【摘要】：作為計量經(jīng)濟學的一個新的分支學科,空間計量經(jīng)濟學在近些年來發(fā)展迅速,越來越多的學者對其理論和應用進行了深入的探討�？臻g計量經(jīng)濟學的基礎是空間自回歸模型,空間自回歸模型現(xiàn)已成為應用最為廣泛的建模方法。但是,空間自回歸模型屬于參數(shù)模型,在實際數(shù)據(jù)產(chǎn)生機制下,參數(shù)模型可能不能很好地解釋實際數(shù)據(jù)。于是為了更好的探索變量間的復雜關系,非參數(shù)與半?yún)?shù)模型在計量經(jīng)濟學和統(tǒng)計學領域都得到了重視,但是基于非參數(shù)與半?yún)?shù)模型分析空間數(shù)據(jù)的研究結果卻相對較少。為了能更好的解釋數(shù)據(jù)和避免“維數(shù)災難”,本文首先提出空間單指標自回歸模型,空間單指標自回歸模型是參數(shù)空間自回歸模型和半?yún)?shù)單指標回歸模型的推廣模型,正因為它不僅具有獨特的降維特性又能很好的擬合空間數(shù)據(jù),對其進行研究將是一件十分有意義的事情。其次,由于局部線性是一種比較好的近似未知函數(shù)的方法,M-估計又是一種比較穩(wěn)健的估計方法,因此本文基于局部線性光滑和M-估計法相結合的兩階段方法及極大似然估計方法對空間單指標自回歸模型進行估計,進而基于Bootstrap對該模型的參數(shù)與非參數(shù)部分進行檢驗。最后通過數(shù)值模擬檢驗所提方法的有效性。
[Abstract]:As a new branch of econometrics, spatial econometrics has developed rapidly in recent years. More and more scholars have deeply discussed its theory and application. Spatial autoregressive model is the basis of spatial econometrics. Spatial autoregressive model has become the most widely used modeling method. However, the spatial autoregressive model belongs to the parameter model. Under the actual data generation mechanism, the parametric model may not be able to explain the actual data well. Therefore, in order to better explore the complex relationship between variables, non-parametric and semi-parametric models have been attached importance in econometrics and statistics, but the results of spatial data analysis based on non-parametric and semi-parametric models are relatively few. In order to better interpret the data and avoid the "dimension disaster", this paper first puts forward the spatial single index autoregressive model, which is a generalized model of parametric space autoregressive model and semi-parametric single index regression model. Because it not only has the special dimension reduction characteristic but also can fit the spatial data well, it will be very meaningful to study it. Secondly, because local linearity is a better approach to approximate unknown functions, M- estimation is also a more robust estimation method. Therefore, this paper estimates the spatial single-parameter autoregressive model based on the two-stage method of local linear smoothing and M- estimation, and the maximum likelihood estimation method, and then tests the parametric and non-parametric parts of the model based on Bootstrap. Finally, the effectiveness of the proposed method is verified by numerical simulation.
【學位授予單位】：新疆大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：O212.1
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本文編號：2231528