initially neutral stimulus is consistently paired with an aversive stimulus. For example, when a light, which initially has no behavioral effect, is paired with an aversive stimulus such as a footshock, the light alone can elicit a constellation of behaviors that are typically used to define a state of fear in animals. To explain these findings, it is generally assumed (cf. McAllister & McAllister 1971 ) that during light-shock pair­ ings (training session ), the shock elicits a variety of behaviors that can be used to infer a central state of fear (unconditioned responses-Figure 1 ). After pairing , the light can produce the same central fear state and thus the same set of bchaviors formerly produced by thc shock. Moreover, the behavioral effects that are produced in animals by this formerly neutral stimulus (now called a conditioned stimulus-CS ) are similar in many respects to the constellation of behaviors that are used to diagnose gener­ alized anxiety in humans (Table 1 ). This chapter summarizes data sup­ porting the idea that the amygdala, and its many efferent projections, may represent a central fear system involved in both the expression and acquisition of conditioned fear.

The role of the amygdala in fear and anxiety

Evidence from many different laboratories using a variety of experimental techniques and animal species indicates that the amygdala plays a crucial role in conditioned fear and anxiety, as well as attention. Many amygdaloid projection areas are critically involved in specific signs used to measure fear and anxiety. Electrical stimulation of the amygdala elicits a pattern of behaviors that mimic natural or conditioned fear. Lesions of the amygdala block innate or conditioned fear, as well as various measures of attention, and local infusions of drugs into the amygdala have anxiolytic effects in several behavioral tests. N-methyl-D-aspartate (NMDA) receptors in the amygdala may be important in the acquisition of conditioned fear, whereas non-NMDA receptors are important for the expression of conditioned fear. The peptide corticotropin-releasing hormone appears to be especially important in fear or anxiety and may act within the amygdala to orchestrate parts of the fear reaction.

https://neuro.psychiatryonline.org/doi/pdfplus/10.1176/jnp.9.3.382

Neurobiology of fear responses: The role of the amygdala.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.ap.5500015.pdf

The Macro Polity

BackgroundMemory impairment is being recognized increasingly as an important feature of the neuropsychology of schizophrenia. Dysfunction of working memory, a system for the short-term storage and manipulation of information, may relate to a number of core symptoms of schizophrenia. Many studies have examined working memory function in schizophrenia but a clear understanding of the nature and extent of any deficit has been elusive.MethodA systematic review and meta-analysis of studies comparing working memory function in subjects with schizophrenia and healthy controls was performed. Following a comprehensive literature search, meta-analyses were conducted on 36 measures of phonological, visuospatial and central executive working memory functioning, encompassing 441 separate results from 187 different studies.ResultsStatistically significant effect sizes were found for all working memory measures, indicating deficits in schizophrenia groups. Some of these were robust findings in the absence of evidence of significant heterogeneity or publication bias. Meta-regression analyses showed that the working memory deficit was not simply explained by discrepancies in current IQ between schizophrenia and control groups.ConclusionsLarge deficits in working memory were demonstrated in schizophrenia groups across all three working memory domains. There were, however, no clear differences across subdomains or between particular working memory tasks. There was substantial heterogeneity across results that could only be partly explained.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0033291708004558

Working memory in schizophrenia: a meta-analysis.

Neuropsychological, olfactory, and hygiene deficits in men with negative symptom schizophrenia

https://www.rba.gov.au/publications/rdp/2008/pdf/rdp2008-05.pdf

Understanding the flattening Phillips curve

Excitant amino acid projections from rat amygdala and thalamus to nucleus accumbens.

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/190780/Estimating DSGE Models with Zero Interest Rate PolicyI.pdf

Estimating DSGE models with zero interest rate policy

The output gap – the difference between actual and potential output – is widely regarded as a useful guide to future inflationary pressures, as well as an important indicator of the state of the economy in its own right. Since the output gap is unobservable, however, its estimation is prone to error, particularly in real time. Errors result both from revisions to the underlying data, as well as from end-point problems that are endemic to econometric procedures used to estimate output gaps. These problems reduce the reliability of output gaps estimated in real time, and lead to questions about their usefulness. We examine 121 vintages of Australian GDP data to assess the seriousness of these problems. Our study, which is the first to address these issues using Australian data, is of interest for the method we use to obtain real-time output-gap estimates. Over the past 28 years, our real-time output-gap estimates show no apparent bias, when compared with final output-gap estimates derived with the benefit of hindsight using the latest available data. Furthermore, the root-mean-square difference between the real-time and final output-gap series is less than 2 percentage points, and the correlation between them is over 0.8. Our general conclusion is that quite good estimates of the output gap can be generated in real time, provided a sufficiently flexible and robust approach is used to obtain them.

Tim Robinson

Papers

Neuropsychological, olfactory, and hygiene deficits in men with negative symptom schizophrenia

Understanding the flattening Phillips curve

Excitant amino acid projections from rat amygdala and thalamus to nucleus accumbens.

Estimating DSGE models with zero interest rate policy

Results | RDP 2002-06: Output Gaps in Real Time: are they Reliable Enough to use for Monetary Policy?