Dynamic regressions with variables observed at different frequencies
We consider the problem of formulating and estimating dynamic regression models with variables observed at different frequencies. The strategy adopted is to define the dynamics of the model in terms of the highest available frequency, and to apply certain lag polynomials to transform the dynamics so...
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sg-smu-ink.soe_research-29012019-04-20T01:03:40Z Dynamic regressions with variables observed at different frequencies ABEYSINGHE, Tilak TAY, Anthony S. We consider the problem of formulating and estimating dynamic regression models with variables observed at different frequencies. The strategy adopted is to define the dynamics of the model in terms of the highest available frequency, and to apply certain lag polynomials to transform the dynamics so that the model is expressed solely in terms of observed variables. A general solution is provided for models with monthly and quarterly observations. We also show how the methods can be extended to models with quarterly and annual observations, and models combining monthly and annual observations. 2000-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1902 https://ink.library.smu.edu.sg/context/soe_research/article/2901/viewcontent/SOEDynamic_regressions_with_variables_observed_at_different_frequencies.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Variables of different frequencies dynamic regressions temporal aggregation systematic sampling lag polynomials serial correlation Econometrics |
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Variables of different frequencies dynamic regressions temporal aggregation systematic sampling lag polynomials serial correlation Econometrics ABEYSINGHE, Tilak TAY, Anthony S. Dynamic regressions with variables observed at different frequencies |
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We consider the problem of formulating and estimating dynamic regression models with variables observed at different frequencies. The strategy adopted is to define the dynamics of the model in terms of the highest available frequency, and to apply certain lag polynomials to transform the dynamics so that the model is expressed solely in terms of observed variables. A general solution is provided for models with monthly and quarterly observations. We also show how the methods can be extended to models with quarterly and annual observations, and models combining monthly and annual observations. |
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ABEYSINGHE, Tilak TAY, Anthony S. |
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ABEYSINGHE, Tilak TAY, Anthony S. |
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ABEYSINGHE, Tilak |
title |
Dynamic regressions with variables observed at different frequencies |
title_short |
Dynamic regressions with variables observed at different frequencies |
title_full |
Dynamic regressions with variables observed at different frequencies |
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Dynamic regressions with variables observed at different frequencies |
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Dynamic regressions with variables observed at different frequencies |
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dynamic regressions with variables observed at different frequencies |
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Institutional Knowledge at Singapore Management University |
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2000 |
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https://ink.library.smu.edu.sg/soe_research/1902 https://ink.library.smu.edu.sg/context/soe_research/article/2901/viewcontent/SOEDynamic_regressions_with_variables_observed_at_different_frequencies.pdf |
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