本文關(guān)鍵詞：空間混頻預(yù)測(cè)模型及其應(yīng)用研究　出處：《重慶大學(xué)》2016年博士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 空間混頻預(yù)測(cè) 空間預(yù)測(cè)方法 MIDAS模型 支持向量機(jī)

【摘要】：空間混頻預(yù)測(cè):考慮到區(qū)域之間的作用關(guān)系緊密(空間相關(guān)性問(wèn)題)和高頻數(shù)據(jù)與低頻數(shù)據(jù)共存(混頻數(shù)據(jù)問(wèn)題)的預(yù)測(cè)方法,是大數(shù)據(jù)時(shí)代下經(jīng)濟(jì)預(yù)測(cè)領(lǐng)域不斷受到重視的新穎問(wèn)題。目前相關(guān)研究包括空間預(yù)測(cè)和混頻預(yù)測(cè)兩個(gè)相對(duì)獨(dú)立的方向,而針對(duì)空間混頻預(yù)測(cè)模型及其應(yīng)用研究較少。鑒于軟空間權(quán)重矩陣能更貼近現(xiàn)實(shí)描述空間關(guān)系、最經(jīng)典的MIDAS為處理混頻提供了新視角、支持向量機(jī)能有效解決多屬性小樣本下的多種非線性設(shè)定問(wèn)題,本研究嘗試集成上述3個(gè)方面的優(yōu)勢(shì),聚焦于空間混頻預(yù)測(cè)模型及其應(yīng)用研究,提出空間混頻預(yù)測(cè)模型,并將新模型應(yīng)用于具體空間混頻預(yù)測(cè)問(wèn)題之中,該過(guò)程實(shí)現(xiàn)了將空間計(jì)量經(jīng)濟(jì)學(xué)中空間預(yù)測(cè)、混頻數(shù)據(jù)預(yù)測(cè)、機(jī)器學(xué)習(xí)方法、軟集合理論等多領(lǐng)域知識(shí)交叉和融合。主要工作可從以下3個(gè)方面論述:第一,既定空間范圍內(nèi)經(jīng)濟(jì)變量大多帶有一定空間相關(guān)性,空間混頻預(yù)測(cè)模型中如何設(shè)定空間相關(guān)性結(jié)構(gòu)是需首要解決的問(wèn)題,是構(gòu)建一元和多元空間混頻預(yù)測(cè)模型的基礎(chǔ)。總結(jié)和分析常用空間權(quán)重矩陣方法后,發(fā)現(xiàn)盡管理論上不存在能夠描述個(gè)體(例如區(qū)域)空間關(guān)系或空間相關(guān)結(jié)構(gòu)的最優(yōu)空間權(quán)重矩陣,但可以發(fā)現(xiàn)常用的空間權(quán)重矩陣在表述空間距離和位置關(guān)系時(shí)和真實(shí)地理環(huán)境的相互關(guān)系存在不一致性。這種不一致性包含了結(jié)論的不確定性、模糊性甚至是相互矛盾的,直接影響空間混頻預(yù)測(cè)模型的建模。軟集合是一種處理不確定性的有效工具,具備屬性、參數(shù)和映射三要素。而單一空間權(quán)重矩陣是一個(gè)特殊的軟集合�；谲浖系亩x和運(yùn)算可以有效將各種常用的權(quán)重矩陣進(jìn)行融合,拓寬了傳統(tǒng)的空間權(quán)重矩陣依賴于“距離”的測(cè)度,同時(shí)考慮鄰近區(qū)域的影響、非鄰近區(qū)域的影響,邊界相互作用、中心之間輻射作用,構(gòu)建出符合實(shí)際的軟空間權(quán)重矩陣。給出了基于軟集合理論的軟空間權(quán)重矩陣構(gòu)建方法的主要步驟,并結(jié)合空間計(jì)量經(jīng)濟(jì)學(xué)相關(guān)理論給出權(quán)重矩陣的滿足條件和檢驗(yàn)方法。最后,為驗(yàn)證模型的有效性,將軟空間權(quán)重矩陣應(yīng)用于區(qū)域產(chǎn)業(yè)集聚的影響因素分析中,根據(jù)一定檢驗(yàn)標(biāo)準(zhǔn),實(shí)例表明新方法具備一定的可行性。第二,借助軟空間權(quán)重更貼近實(shí)際描述經(jīng)濟(jì)變量的空間相關(guān)性的優(yōu)勢(shì)、一般預(yù)測(cè)模型發(fā)展到空間預(yù)測(cè)處理空間相關(guān)性的經(jīng)驗(yàn)、以已有混頻預(yù)測(cè)中MIDAS處理混頻建模思路為切入點(diǎn),構(gòu)建出基于軟空間權(quán)重的一元空間混頻預(yù)測(cè)模型。該模型承上啟下,是空間混頻預(yù)測(cè)模型的基礎(chǔ)模型,對(duì)MIDAS預(yù)測(cè)模型和空間預(yù)測(cè)模型的初步融合有一定理論意義。首先,總結(jié)空間混頻預(yù)測(cè)模型的建模思路,以備借鑒空間混頻預(yù)測(cè)模型引入空間權(quán)重的經(jīng)驗(yàn),并介紹和分析MIDAS預(yù)測(cè)模型的基本原理和模型設(shè)定方式。其次,鑒于被解釋變量和一元解釋變量之間頻率不一致、同時(shí)解釋變量顯著帶有空間相關(guān)性,引入基于軟集合理論的軟空間權(quán)重矩陣來(lái)修正MIDAS預(yù)測(cè)模型中混頻數(shù)據(jù)的多項(xiàng)式賦予權(quán)重方法,即新模型解釋變量的系數(shù)由混頻數(shù)據(jù)分布滯后賦權(quán)、軟空間權(quán)重和系數(shù)共同決定,其綜合反映了帶有空間相關(guān)性的單一解釋變量下混頻預(yù)測(cè)模型的設(shè)定方式。構(gòu)建出考慮一個(gè)高頻解釋變量預(yù)測(cè)一個(gè)低頻被解釋變量的基于軟空間權(quán)重的一元空間混頻預(yù)測(cè)模型。而后深入分析新模型的主要特點(diǎn),提出了檢驗(yàn)?zāi)Ｐ陀行缘念A(yù)測(cè)誤差或精度指標(biāo)。最后將所構(gòu)建新的一元空間混頻預(yù)測(cè)模型應(yīng)用于中國(guó)區(qū)域GDP預(yù)測(cè)之中,通過(guò)中國(guó)30個(gè)省市自治區(qū)季度實(shí)際GDP增長(zhǎng)預(yù)測(cè)效果分析和中國(guó)30個(gè)省市自治區(qū)GDP區(qū)域特征權(quán)重分析,證實(shí)了模型可行性。第三,基于軟空間權(quán)重的多元空間混頻預(yù)測(cè)模型是預(yù)測(cè)領(lǐng)域現(xiàn)實(shí)存在的問(wèn)題,有一定的現(xiàn)實(shí)意義和理論價(jià)值,但國(guó)內(nèi)外集中解決該問(wèn)題的研究較少,因而構(gòu)建出基于軟空間權(quán)重的多元空間混頻預(yù)測(cè)模型正是本研究的重點(diǎn)。首先,考慮到基于軟空間權(quán)重的一元空間混頻預(yù)測(cè)模型是在最經(jīng)典的MIDAS預(yù)測(cè)模型框架下初步改進(jìn)、且應(yīng)用范圍有限,以其為參考,綜合分析了基于軟空間權(quán)重的多元空間混頻預(yù)測(cè)模型構(gòu)建過(guò)程中亟待模型解決的問(wèn)題:解釋變量維度增加(變量之間混頻)的估計(jì)參數(shù)增加問(wèn)題;解釋變量維度增加(變量之間混頻)的樣本量偏小問(wèn)題;空間混頻共存下模型設(shè)定非線性設(shè)定問(wèn)題。緊接著,針對(duì)性介紹了支持向量機(jī)基本原理及其主要優(yōu)勢(shì)—核函數(shù)拓展模型多元非線性設(shè)定和小樣本計(jì)算優(yōu)勢(shì),通過(guò)核函數(shù)替代混頻預(yù)測(cè)模型的賦權(quán)方式,軟空間權(quán)重矩陣表征不同頻率變量空間相關(guān)性,利用最小二乘支持向量回歸機(jī)構(gòu)建出基于軟空間權(quán)重的多元空間混頻預(yù)測(cè)模型。給出了模型參數(shù)求最優(yōu)的方式,并深入分析新模型的主要特點(diǎn),提出了檢驗(yàn)?zāi)Ｐ陀行缘念A(yù)測(cè)誤差或精度指標(biāo)。而后將本章構(gòu)建新預(yù)測(cè)模型應(yīng)用于中國(guó)區(qū)域生態(tài)效率的預(yù)測(cè)之中,通過(guò)中國(guó)30個(gè)省市自治區(qū)生態(tài)效率預(yù)測(cè)效果分析和區(qū)域特征分析,證實(shí)了模型可行性。
[Abstract]:Spatial prediction: taking into account the mixing area between the role of close relationship (spatial correlation problem) and high frequency data and low frequency data coexist (mixing data) prediction method, is a new problem in the field of economic forecasting under the era of big data has attracted more attentions. The related research including spatial prediction and prediction of mixing two relatively independent direction. The spatial mixing forecasting model and its application research. In view of the soft spatial weight matrix can be more close to the realistic description of spatial relation, the most classic MIDAS provides a new perspective for the treatment of mixing, support vector machine can effectively solve various nonlinear multi attribute small sample set, this study attempts to integrate these 3 aspects of the advantages. Focusing on the spatial mixing prediction model and its application, proposes the space mixing prediction model, and the new model is applied to the concrete mixing space prediction problem In the process the spatial prediction of spatial econometrics, mixing data prediction, machine learning method, soft set theory in areas such as crossover and fusion. The main work can be discussed from the following 3 aspects: first, in certain range of economic variables often has certain spatial correlation, spatial mixing prediction model to set the spatial correlation structure is required to solve the problem first, is to construct a univariate and multivariate spatial mixing prediction model. Summarize and analyze the commonly used spatial weight matrix method, found that although the theory does not exist in the individual description (e.g. area) to optimal spatial weight matrix of spatial relations and spatial structure, but can be found in space the weight matrices used in the expression of the relationship between space distance and location relationship and real geographic environment is not consistent. This inconsistency contains Conclusion the uncertainty, fuzziness and even contradictory, directly affect the model prediction of the spatial mixing. Soft set is an effective tool for dealing with uncertainty, with the attribute parameters and the mapping of three elements. The single spatial weight matrix is a special soft set. Soft set definition and operation can be effective the integration of a variety of commonly used weight matrix based on the measure to broaden the traditional spatial weight matrix depends on the "distance", considering the impact of adjacent area, non adjacent area, boundary interaction between the center of radiation, soft spatial weight matrix in line with the actual construction. The main method of building soft weight space matrix of soft set theory is proposed based on combining to meet the conditions and test methods of spatial econometrics theory gives the weights matrix. Finally, to verify the model The validity, the soft spatial weight matrix is applied to analysis the influencing factors of regional industrial agglomeration, according to certain standards, examples show that the new method has certain feasibility. Second, with the help of the soft space weight advantage closer to the spatial correlation of the actual description of economic variables, the general prediction model to forecast the spatial correlation of the spatial processing experience. The MIDAS has forecast modeling methods of mixing mixing as the starting point, to construct a prediction model of element space mixing soft space based on weight. The model is the basic model of space link, mixing model, has a certain theoretical significance for the preliminary integration of MIDAS prediction model and spatial prediction model. Firstly, summarizes the spatial prediction of mixing the idea of modeling model, for reference space mixing model is introduced and the weights of the space experience, the introduction and analysis of MIDAS based prediction model The principle and model setting method. Secondly, in view of explanatory variables and a variable element between different frequency, and explanatory variables with significant spatial correlation, multi type spatial weight matrix introducing soft soft set theory based on the modified MIDAS prediction model of mixing in data weighted method, the new model to explain the variable coefficient by mixing data distributed lag weight, weight and coefficient of soft space is determined, it reflects the spatial correlation with single variable prediction model under mixing setting mode. To construct a high frequency considering the explanatory variables predict a low frequency dependent variable model predicts a mixing element space soft space based on weight. Then deeply analysis of the main features of the new model, put forward the test the effectiveness of the model prediction error or accuracy index. Finally the construction of a new mixed element space The model is applied in Chinese region GDP forecast, through the analysis and prediction and analysis of 30 provinces and autonomous regions Chinese characteristics GDP weight growth in real GDP Chinese 30 provinces quarter, confirmed the feasibility of the model. Third, multiple space soft spatial weight based on mixed frequency prediction model is the prediction of the existing problems, there are the practical significance and theoretical value, but the domestic and foreign research focus to solve the problem of less so constructed based on multivariate spatial mixing prediction model of soft space weight is the focus of this study. First, considering a mixing element space prediction model of soft space weight based prediction model is improved in the preliminary framework the classic MIDAS, and the application range is limited, as the reference, a comprehensive analysis of the spatial mixing soft spatial weight prediction model in the process of constructing the model based on the solution urgently Question: increase the explanatory variable dimension (variable between mixing) adds to the problems of parameter estimation; increase the explanatory variables (variables between the dimensions of mixing) small sample problem; nonlinear model space mixing coexist set set. Then, according to the introduction of the basic principle of support vector machine and its main advantages: kernel function expansion the model of multivariate nonlinear and small sample set is calculated by kernel function instead of mixing advantage, prediction model of weighting methods soft spatial weight matrix representation of different frequency variable spatial correlation, using the least squares support vector regression to build prediction model of multiple space mixing soft space based on weight. The parameters of this model to find the optimal way, and in-depth analysis of the main the characteristics of the new model, put forward the test the effectiveness of the model prediction error or accuracy index. Then this chapter Gou Jianxin prediction model It is applied to the prediction of regional eco efficiency in China, and the feasibility of the model is confirmed through the analysis of the prediction efficiency and the regional characteristics of 30 provinces and autonomous regions in China.

【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：F224
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本文編號(hào)：1358455