本文選題：廣義動態(tài)因子模型　切入點：漸近靜態(tài)因子模型　出處：《浙江工商大學》2017年碩士論文　論文類型：學位論文

【摘要】：本文分別對大維條件下的廣義動態(tài)因子模型和漸近靜態(tài)因子模型構造了一種信息準則以估計他們的因子個數(shù)。本文主要受Onatski(2009)的啟發(fā),其核心在于以下兩點:第一,關于廣義動態(tài)因子模型的方法,在一定的條件下,利用傅立葉變換將序列的動態(tài)結(jié)構轉(zhuǎn)化為類似Chamberlain和Rothschild(1983)提出的靜態(tài)結(jié)構,然后用類似Bai和Ng(2002)的方法建立信息準則,相比Hallin和Liska(2007)的方法,我們的條件更加寬松。第二,關于漸近靜態(tài)因子模型的方法,我們對原變量做一個特殊的變換:為變換后的向量,j=1,…,T/2),然后在一定條件下建立信息準則。我們的方法的一致性在文中的假設下均得到了有效的保證,而且蒙特卡洛模擬顯示,我們的方法比Bai和Ng(2002)和Haliin(2007)更能適應強相關和強噪聲的情況。本文的主要內(nèi)容如下:第一章主要論述因子模型的研究背景、意義和研究現(xiàn)狀,并提出廣義動態(tài)因子模型和漸近靜態(tài)因子模型;這兩個模型的因子個數(shù)估計方法及假設和證明分別在第二章和第三章;第四章利用模特卡羅模擬拿我們的方法與其他方法進行比較;第五章是我們的結(jié)論。
[Abstract]:In this paper, we construct an information criterion for generalized dynamic factor model and asymptotically static factor model under the condition of large dimension to estimate the number of their factors. This paper is mainly inspired by Onatskii 2009), the core of which lies in the following two points: first, On the method of generalized dynamic factor model, under certain conditions, the dynamic structure of a sequence is transformed into a static structure similar to Chamberlain and Rothschild1983by Fourier transform, and then the information criterion is established by using a method similar to Bai and Ngn 2002). Our conditions are more relaxed than those of Hallin and Liska 2007. Second, the method of asymptotic static factor model, We make a special transformation of the original variable: we set up the information criterion under certain conditions for the transformed vector / t / 2. The consistency of our method is effectively guaranteed under the assumptions in this paper, and the Monte Carlo simulation shows that, Our method is more suitable for strong correlation and strong noise than Bai and Nggan 2002) and Haliin 2007. The main contents of this paper are as follows: the first chapter mainly discusses the background, significance and research status of the factor model. Then the generalized dynamic factor model and the asymptotic static factor model are proposed, and the methods of estimating the number of factors and their hypotheses and proofs are given in chapter 2 and chapter 3, respectively. Chapter 4th uses model simulations to compare our methods with other methods; Chapter 5th is our conclusion.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F224

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