Previous ESRC Research Grant (October 2015-September 2017):

 


"The Analysis of Non-stationary Time Series in Economics and Finance: Cointegration, Trend Breaks, and Mixed Frequency Data" (with Marcus Chambers, Department of Economics, University of Essex)
(Award Number ES/M01147X/1).

The Analysis of Non-stationary Time Series in Economics and Finance: Cointegration, Trend Breaks, and Mixed Frequency Data

Principal Investigator: Robert Taylor, Essex Business School, University of Essex
Co-Investigator: Marcus Chambers, Department of Economics, University of Essex

Summary

Macroeconomic and financial time series are typically non-stationary (or unstable), in that their means, variances and autocovariances evolve over time. As a result standard multivariate time series models can only be validly applied to the changes in these variables. Such models, however, contain no information about any long run relationships between the series, as are often predicted by economic or finance theory. A solution is provided by co-integration analysis which recognises that certain combinations of the variables are stationary (stable). A key example is term structure data, where it is often found that while individual interest rates appear to be unstable, the spreads between the rates appear stable.

Practical co-integration analysis is complicated by the fact that economies periodically undergo episodes of structural change, such as stock market crashes or changes in government regime/policy. Empirical evidence suggests that these episodes often manifest themselves in the form of multiple changes in the underlying deterministic trend component of the variables and/or changes in the volatility of the unanticipated random shocks. Extant co-integration tests can result in misleading inference regarding the presence or otherwise of long run relationships between the variables when these forms of structural change are present. This will typically result in misspecified econometric models with poor forecasting ability. It is therefore important to develop new co-integration tests which can deliver reliable inference in such environments. Doing so constitutes the first part of this project and will involve the development of a new simulation-based (bootstrap) procedure.

In light of the recent financial crisis, attention has increasingly focused on understanding the interactions between the macroeconomy and the financial sector. To do so effectively, econometric methods are needed that are capable of handling the mismatch between the frequencies at which data on the financial sector (eg exchange rates, stock prices) and the macroeconomy (eg GDP) become available, and this constitutes the second part of the project. While financial data can be observed at very high frequencies, macroeconomic data are typically available only monthly at best. The vast majority of methods for modelling multivariate time series assume a common sampling frequency; this typically entails discarding information in the high frequency data by converting it to the lowest frequency. However, high frequency financial data contains information that can affect the future time path of the low frequency data, and its utilisation can enable policymakers to act promptly prior to the macroeconomic data becoming available. For example, a financial crisis can be observed long before its effects on GDP are observed, but the ability to predict what those effects might be, using an econometric model capable of dealing with mixed frequency data, can be an important aid to policy making. Methods to allow for structural changes when dealing with mixed frequency data will also be considered.

The theoretical development, to be conducted using large sample econometric theory, will exploit the expertise and experience of the applicants. Taylor has already examined the behaviour of non-constant volatility on co-integration tests which do not allow for structural change in the trend. Chambers has recently developed methods of combining mixed frequency data that preserve the underlying relationships between the series and has also analysed co-integrated systems under temporal aggregation. This project will build on these foundations.

The practical relevance of the theoretical results will be explored using simulation experiments. We will also provide clear guidance to empirical researchers, through worked examples on key international datasets, and make freely available computer programs, to facilitate the implementation of the new techniques.

Papers, Computer Code and Data-sets

"The Estimation of Continuous Time Models with Mixed Frequency Data", by Marcus Chambers, October 2015 (pdf).  Published under Gold Open Access in, Journal of Econometrics Volume 193, Issue 2, August 2016, Pages 390-404.  Available from the publisher from  https://www.sciencedirect.com/science/article/pii/S0304407616300720?via%3Dihub

"Tests of the Co-integration Rank in VAR Models in the Presence of a Possible Break in Trend at an Unknown Point", by David Harris, Stephen Leybourne and Robert Taylor, published under Gold Open Access in, Journal of Econometrics (2016) Volume 192, pages 451-467 (pdf) (supplementary appendix). Available from the publisher from https://www.sciencedirect.com/science/article/pii/S0304407616300136?via%3Dihub  A Gauss program to implement the methods developed in this paper is available here which is currently set up to run on the GDP data set here.   This program can be run as freeware by using the OxGauss facility in the freeware version of the Ox package available from http://www.doornik.com/products.html .  A copy of slides of research presentations based on the research in this paper presented at conferences run by the Bank of England and the Bank of Portugal (both in June 2016) can be obtained here.

“Testing the Order of Fractional Integration of a Time Series in the Possible Presence of a Trend Break at an Unknown Point” by Fabrizio Iacone, Steve Leybourne and Robert Taylor.  Essex Finance Centre Working Papers University of Essex (also published in the  RePEc working paper series), available from http://econpapers.repec.org/paper/esyuefcwp/19654 .  Final version accepted for publication in Econometric Theory is available here

“Level Shift Estimation in the Presence of Non-stationary Volatility with an Application to the Unit Root Testing Problem" by David Harris, Hsein Kew and Robert Taylor.  Essex Finance Centre Working Papers University of Essex (also published in the  RePEc working paper series), available from https://ideas.repec.org/p/esy/uefcwp/20329.html A copy of slides of a research presentation based on the research in this paper presented at the CFE conference in London in 2017 can be obtained here.

"Time-Varying Parameters in Continuous and Discrete Time" by Marcus Chambers and Robert Taylor, March 2018.  Essex Finance Centre Working Papers University of Essex (also published in the  RePEc working paper series), available from https://ideas.repec.org/p/esy/uefcwp/21684.html

"Sieve-based inference for infinite-variance linear processes" by  Giuseppe Cavaliere, Iliyan Georgiev, and Robert Taylor , published in Annals of Statistics (2016)  Volume 44, Number 4, pages 1467-1494.  Available under Gold Open Access from   https://projecteuclid.org/euclid.aos/1467894705

"Continuous Time Modelling Based on an Exact Discrete Time Representation. Working Paper" by Marcus Chambers, Roderick McCrorie and Michael Thornton, University of Essex, Department of Economics Working paper (2018), available from http://repository.essex.ac.uk/id/eprint/20497

"Frequency Domain Estimation of Cointegrating Vectors with Mixed Frequency and Mixed Sample Data" by Marcus Chambers, University of Essex, Department of Economics Working paper (2017), available from http://repository.essex.ac.uk/21144/1/MFCDT.pdf

Events

A one-day workhop on the theme "Co-integration, Multivariate Time Series Modelling and Structural Change" was held at Essex Business School on Monday July 11th 2016.  The Workshop programme is available here.  

A second one-day workshop on the theme  “Econometric Modelling with Mixed Frequency and Aggregated Data” was held at the Wivenhoe Park Campus of the University of Essex on Wednesday July 5th 2017.  The workshop programme is available here.  

Contact Details

For contact details please see the main page of this website

 

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