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Nowcasting with dynamic masking
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.ORCID iD: 0000-0001-6684-8088
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Nowcasting consists of tracking GDP by focusing on the data flow consisting oftimely available releases. The essential features of a nowcasting data set are differing sampling frequencies, and the ragged-edge: the differing patterns of missing observations at the end of the sample due to non-synchronicity of data publications. Various econometric frameworks have been proposed to deal withthese characteristics. During a sequence of subsequent nowcast occasions, the models are traditionally re-estimated, or updated, based on an expanding dataset as more and more data becomes available. This paper proposes to take the ragged-edge structure into account when estimating a nowcast model. Instead of using all available historical data, it is here proposed to first mask the historical data so as to reflect the pattern of data availability at the specific nowcast occasion. Since each nowcast occasion exhibits its own specific ragged-edge structure, we propose to re-estimate or recalibrate the model at each juncture employing the accompanying mask, hence dynamic masking.Dynamic masking thus tailors the model to the specific nowcast occasion. It is shown how tailoring improves precision by employing ridge regressions with and without masking in a real-time nowcasting back-test.

The masking approach is motivated by theory and demonstrated on real data. It surpasses the dynamic factor model in backtests. Dynamic masking gives ease of implementation, a solid theoretical foundation, flexibility in modeling, and encouraging results; we therefore consider it a relevant addition to the nowcasting methodology.

Keyword [en]
Nowcasting; Time-series; Masking; Ragged edge; Machine Learning.
National Category
Probability Theory and Statistics
Research subject
Economics
Identifiers
URN: urn:nbn:se:kth:diva-193923OAI: oai:DiVA.org:kth-193923DiVA: diva2:1034583
Funder
Swedish Research Council, 2009-5834
Note

QC 20161013

Available from: 2016-10-12 Created: 2016-10-12 Last updated: 2016-10-13Bibliographically approved
In thesis
1. Inference in Temporal Graphical Models
Open this publication in new window or tab >>Inference in Temporal Graphical Models
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis develops mathematical tools used to model and forecast different economic phenomena. The primary starting point is the temporal graphical model. Four main topics, all with applications in finance, are studied.

The first two papers develop inference methods for networks of continuous time Markov processes, so called Continuous Time Bayesian Networks. Methodology for learning the structure of the network and for doing inference and simulation is developed. Further, models are developed for high frequency foreign exchange data.

The third paper models growth of gross domestic product (GDP) which is observed at a very low frequency. This application is special and has several difficulties which are dealt with in a novel way using a framework developed in the paper. The framework is motivated using a temporal graphical model. The method is evaluated on US GDP growth with good results.

The fourth paper study inference in dynamic Bayesian networks using Monte Carlo methods. A new method for sampling random variables is proposed. The method divides the sample space into subspaces. This allows the sampling to be done in parallel with independent and distinct sampling methods on the subspaces. The methodology is demonstrated on a volatility model for stock prices and some toy examples with promising results.

The fifth paper develops an algorithm for learning the full distribution in a harness race, a ranked event. It is demonstrated that the proposed methodology outperforms logistic regression which is the main competitor. It also outperforms the market odds in terms of accuracy.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2016. 16 p.
Series
TRITA-MAT-A, 2016:08
National Category
Probability Theory and Statistics
Research subject
Applied and Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-193934 (URN)978-91-7729-115-2 (ISBN)
Public defence
2016-10-21, F3, Lindstedtsvagen, Stockholm, 13:00
Opponent
Supervisors
Note

QC 20161013

Available from: 2016-10-13 Created: 2016-10-12 Last updated: 2016-10-13Bibliographically approved

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CiteExportLink to record
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Citation style
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