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A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation

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A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation. / Hall, S.G.; Gibson, H.D.; Tavlas, G.S.; Tsionas, M.G.

In: Computational Economics, Vol. 56, 01.06.2020, p. 115–130.

Research output: Contribution to journalJournal article

Harvard

Hall, SG, Gibson, HD, Tavlas, GS & Tsionas, MG 2020, 'A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation', Computational Economics, vol. 56, pp. 115–130. https://doi.org/10.1007/s10614-018-9878-6

APA

Hall, S. G., Gibson, H. D., Tavlas, G. S., & Tsionas, M. G. (2020). A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation. Computational Economics, 56, 115–130. https://doi.org/10.1007/s10614-018-9878-6

Vancouver

Hall SG, Gibson HD, Tavlas GS, Tsionas MG. A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation. Computational Economics. 2020 Jun 1;56:115–130. https://doi.org/10.1007/s10614-018-9878-6

Author

Hall, S.G. ; Gibson, H.D. ; Tavlas, G.S. ; Tsionas, M.G. / A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation. In: Computational Economics. 2020 ; Vol. 56. pp. 115–130.

Bibtex

@article{6437e8f720df4ea0820b9e8e07d3ebb3,
title = "A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation",
abstract = "A number of recent papers have proposed a time-varying-coefficient (TVC) procedure that, in theory, yields consistent parameter estimates in the presence of measurement errors, omitted variables, incorrect functional forms, and simultaneity. The key element of the procedure is the selection of a set of driver variables. With an ideal driver set the procedure is both consistent and efficient. However, in practice it is not possible to know if a perfect driver set exists. We construct a number of Monte Carlo experiments to examine the performance of the methodology under (i) clearly-defined conditions and (ii) a range of model misspecifications. We also propose a new Bayesian search technique for the set of driver variables underlying the TVC methodology. Experiments are performed to allow for incorrectly specified functional form, omitted variables, measurement errors, unknown nonlinearity and endogeneity. In all cases except the last, the technique works well in reasonably small samples. {\textcopyright} 2018, The Author(s).",
keywords = "Monte Carlo, Specification errors, Time-varying coefficients",
author = "S.G. Hall and H.D. Gibson and G.S. Tavlas and M.G. Tsionas",
year = "2020",
month = jun
day = "1",
doi = "10.1007/s10614-018-9878-6",
language = "English",
volume = "56",
pages = "115–130",
journal = "Computational Economics",
issn = "0927-7099",
publisher = "Springer Netherlands",

}

RIS

TY - JOUR

T1 - A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation

AU - Hall, S.G.

AU - Gibson, H.D.

AU - Tavlas, G.S.

AU - Tsionas, M.G.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - A number of recent papers have proposed a time-varying-coefficient (TVC) procedure that, in theory, yields consistent parameter estimates in the presence of measurement errors, omitted variables, incorrect functional forms, and simultaneity. The key element of the procedure is the selection of a set of driver variables. With an ideal driver set the procedure is both consistent and efficient. However, in practice it is not possible to know if a perfect driver set exists. We construct a number of Monte Carlo experiments to examine the performance of the methodology under (i) clearly-defined conditions and (ii) a range of model misspecifications. We also propose a new Bayesian search technique for the set of driver variables underlying the TVC methodology. Experiments are performed to allow for incorrectly specified functional form, omitted variables, measurement errors, unknown nonlinearity and endogeneity. In all cases except the last, the technique works well in reasonably small samples. © 2018, The Author(s).

AB - A number of recent papers have proposed a time-varying-coefficient (TVC) procedure that, in theory, yields consistent parameter estimates in the presence of measurement errors, omitted variables, incorrect functional forms, and simultaneity. The key element of the procedure is the selection of a set of driver variables. With an ideal driver set the procedure is both consistent and efficient. However, in practice it is not possible to know if a perfect driver set exists. We construct a number of Monte Carlo experiments to examine the performance of the methodology under (i) clearly-defined conditions and (ii) a range of model misspecifications. We also propose a new Bayesian search technique for the set of driver variables underlying the TVC methodology. Experiments are performed to allow for incorrectly specified functional form, omitted variables, measurement errors, unknown nonlinearity and endogeneity. In all cases except the last, the technique works well in reasonably small samples. © 2018, The Author(s).

KW - Monte Carlo

KW - Specification errors

KW - Time-varying coefficients

U2 - 10.1007/s10614-018-9878-6

DO - 10.1007/s10614-018-9878-6

M3 - Journal article

VL - 56

SP - 115

EP - 130

JO - Computational Economics

JF - Computational Economics

SN - 0927-7099

ER -