本文關(guān)鍵詞： 穩(wěn)健回歸 異常點(diǎn)診斷 數(shù)據(jù)質(zhì)量　出處：《天津財(cái)經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：準(zhǔn)確的GDP統(tǒng)計(jì)數(shù)據(jù)是一個(gè)國家或地區(qū)正確制定宏觀經(jīng)濟(jì)政策的重要保證和依據(jù)。統(tǒng)計(jì)數(shù)據(jù)質(zhì)量的好壞不僅決定著我國統(tǒng)計(jì)部門的威信,而且對中國的國際地位也有重要意義。此外,隨著穩(wěn)健回歸理論和統(tǒng)計(jì)診斷技術(shù)的日益成熟,從異常點(diǎn)診斷角度評估數(shù)據(jù)質(zhì)量成為一個(gè)重要發(fā)展方向。因此,在穩(wěn)健回歸方法基礎(chǔ)上討論異常點(diǎn)診斷技術(shù),選擇同時(shí)具備統(tǒng)計(jì)意義和經(jīng)濟(jì)意義的模型來評估各地區(qū)GDP數(shù)據(jù)質(zhì)量,極具理論價(jià)值和現(xiàn)實(shí)意義。 論文主要從異常點(diǎn)診斷的角度評估我國各地區(qū)的GDP數(shù)據(jù)質(zhì)量。具體采用蒙特卡洛模擬比較研究了四類穩(wěn)健回歸方法,得到小樣本量情況下受異常點(diǎn)影響較小的回歸方法,進(jìn)而在該穩(wěn)健回歸的基礎(chǔ)上對我國31個(gè)地區(qū)的GDP數(shù)據(jù)進(jìn)行全面的診斷和分析,最后得出各地區(qū)歷年的GDP數(shù)據(jù)質(zhì)量評估結(jié)果。文章主要分為四個(gè)部分：第一部分主要概括了統(tǒng)計(jì)數(shù)據(jù)質(zhì)量的內(nèi)涵和評估方法,重點(diǎn)綜述了穩(wěn)健回歸和異常點(diǎn)診斷方法在GDP數(shù)據(jù)質(zhì)量評估中的應(yīng)用；第二部分采用隨機(jī)模擬技術(shù)比較研究了四種穩(wěn)健回歸估計(jì),得出小樣本下受異常點(diǎn)影響較小的穩(wěn)健回歸方法；第三部分用穩(wěn)健LTS回歸方法實(shí)證診斷我國31個(gè)地區(qū)GDP數(shù)據(jù)中包含的異常點(diǎn)情況,并和基于OLS的異常點(diǎn)診斷結(jié)果進(jìn)行對比；第四部分在異常點(diǎn)診斷的基礎(chǔ)上,使用穩(wěn)健主成份回歸方法印證上一章的診斷結(jié)果,并計(jì)算出2011年我國各地區(qū)GDP增長率的置信區(qū)間。研究結(jié)果表明,在小樣本情況下,不同比率和類型的異常點(diǎn)對LTS估計(jì)影響較��；1978-2011年大部分地區(qū)GDP數(shù)據(jù)異常點(diǎn)在10%以下,且集中出現(xiàn)在經(jīng)濟(jì)轉(zhuǎn)型或經(jīng)濟(jì)危機(jī)時(shí)期；部分地區(qū)2011年的GDP增長率存在高估的現(xiàn)象。 論文的創(chuàng)新之處在于,運(yùn)用隨機(jī)模擬技術(shù)討論了小樣本下異常點(diǎn)類型和比率不同時(shí),穩(wěn)健回歸方法的優(yōu)劣和適用性,并在穩(wěn)健LTS基礎(chǔ)上對我國各地區(qū)歷年GDP數(shù)據(jù)做了全面的異常點(diǎn)診斷和評估。本文不僅對穩(wěn)健回歸方法做了有益的比較,更重要的是對數(shù)據(jù)質(zhì)量評估方法做了進(jìn)一步的探討。
[Abstract]:Accurate GDP statistical data is an important guarantee and basis for a country or region to correctly formulate macroeconomic policies. The quality of statistical data not only determines the prestige of statistical departments in our country. In addition, with the development of robust regression theory and statistical diagnostic technology, evaluating data quality from outlier diagnosis has become an important development direction. On the basis of robust regression method, this paper discusses the technique of anomaly diagnosis, and selects a model with both statistical and economic significance to evaluate the quality of GDP data in various regions, which is of great theoretical and practical significance. In this paper, the quality of GDP data in different regions of China is evaluated from the point of outlier diagnosis. Four kinds of robust regression methods are studied by Monte Carlo simulation. On the basis of the robust regression, the GDP data of 31 regions in China are diagnosed and analyzed comprehensively. Finally, the evaluation results of GDP data quality in various regions are obtained. The paper is divided into four parts: the first part summarizes the connotation and evaluation methods of statistical data quality. The application of robust regression and outlier diagnosis in GDP data quality assessment is reviewed. In the second part, four kinds of robust regression estimation are compared with each other by using stochastic simulation techniques. In the third part, we use robust LTS regression method to diagnose the abnormal points in 31 regions of China, and compare the results with the results of abnormal points based on OLS. 4th based on the diagnosis of outliers, the robust principal component regression method is used to verify the diagnostic results in the previous chapter, and the confidence interval of GDP growth rate in each region of China in 2011 is calculated. The results show that, in the case of small samples, From 1978 to 2011, the outliers of GDP data in most regions were below 10%, and concentrated in the period of economic transition or economic crisis. In 2011, the GDP growth rate in some regions was overestimated. The innovation of this paper lies in the use of stochastic simulation technology to discuss the advantages and disadvantages and applicability of robust regression method with different types and ratios of outliers in small samples. On the basis of robust LTS, this paper makes a comprehensive diagnosis and evaluation of the outliers of GDP data in various regions of China. This paper not only makes a beneficial comparison to the robust regression method, but also makes a further discussion on the evaluation method of data quality.
【學(xué)位授予單位】：天津財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F224;F127

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