一個社會經濟學的目標變量，經常存在兩種不同收集頻率的數據。由於較低頻率的一組數據通常由大型普查中所獲得，其準確度及可靠性會較高。因此較低頻率的一組數據一般會視作基準，用作對頻率較高的另一組數據進行修正。 / 在基準修正過程中，一般會假設調查誤差及目標數據的大小互相獨立，即「累加模型」。然而，現實中兩者通常是相關的，目標變量越大，調查誤差亦會越大，即「乘積模型」。對此問題，陳兆國及胡家浩提出了利用準線性回歸手法對乘積模型進行基準修正。在本論文中，假設調查誤差服從AR(1)模型，首先我們會示範如何利用準線性回歸手法及默認調查誤差模型進行基準數據修正。然後，運用基準預測的方式，提出一個對調查誤差模型的估計辦法。最後我們會比較兩者的表現以及一些選擇誤差模型的指引。 / For a target socio-economic variable, two sources of data with different collecting frequencies may be available in survey data analysis. In general, due to the difference of sample size or the data source, two sets of data do not agree with each other. Usually, the more frequent observations are less reliable, and the less frequent observations are much more accurate. In benchmarking problem, the less frequent observations can be treated as benchmarks, and will be used to adjust the higher frequent data. / In the common benchmarking setting, the survey error and the target variable are always assumed to be independent (Additive case). However, in reality, they should be correlated (Multiplicative case). The larger the variable, the larger the survey error. To deal with this problem, Chen and Wu (2006) proposed a regression method called quasi-linear regression for the multiplicative case. In this paper, by assuming the survey error to be an AR(1) model, we will demonstrate the benchmarking procedure using default error model for the quasi-linear regression. Also an error modelling procedure using benchmark forecast method will be proposed. Finally, we will compare the performance of the default error model with the fitted error model. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Luk, Wing Pan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 56-57). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Recent Development For Benchmarking Methods --- p.2 / Chapter 1.2 --- Multiplicative Case And Benchmarking Problem --- p.3 / Chapter 2 --- Benchmarking With Quasi-linear Regression --- p.8 / Chapter 2.1 --- Iterative Procedure For Quasi-linear Regression --- p.9 / Chapter 2.2 --- Prediction Using Default Value φ --- p.16 / Chapter 2.3 --- Performance Of Using Default Error Model --- p.17 / Chapter 3 --- Estimation Of φ Via BM Forecasting method --- p.26 / Chapter 3.1 --- Benchmark Forecasting Method --- p.26 / Chapter 3.2 --- Performance Of Benchmark Forecasting Method --- p.28 / Chapter 4 --- Benchmarking By The Estimated Value --- p.34 / Chapter 4.1 --- Benchmarking With The Estimated Error Model --- p.35 / Chapter 4.2 --- Performance Of Using Estimated Error Model --- p.36 / Chapter 4.3 --- Suggestions For Selecting Error Model --- p.45 / Chapter 5 --- Fitting AR(1) Model For Non-AR(1) Error --- p.47 / Chapter 5.1 --- Settings For Non-AR(1) Model --- p.47 / Chapter 5.2 --- Simulation Studies --- p.48 / Chapter 6 --- An Illustrative Example: The Canada Total Retail Trade Se-ries --- p.50 / Chapter 7 --- Conclusion --- p.54 / Bibliography --- p.56
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328803 |
| Date | January 2012 |
| Contributors | Luk, Wing Pan., Chinese University of Hong Kong Graduate School. Division of Statistics. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource (ix, 57 leaves) : ill. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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