Jennifer Castle

Dr. Jennifer Castle

Magdalen College, Oxford University
+1
orcid

Research Focus & Metrics

Academic Career Length

22Years (2005-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
14 April 2015 in Econometrics
Verified
Journal Article
Open access
PDF file
Jennifer CastleJennifer Castle
Jennifer Castle
JDJurgen Doornik
Jurgen Doornik
David HendryDavid Hendry
David Hendry
FPFelix Pretis
Felix Pretis
To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a ‘split-half’ analysis, the simplest specialization of a multiple-path block-search algorithm. Monte Carlo simulations, extended to sequential reduction, confirm the accuracy of nominal significance levels under the null and show retentions when location shifts occur, improving the non-null retention frequency compared to the corresponding impulse-indicator saturation (IIS)-based method and the lasso.

1 August 2012 in Journal of Econometrics
Verified
Journal Article
Closed access
Jennifer CastleJennifer Castle
Jennifer Castle
JDJurgen A. Doornik
Jurgen A. Doornik
David HendryDavid Hendry
David Hendry

3 February 2011 in Journal of Time Series Econometrics
Verified
Journal Article
Closed access
Jennifer CastleJennifer Castle
Jennifer Castle
JDJurgen A Doornik
Jurgen A Doornik
DHDavid F. Hendry
David F. Hendry
We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, Autometrics (Doornik, 2009), show similar properties, but not restricted to orthogonal cases. Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing as well as evaluate selection in dynamic models by costs of search versus costs of inference.

1 January 2015 in International Journal of Forecasting
Verified
Journal Article
Closed access
Jennifer CastleJennifer Castle
Jennifer Castle
Michael ClementsMichael Clements
Michael Clements
David HendryDavid Hendry
David Hendry

1 October 2010 in Journal of Econometrics
Verified
Journal Article
Closed access
Jennifer CastleJennifer Castle
Jennifer Castle
DHDavid F. Hendry
David F. Hendry

1 April 2022 in International Journal of Forecasting
Verified
Journal Article
Open access
JDJurgen Doornik
Jurgen Doornik
Jennifer CastleJennifer Castle
Jennifer Castle
David HendryDavid Hendry
David Hendry
We have been publishing real-time forecasts of confirmed cases and deaths from coronavirus disease 2019 (COVID-19) since mid-March 2020 (published at www.doornik.com/COVID-19). These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative regarding short-term developments but without requiring other assumptions about how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is spreading, or whether preventative policies are effective. Thus, they are complementary to the forecasts obtained from epidemiological models.