We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Find out more about the Kindle Personal Document Service.
We present a new methodology for estimating time-varying conditional skewness. Our model allows for changing means and variances, uses a maximum likelihood framework with instruments, and assumes a non-central t distribution. We apply this method to daily, weekly, and monthly stock returns, and find that conditional skewness is important. In particular, we show that the evidence of asymmetric variance is consistent with conditional skewness. Inclusion of conditional skewness also impacts the persistence in conditional variance.
Investigation of the geographical distribution of schizophrenia and its relationship to socio-demographic factors is useful for planning services.
Individuals with schizophrenia (n=980) were identified by key informants within an inner London borough and point prevalence calculated for broad, Feighner and DSM–III–R schizophrenia. The distribution of cases was tested for significant variation using the Poisson process model. Regression models using the Jarman-8 score and its component variables were tested for their ability to predict the prevalence of schizophrenia.
A high point prevalence of schizophrenia (5.3 per 1000 resident population) was demonstrated. Case distribution showed a marked and significant variation associated with socio-demographic factors. The prediction of prevalence was more accurate for broad than for narrower definitions of schizophrenia; unemployment rate performed best.
Unemployment rates and Jarman-8 scores may provide crude estimates for resource allocation in planning mental health services, highlighting the need for additional services in deprived inner city areas.
Email your librarian or administrator to recommend adding this to your organisation's collection.