本文關(guān)鍵詞：中國(guó)物價(jià)指數(shù)單位根檢驗(yàn)中的結(jié)構(gòu)斷點(diǎn)問(wèn)題　出處：《河南大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 單位根檢驗(yàn) 結(jié)構(gòu)斷點(diǎn) ZA模型 狀態(tài)空間模型

【摘要】：多數(shù)宏觀經(jīng)濟(jì)變量序列都是具有趨勢(shì)性，在長(zhǎng)期的動(dòng)態(tài)變化中，表現(xiàn)出非平穩(wěn)的特性。判斷和確定宏觀經(jīng)濟(jì)變量的動(dòng)態(tài)特征對(duì)國(guó)家做出決策和制定措施有著重要的理論和實(shí)踐意義。通常來(lái)說(shuō)，對(duì)于時(shí)間序列的研究都是最先從單位根檢驗(yàn)入手的，若時(shí)序數(shù)據(jù)存在單位根過(guò)程，則序列的隨機(jī)趨勢(shì)是由新息的加總累積形成的，這樣在我們對(duì)序列進(jìn)行預(yù)測(cè)時(shí)，未來(lái)值都會(huì)受到過(guò)去所累積新息的影響，對(duì)序列的波動(dòng)有持續(xù)性影響；若時(shí)序數(shù)據(jù)是平穩(wěn)或者趨勢(shì)平穩(wěn)的，那么新息只有暫時(shí)性效應(yīng)，隨著時(shí)間就會(huì)不斷衰弱趨于零。但時(shí)序數(shù)據(jù)也可能是含有結(jié)構(gòu)斷點(diǎn)的趨勢(shì)平穩(wěn)的情形，所以在忽視這種情況下，而序列確實(shí)是含有結(jié)構(gòu)斷點(diǎn)的趨勢(shì)平穩(wěn)過(guò)程，這樣就造成誤判為單位根，對(duì)潛在DGP的判定出現(xiàn)錯(cuò)誤，更會(huì)導(dǎo)致后序的數(shù)據(jù)處理出現(xiàn)偏誤。 基于對(duì)宏觀經(jīng)濟(jì)變量時(shí)序平穩(wěn)性研究的理論意義，本文通過(guò)考察居民消費(fèi)價(jià)格定基指數(shù)月度數(shù)據(jù)，對(duì)通貨膨脹的長(zhǎng)期動(dòng)態(tài)結(jié)構(gòu)變化進(jìn)行了較為深入的探究。先觀察所研究的數(shù)據(jù)對(duì)象發(fā)現(xiàn)其長(zhǎng)期波動(dòng)過(guò)程中，出現(xiàn)了劇烈的大幅度的增長(zhǎng)，而大多數(shù)文獻(xiàn)及研究常常認(rèn)為通貨膨脹序列是單位根過(guò)程，目前關(guān)于深入挖掘結(jié)構(gòu)斷點(diǎn)的資料還很少。文中對(duì)消費(fèi)價(jià)格定基指數(shù)數(shù)據(jù)取對(duì)數(shù)后，進(jìn)行季節(jié)調(diào)整，然后通過(guò)常規(guī)的ADF檢驗(yàn)、PP檢驗(yàn)、KPSS檢驗(yàn)對(duì)經(jīng)過(guò)季調(diào)后的序列進(jìn)行平穩(wěn)性的最初判斷，多數(shù)結(jié)論顯示其為單位根過(guò)程，而KPSS檢驗(yàn)中對(duì)于帶有漂移的一階差分檢驗(yàn)，卻出現(xiàn)了相反的結(jié)果。 本文中主要基于Zivot和Andrews（1992）模型和狀態(tài)空間模型對(duì)該通脹序列進(jìn)行了參數(shù)和非參數(shù)的檢驗(yàn)。其中，ZA模型理論是運(yùn)用內(nèi)生性結(jié)構(gòu)斷點(diǎn)檢驗(yàn)方法，即結(jié)構(gòu)斷點(diǎn)是未知的，這樣就避免了經(jīng)驗(yàn)判斷結(jié)構(gòu)斷點(diǎn)日期的主觀性，當(dāng)合理判斷數(shù)據(jù)生成過(guò)程的情形下，，提高了檢驗(yàn)水平和功效。狀態(tài)空間理論是一種非參數(shù)檢驗(yàn)?zāi)Ｐ停疚幕赟-PULS軟件，通過(guò)命令窗口實(shí)現(xiàn)對(duì)序列形態(tài)的直觀判斷。大多數(shù)時(shí)間序列都可以表示成結(jié)構(gòu)性要素，即趨勢(shì)、周期、季節(jié)和不規(guī)則擾動(dòng)因素。這些要素的集合就構(gòu)成了可觀測(cè)到的變量的在某時(shí)刻的狀態(tài)。在一般的模型里面，這些不能觀測(cè)到的變量，被稱之為狀態(tài)變量。而狀態(tài)空間模型卻能夠把這些不能觀測(cè)到的狀態(tài)變量與可觀測(cè)變量建立某種結(jié)構(gòu)關(guān)系，結(jié)構(gòu)時(shí)間序列轉(zhuǎn)化為狀態(tài)空間模型以后，各狀態(tài)量就通過(guò)狀態(tài)空間模型被提取出來(lái)。本文用兩種不同類型的檢驗(yàn)方法進(jìn)一步驗(yàn)證了通貨膨脹時(shí)序數(shù)據(jù)確實(shí)有結(jié)構(gòu)斷點(diǎn)的存在。 ZA模型得出的檢驗(yàn)結(jié)果顯示截距上的斷點(diǎn)日期為1992年6月，截距和斜率上都發(fā)生結(jié)構(gòu)突變的斷點(diǎn)日期為1993年5月。而狀態(tài)空間模型則顯示出該序列存在多個(gè)不同斷點(diǎn)，在1994年前后和2007年。造成結(jié)構(gòu)斷點(diǎn)發(fā)生的成因，往往是受到一些事件的沖擊（譬如金融危機(jī)、政策改革、災(zāi)害等等），所以在研究長(zhǎng)期時(shí)序數(shù)據(jù)樣本時(shí)，由于外部性沖擊事件的存在，非常有必要考慮時(shí)序數(shù)據(jù)中是否含有結(jié)構(gòu)斷點(diǎn)，以及結(jié)構(gòu)斷點(diǎn)的形式和個(gè)數(shù)等問(wèn)題。 最后，根據(jù)本文得到的檢驗(yàn)結(jié)果，并結(jié)合我國(guó)改革以來(lái)的政策制度的革新變化，發(fā)現(xiàn)在我國(guó)發(fā)生較為嚴(yán)重的通貨膨脹時(shí)期（上個(gè)世紀(jì)80年代末和90年代初中期）是我國(guó)經(jīng)濟(jì)轉(zhuǎn)軌最關(guān)鍵時(shí)期，也是改革力度相對(duì)較大、次數(shù)相對(duì)較頻繁的時(shí)期。 總之，當(dāng)進(jìn)行時(shí)間序列的單位根檢驗(yàn)時(shí)考慮結(jié)構(gòu)斷點(diǎn)的存在問(wèn)題，對(duì)于正確判定序列的數(shù)據(jù)生成過(guò)程（DGP）至關(guān)重要，正確建立數(shù)據(jù)生成過(guò)程的模型是檢驗(yàn)平穩(wěn)和向平穩(wěn)序列轉(zhuǎn)化的前提，而序列是否正確又是進(jìn)行其他處理的關(guān)鍵。考慮結(jié)構(gòu)斷點(diǎn)的單位根檢驗(yàn)除了具有重大的理論意義之外，對(duì)于國(guó)家進(jìn)行經(jīng)濟(jì)預(yù)期、制定政策等有著實(shí)際的指導(dǎo)意義。
[Abstract]:Most macroeconomic variables are trend, dynamic changes in the long term, exhibit non-stationary characteristics. Judge and determine the dynamic characteristics of macroeconomic variables on the national decision-making and has important theoretical and practical significance to develop measures. Generally speaking, for the study of time series is the first from the unit root test to start, if the timing data are unit root process, stochastic trend sequence is composed of new information and the total accumulation, so that when we predict the sequence, the future value will be cumulative past innovation influence, have lasting effects on sequence fluctuation; if the timing data is stationary or trend stationary, so new information only temporary effects, as time will continue to decline to zero. But the sequence data may also be the breakpoint containing structural trend is stable, so ignore this In fact, the sequence is indeed a trend stationary process containing structural breakpoints, which results in misjudgement as unit root, and the decision of potential DGP is wrong, which will lead to errors in subsequent order data processing.
The theoretical significance of the research on the stability of time series based on macroeconomic variables, this paper investigates the consumer price index based on monthly data, the long-term dynamic changes of inflation is elaborated. The first observation data of the object of study found that the long wave process, there have been dramatic substantial growth, and most of the literature and research often think inflation sequence is a unit root process, there is little about the deep mining structure information. The breakpoint in the consumer price index data after logarithm, seasonal adjustment, and then through the ADF test, conventional PP test, KPSS test, the initial judgment of the sequence after seasonally adjusted smoothly the conclusion shows that the majority of unit root process, and KPSS test for a drift of one order difference test, there was an opposite result.
This paper is mainly based on Zivot and Andrews (1992) model and the state space model of the test parameters and non parameters of the inflation series. Among them, ZA model is a structure using the method of breakpoint test, the structural break is unknown, thus avoiding the experience judgment break date subjectivity, when reasonable to judge the data generating process conditions, improve the inspection level and efficiency. The state space theory is a non parametric test model based on S-PULS software, implementation of intuitive judgment sequence pattern through the command window. Most of the time sequence can be expressed as structural elements, namely, trend, cycle, seasonal and irregular disturbance factors set. These elements constitute at a time the state of the observable variables. The model in general, these cannot be observed variables, called state variables. The state space model is able to put these state variables can not be observed with observable variables to establish a structure, structure of time series into a state space model, the state through the state space model is extracted. The two kinds of inspection methods to further validate the inflation time series data does have a break there.
ZA model test results show that the intercept breakpoint date is June 1992, the intercept and the slope has breakpoint date structure change for May 1993. While the state space model shows that the sequence has many different breakpoints, before and after 1994 and 2007. The causes of structural break occurs, is often influenced by the impact of events (such as financial crisis, policy reform, disaster and so on, so in the study) time series data samples, due to external impact events are very necessary to consider whether the time series data structure contains a breakpoint, and structural form and the number of breakpoints.
Finally, according to the test results, combined with changes in our country since the reform of the policy system, found that the more serious inflation happened in China (the last century at the end of 80s and early 90s) is the most critical period of economic transition in China, the reform is relatively large, the number of relatively frequent periods.
In conclusion, considering the existing problems of the structure when the breakpoint for time series unit root test, to determine correct sequence data generating process (DGP) is to establish the correct data generation process model is the premise of smooth and inspection into stationary sequence, and the sequence is correct and is the key to consider the unit root of other treatments. In addition to the structural break test is of great theoretical significance, is expected for the national economy, and have practical guiding significance to formulate policies.

【學(xué)位授予單位】：河南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F726;F224

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