The application of simple errors in variables model on real data
The Ordinary Least Squares (OLS) method is the most widely used method to estimate the parameters of regression model. One of the critical assumption of the OLS estimation method is that the regression variables are measured without error. However, in many practical situations this assumption is of...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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IEEE
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| _version_ | 1796973564929245184 |
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| author | Mohammadi, Mandana Midi, Habshah Rana, Sohel Arasan, Jayanthi |
| author_facet | Mohammadi, Mandana Midi, Habshah Rana, Sohel Arasan, Jayanthi |
| author_sort | Mohammadi, Mandana |
| collection | UPM |
| description | The Ordinary Least Squares (OLS) method is the
most widely used method to estimate the parameters of regression model. One of the critical assumption of the OLS estimation method is that the regression variables are measured without error. However, in many practical situations this assumption is often violated, whereby both dependent and independent variables are measured with errors. In these situations the OLS estimates lead to inconsistent and biased estimates. Consequently, the parameter estimates do not come closer to the true values,
even in very large sample. To remedy this problem, instrumental variables (IV) estimation technique is utilized. In this article we examine some interesting numerical examples which are related to measurement errors. The results show that the IV estimates is more appropriate than the OLS estimates in such situations. |
| first_indexed | 2024-03-06T08:44:31Z |
| format | Conference or Workshop Item |
| id | upm.eprints-39573 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:44:31Z |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-395732015-07-23T07:28:28Z http://psasir.upm.edu.my/id/eprint/39573/ The application of simple errors in variables model on real data Mohammadi, Mandana Midi, Habshah Rana, Sohel Arasan, Jayanthi The Ordinary Least Squares (OLS) method is the most widely used method to estimate the parameters of regression model. One of the critical assumption of the OLS estimation method is that the regression variables are measured without error. However, in many practical situations this assumption is often violated, whereby both dependent and independent variables are measured with errors. In these situations the OLS estimates lead to inconsistent and biased estimates. Consequently, the parameter estimates do not come closer to the true values, even in very large sample. To remedy this problem, instrumental variables (IV) estimation technique is utilized. In this article we examine some interesting numerical examples which are related to measurement errors. The results show that the IV estimates is more appropriate than the OLS estimates in such situations. IEEE Conference or Workshop Item NonPeerReviewed Mohammadi, Mandana and Midi, Habshah and Rana, Sohel and Arasan, Jayanthi The application of simple errors in variables model on real data. In: International Conference on Statistics in Science, Business and Engineering 2011 (ICSSBE2012), 10-12 Sep. 2012, Langkawi, Kedah. (pp. 1-4). 10.1109/ICSSBE.2012.6396544 |
| spellingShingle | Mohammadi, Mandana Midi, Habshah Rana, Sohel Arasan, Jayanthi The application of simple errors in variables model on real data |
| title | The application of simple errors in variables model on real data |
| title_full | The application of simple errors in variables model on real data |
| title_fullStr | The application of simple errors in variables model on real data |
| title_full_unstemmed | The application of simple errors in variables model on real data |
| title_short | The application of simple errors in variables model on real data |
| title_sort | application of simple errors in variables model on real data |
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