Non-parametric direct multi-step estimation for forecasting economic processes

Chevillon, Guillaume; Hendry, David F.

doi:10.1016/j.ijforecast.2004.08.004

Cited by 106 publications

(60 citation statements)

References 30 publications

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“…Given the negative expected correlation between the intercept and slope estimators, positive ψ will have the same effect on the bias of the multistep intercept estimator. This is what Chevillon and Hendry (2005) found in their simulations.…”

Section: Ols Estimatorssupporting

confidence: 85%

“…In this paper, we propose a local-asymptotic model that builds on the work of Kemp (1999), Valkanov (2003), Torous et al (2004), Chevillon and Hendry (2005) and Hjalmarsson (2011). We prove a new key property of direct multi-step estimators, namely their robustness to misspecification of the serial correlation of the error process.…”

Section: Introduction and Overviewmentioning

confidence: 87%

“…In particular if τ is indeed nonzero but very small so that a near linear trend is mistaken for a non zero mean (see for instance Pástor and Stambaugh, 2009, where τ is low when ρ is close to unity). Chevillon and Hendry (2005) have shown that small nonzero drifts can have a significant influence on the multistep forecasts when dealing with non-stationary variables.…”

Section: The Models and Local-asymptotic Assumptionsmentioning

confidence: 99%

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Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons

Chevillon

2017

SSRN Journal

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This paper studies the properties of multi-step projections, and forecasts that are obtained using either iterated or direct methods. The models considered are local asymptotic: they allow for a near unit root and a local to zero drift. We treat short, intermediate and long term forecasting by considering the horizon in relation to the observable sample size. We show the implication of our results for models of predictive regressions used in the financial literature. We show here that direct projection methods at intermediate and long horizons are robust to the potential misspecification of the serial correlation of the regression errors. We therefore recommend, for better global power in predictive regressions, a combination of test statistics with and without autocorrelation correction.

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Section: Ols Estimatorssupporting

confidence: 85%

Section: Introduction and Overviewmentioning

confidence: 87%

Section: The Models and Local-asymptotic Assumptionsmentioning

confidence: 99%

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Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons

Chevillon

2017

SSRN Journal

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“…Poskitt (2003) discussed the specification of cointegrated VARMA systems. Chevillon & Hendry (2005) analyzed the relation between direct multi-step estimation of stationary and non-stationary VARs and forecast accuracy.…”

Section: Multivariatementioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

1,297

632

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During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

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“…In order to overcome with the above mentioned difficulty Litterman (1986) imposed a prior distribution on the parameters with the view that many economic variables behave similar to random walk. Chevillon and Hendry (2005) studied the functional relationship of direct multi-step estimation of stationary and nonstationary VARs and forecast accuracy.…”

Section: State-of-the-artmentioning

confidence: 99%

An Intelligent Decision Support System for Workforce Forecast

Wan¹,

Chen²,

Kuriger³

2011

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information , 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) 05-01-2011 REPORT TYPE 7184D214 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERThe University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)Air SPONSOR/MONITOR'S REPORT NUMBER(S)AFRL-RH-WP-TR-2011-0019 DISTRIBUTION / AVAILABILITY STATEMENTDistribution A: Approved for public release; distributed is unlimited. SUPPLEMENTARY NOTES88ABW/PA cleared on 23 Feb 2011, 88ABW-2011-0727. ABSTRACTThe main objective of this project is to enhance the effectiveness of workforce forecast and deployment through innovative approaches of artificial intelligence. The research included a final document of conventional and innovative methods of workforce forecasting and a decision support software program incorporating advanced artificial intelligence techniques for workforce forecasting. SUBJECT TERMS ABSTRACTThis project studies workforce forecasting in two main aspects: (1) an extensive review of the existing methodologies and techniques and (2) an effort to develop a decision support system with models and software programming. The main objective of this project is to enhance the effectiveness of workforce forecasting and deployment through innovative approaches of artificial intelligence. The deliverables include a summary report of conventional and innovative methods of workforce forecasting (Part I) and a decision support software program using artificial intelligence techniques for workforce forecasting (Part II).Part I, provided a thorough literature review of fundamental research and practices of demand and supply forecasting techniques for workforce analysis. Over 300 relevant literatures have been identified and reviewed by the project team, and 289 of them are covered in this report.Following is a summary of the main contents and contributions of Part I:• This report provides an extensive review of the fundamental knowledge, applications, guidelines, and scope of use of various workforce forecasting methods.• A decision tree has been proposed for selecting an appropriate forecasting ...

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Non-parametric direct multi-step estimation for forecasting economic processes

Cited by 106 publications

References 30 publications

Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons

Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons

25 years of time series forecasting

An Intelligent Decision Support System for Workforce Forecast

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