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Locally stationary wavelet process models for autoregressive conditional duration data


Key facts

Type of research degree
Application deadline
Ongoing deadline
Country eligibility
International (open to all nationalities, including the UK)
Competition funded
Dr Stuart Barber
School of Mathematics
Research groups/institutes
<h2 class="heading hide-accessible">Summary</h2>

Duration data are a type of time series where we are interested both in the observed value of some variable and also how long it takes for the next event in a related sequence to happen. (For example, how frequently a given asset is traded and at what price, how often a group of animals visit a location and how many animals there are, or patient monitoring data recorded once per heartbeat).<br /> <br /> They can be analysed in two different ways. One is to take the time intervals (durations) as the observations of interest, and then they become a regular time series which can be analysed using standard methods, or methods which have been proposed specifically for duration data. The classic reference here is Engle and Russell (1998). Such data could be analysed using wavelet methods such as wavelet variance (Percival and Walden, 2006) and the locally stationary wavelet process model (Nason, von Sachs and Kroisandt, 2000) to accommodate non-stationarity.<br /> <br /> Another way is to use the values that are observed at the irregular time points and analyse these. There are fewer methods available for analysing irregular time series, and it would be interesting to develop wavelet tools for this. It would be even more interesting to bring the two ideas together and develop methods (wavelet or otherwise) to jointly analyse both the durations and the values observed at the irregular intervals.

<h2 class="heading hide-accessible">Full description</h2>

<p>References</p> <p>Engle, RF &amp; Russell, JR (1998). Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica 66, 1127-1162.</p> <p>Percival, DB &amp; Walden, AT (2006). Wavelet methods for time series analysis.<br /> Cambridge: Cambridge University Press.</p> <p>Nason, GP, Von Sachs, R, &amp; Kroisandt, G (2000). Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(2), 271-292.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Statistics</strong></em> and in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Locally stationary wavelet process models for autoregressive conditional duration data</strong></em>&nbsp;as well as&nbsp;<a href="">Dr Stuart Barber</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p>&nbsp;</p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK&nbsp;</strong>&ndash;&nbsp;The&nbsp;<a href="">Leeds Doctoral Scholarships</a>, <a href="">Akroyd &amp; Brown</a>, <a href="">Frank Parkinson</a> and <a href="">Boothman, Reynolds &amp; Smithells</a> Scholarships are available to UK applicants. &nbsp;<a href="">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK </strong>&ndash; The&nbsp;<a href="">China Scholarship Council - University of Leeds Scholarship</a>&nbsp;is available to nationals of China. The&nbsp;<a href="">Leeds Marshall Scholarship</a>&nbsp;is available to support US citizens.&nbsp; <a href="">Alumni Bursary</a> is available to graduates of the University of Leeds.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding your application, please contact Doctoral College Admission by&nbsp;email:&nbsp;<a href=""></a>, or by telephone: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Stuart Barber by email:&nbsp;&nbsp;<a href=""></a></p>

<h3 class="heading heading--sm">Linked research areas</h3>