本文選題：人口預測 + 預測區(qū)間�。� 參考：《東北財經(jīng)大學》2013年碩士論文

【摘要】：人口預測作為區(qū)域規(guī)劃和政策決策的依據(jù),對于區(qū)域經(jīng)濟社會可持續(xù)發(fā)展有重要的理論和現(xiàn)實意義。從宏觀上說,人口預測可提供今后幾十年乃至上百年內(nèi)全國各年齡段的兒童和青少年數(shù)量,這對于勞動就業(yè)和教育規(guī)劃是至關重要的；從微觀上講,預測某一地區(qū)的人口,則會為區(qū)域的基礎設施建設、資金投入提供幫助。例如：合并學校、增加電廠、重新設計城市規(guī)劃、房地產(chǎn)的開發(fā)等。然而到目前為止,雖已有不少學者使用時序模型進行了人口預測,但以區(qū)域數(shù)據(jù)作為分析樣本,從歷史區(qū)間長度、預測的臨界年和預測區(qū)間角度選擇最優(yōu)模型的研究幾乎沒有。 本文第一部分簡要介紹了選擇此論題的意義與背景、國內(nèi)外的研究成果、研究的思路與內(nèi)容、研究方法、和本文的創(chuàng)新與不足之處；第二部分利用一個優(yōu)良的ARIMA人口時序模型,得出預測誤差作為因變量,把有可能影響預測精度的因素作為自變量,做定量的回歸研究,并對影響預測精度的因素進行分析；第三部分首先利用多個ARIMA模型對我國部分具有代表性的省域人口進行預測,然后考慮第二部分得出的影響精度的因素,探討了在不同性質(zhì)的模型、不同地區(qū)、基區(qū)和預測區(qū)間等條件下人口的時序預測模型選擇的一般性規(guī)律；第四部分是對前兩部分論述的總結,最后附上主要的參考文獻以及后記。研究結果發(fā)現(xiàn),人口數(shù)目和增長率對人口精度的影響皆呈U型；臨界年和地區(qū)因素對人口精度也有較大影響,以及對于預測精度來說,各因素的相對影響權重。在模型選擇過程中,一些ARIMA模型能夠提供相對精確的結果,而另一些則不能；線性模型和非線性模型在省域人口預測精度方面具有較大的差異；歷史數(shù)據(jù)長度不同也可能導致選擇不同的模型；從不同角度觀測的模型精度有較強的一致性,但也存在一定程度的不一致性,以上結論可以為選擇人口時序預測模型提供更深入的參考,并為以后的研究指明方向與建議。 本文的創(chuàng)新之處在于,首先沒有局限在人口總量數(shù)據(jù)上進行分析,而是進行了分省份人口數(shù)據(jù)的建模與分析,從而使分析結果更加細致準確。其次,第二部分在省份數(shù)據(jù)的基礎上通過一個人口時序模型總結出影響人口預測精度的因素,然后把這些因素加入到關于預測精度的回歸模型中,得出各因素影響的權重,省份、歷史長度,這些因素對預測精度的影響還是比較明顯的,這在以往的文獻中很少提到。在做變量的回歸模型中,不但選取了一次變量,而且選取了變量的二次形式,使模型的擬合優(yōu)度得到明顯提高。再次,在第三部分進行ARIMA模型的評價標準上,運用了從不同基區(qū)間,不同預測區(qū)間的預測有效性評價標準,然后從不同角度選擇時序模型,這點在國內(nèi)研究中也比較少見。以上從數(shù)據(jù)的選取,變量的篩選,模型的選擇三方面相比于以往的研究都有很大的創(chuàng)新。本文的不足之處在于雖然加入了一些有關的變量因素,但是變量以及變量的形式選取是否具有完全代表性?在從多角度選擇模型時,本文得出的結果對于其他省份是否適用?有沒有其他的模型更加適合于省域人口的預測?這些問題希望可以在以后的研究中得到解決。
[Abstract]:As the basis of regional planning and policy decision, population forecasting is of great theoretical and practical significance to the sustainable development of regional economy and society. From the macro point of view, population prediction can provide the number of children and adolescents in all ages of the country in the next hundred years, which is vital to employment and education planning. On the microcosmic point of view, the population of a region will be predicted for the infrastructure construction of the region, and the funds will be provided to help. For example, combining schools, increasing power plants, redesigning urban planning, developing real estate, and so on. There is little research on the selection of optimal models from the perspective of the historical interval length, the critical year and the forecast interval.
The first part of this paper briefly introduces the significance and background of choosing the topic, the research results at home and abroad, the thinking and content of the research, the research method, and the innovation and inadequacies of this paper. The second part makes use of a good ARIMA population time series model to get the prediction error as the dependent variable and make the factors that may affect the prediction accuracy. The quantitative regression study is made for the independent variables, and the factors affecting the prediction accuracy are analyzed. The third part first makes use of multiple ARIMA models to predict some of the representative provincial population in China, and then takes into account the factors of the influence accuracy of the second parts, and discusses the different properties of the models, different regions, base areas and prepositions. The general rule of selecting the time series prediction model of population under the condition of measuring interval and so on; the fourth part is the summary of the first two parts, and finally the main references and the postscript. The results show that the population and the growth rate have the U type on the population precision, and the annual and regional factors are also larger for the population precision. In the process of model selection, some ARIMA models can provide relatively accurate results, while others can not. The linear and nonlinear models have great differences in the precision of population prediction in the province, and the difference in the length of historical data may also lead to selection. Different models are chosen; the accuracy of the models observed from different angles has a strong consistency, but there is a certain degree of inconsistency. The above conclusions can provide a more in-depth reference for the selection of the population timing prediction model, and point out the direction and suggestions for the future research.
The innovation of this paper is that, firstly, the analysis of population data is not limited, but the model and analysis of the population data in the provinces are carried out to make the analysis result more detailed and accurate. Secondly, the second part summarizes the factors that affect the accuracy of population prediction on the basis of a personal order model on the basis of the province data. Then, these factors are added to the regression model of prediction accuracy, and the weight of the influence of each factor, the province, the history length, and the influence of these factors on the prediction accuracy are still obvious, which is rarely mentioned in the previous literature. In the regression model of variable variables, not only a variable is selected, but also the two of the variable is selected. In the second form, the goodness of fit of the model is obviously improved. Thirdly, in the third part of the evaluation standard of the ARIMA model, the evaluation criteria of the prediction validity are used from different intervals, different prediction intervals, and then the time series model is selected from different angles. This is also rare in the domestic research. There are great innovations in the selection of the model and the selection of the model in three aspects. The deficiency of this paper is whether the selection of variables and the form of variables is completely representative, although some variables are added, and whether the results obtained in this paper are applicable to other provinces when choosing the model from multiple angles. Is there any other model that is more suitable for the prediction of provincial population? These problems can be solved in future research.

【學位授予單位】：東北財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：C81

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