【摘要】：作為計(jì)量經(jīng)濟(jì)學(xué)的一個(gè)新的分支學(xué)科,空間計(jì)量經(jīng)濟(jì)學(xué)在近些年來發(fā)展迅速,越來越多的學(xué)者對其理論和應(yīng)用進(jìn)行了深入的探討。空間計(jì)量經(jīng)濟(jì)學(xué)的基礎(chǔ)是空間自回歸模型,空間自回歸模型現(xiàn)已成為應(yīng)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(shí)際數(shù)據(jù)產(chǎn)生機(jī)制下,參數(shù)模型可能不能很好地解釋實(shí)際數(shù)據(jù)。于是為了更好的探索變量間的復(fù)雜關(guān)系,非參數(shù)與半?yún)?shù)模型在計(jì)量經(jīng)濟(jì)學(xué)和統(tǒng)計(jì)學(xué)領(lǐng)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(jié)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(zāi)難”,本文首先提出空間單指標(biāo)自回歸模型,空間單指標(biāo)自回歸模型是參數(shù)空間自回歸模型和半?yún)?shù)單指標(biāo)回歸模型的推廣模型,正因?yàn)樗粌H具有獨(dú)特的降維特性又能很好的擬合空間數(shù)據(jù),對其進(jìn)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(jì)又是一種比較穩(wěn)健的估計(jì)方法,因此本文基于局部線性光滑和M-估計(jì)法相結(jié)合的兩階段方法及極大似然估計(jì)方法對空間單指標(biāo)自回歸模型進(jìn)行估計(jì),進(jìn)而基于Bootstrap對該模型的參數(shù)與非參數(shù)部分進(jìn)行檢驗(yàn)。最后通過數(shù)值模擬檢驗(yàn)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(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：O212.1
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本文編號：2231528