astronomy

Neil Shephard

Neil Shephard is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Neil Shephard rather than just read about it. In short: Neil Shephard (born 8 October 1964), FBA, is an econometrician, currently Frank B. Baird Jr., Professor of Science in the Department of Economics and the Department of Statistics at Harvard University.

Key takeaways

Neil Shephard belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Neil Shephard to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Neil Shephard from memory before moving on to harder problems.

Reference excerpt

Neil Shephard (born 8 October 1964), FBA, is an econometrician, currently Frank B. Baird Jr., Professor of Science in the Department of Economics and the Department of Statistics at Harvard University. His most well known contributions are: (i) the formalisation of the econometrics of realised volatility, which nonparametrically estimates the volatility of asset prices, (ii) the introduction of the auxiliary particle filter (signal extraction), (iii) the nonparametric identification of jumps in financial economics, through multipower variation, (iv) stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes, known as 'Barndorff-Nielsen-Shephard' models.

Early life and education Neil Shephard was born in Plymouth, England, but moved to Norfolk, England, aged one. His mother was Tydfil Shephard (1930–1972), who was a high school teacher. His father was Tom Shephard (1930–2023), who was a Norfolk high school head. Since 1975, the former Conservative MP and government minister Gillian Shephard has been his step-mother. He attended the Marshland High School West Walton (a state comprehensive school), King Edward VII School in King's Lynn and City of Norwich School, where he studied pure mathematics & statistics, economics and politics at A-level. He studied economics and statistics as an undergraduate at the University of York in the UK (1983–86), and was awarded a first class degree with distinction. He did his M.Sc. (awarded in 1987, with distinction) and Ph.D. (examined in 1989 and graduated in 1990) at the LSE.

Academic career He was a lecturer in statistics at the LSE from 1988 to 1993. He moved to Nuffield College, Oxford in 1991 to join the economics group as the Gatsby Prize Research Fellow in Econometrics (funded by the Gatsby Foundation). In 1993 he became an Official Fellow in Economics at Nuffield College, Oxford. He has been Professor of Economics and of Statistics at Harvard University since 2013. He was elected a Fellow of the British Academy in 2006, a Fellow of the Econometric Society in 2004. He was awarded an honorary doctorate in economics by Aarhus University in 2009, the 2012 Richard Stone Prize in Applied Econometrics and the 2017 Guy Medal in Silver of the Royal Statistical Society. With David F. Hendry he founded the Econometrics Journal in 1998. With Colin Mayer he founded Oxford University's Masters in Financial Economics. In 2007 he co-founded the Oxford-Man Institute, which he directed from 2007 to 2011. He chaired the Statistics Department at Harvard from 2015 to 2022. With Computer Science and Statistical colleagues, he founded Harvard University's Masters in Data Science in 2018 and the Harvard Data Science Initiative.

Publications

Representative articles Iavor Bojinov and Neil Shephard (2019) "Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading", Journal of the American Statistical Association, 114, 1665-1682. Luke Bornn, Neil Shephard and Reza Solgi (2019) "Moment conditions and Bayesian nonparametrics", Journal of Royal Statistical Society, 81, 5-43. Ole E. Barndorff-Nielsen, Peter Reinhard Hansen, Asger Lunde, Neil Shephard (2008), "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise", Econometrica. Vol. 76, pp. 1481–1536 G. Fiorentini, Enrique Sentana and Neil Shephard (2004) Likelihood-based estimation of latent generalised ARCH structures, Econometrica, 2004, 72, 1481–1517. Ole E. Barndorff-Nielsen and Neil Shephard (2004) Econometric analysis of realised covariation: high frequency based covariance, regression and correlation in financial economics, Econometrica, 72, 885–925. Ole E. Barndorff-Nielsen and Neil Shephard (2004) Power and bipower variation with stochastic volatility and jumps (with discussion) Journal of Financial Econometrics, 2004, 2, 1–48. Ole E. Barndorff-Nielsen and Neil Shephard (2002) Econometric analysis of realised volatility and its use in estimating stochastic volatility models, Journal of the Royal Statistical Society, Series B, 63, 2002, 253–280. Ole E. Barndorff-Nielsen and Neil Shephard (2001) Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics, (with discussion), Journal of the Royal Statistical Society, Series B, 63, 2001, 167–241. Michael K. Pitt and Neil Shephard (1999) Filtering via simulation: auxiliary particle filter, Journal of the American Statistical Association, 94, 1999, 590–599. Sangjoon Kim, Siddhartha Chib and Neil Shephard (1998) Stochastic volatility: likelihood inference and comparison with ARCH models, Review of Economic Studies, 65, 1998, 361–393. Andrew C. Harvey, Esther Ruiz and Neil Shephard (1994) Multivariate stochastic variance models, Review of Economic Studies 61, 1994, 247–264.

Edited volumes Neil Shephard (2005) Stochastic Volatility: Selected Readings, edited volume, Oxford University Press.

References

Illustrations

Worked examples

Example 1 — a first encounter with Neil Shephard

Start with the simplest possible case. Write down what Neil Shephard claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Neil Shephard before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Neil Shephard ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Neil Shephard

In research: Neil Shephard appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Neil Shephard in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Neil Shephard is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1964 births, 20th-century British economists, 21st-century British economists, so understanding it makes those chapters shorter.
In everyday life: Look for Neil Shephard outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Neil Shephard in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Neil Shephard means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Neil Shephard out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Neil Shephard in simple terms?

Why does Neil Shephard matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Neil Shephard?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Neil Shephard.