【摘要】：中央銀行貨幣政策的有效實(shí)施,企業(yè)和家庭對(duì)通貨膨脹風(fēng)險(xiǎn)的理性規(guī)避,都需要對(duì)通貨膨脹進(jìn)行準(zhǔn)確預(yù)測(cè)。由于通貨膨脹的發(fā)生機(jī)制和影響因素非常復(fù)雜,通貨膨脹的預(yù)測(cè)應(yīng)該包含科學(xué)性和藝術(shù)性。先是采用科學(xué)性較強(qiáng)的ARIMA模型對(duì)我國(guó)通貨膨脹進(jìn)行預(yù)測(cè)。對(duì)2008年至2015年的月度和季度CPI數(shù)據(jù),建立ARIMA模型,進(jìn)行樣本內(nèi)預(yù)測(cè),并與真實(shí)數(shù)據(jù)作比較。采用均方預(yù)測(cè)誤差、平均絕對(duì)預(yù)測(cè)誤差、平均正確預(yù)測(cè)方向三個(gè)預(yù)測(cè)精度指標(biāo),檢驗(yàn)月度和季度通貨膨脹預(yù)測(cè)值的預(yù)測(cè)精度。隨后,采用藝術(shù)性較強(qiáng)的預(yù)期性指標(biāo)法預(yù)測(cè)我國(guó)通貨膨脹。選擇四個(gè)典型的通貨膨脹預(yù)期性指標(biāo),利用三個(gè)預(yù)測(cè)精度指標(biāo)比較分析。結(jié)果發(fā)現(xiàn),萬(wàn)得CPI預(yù)測(cè)指數(shù)和朗潤(rùn)預(yù)測(cè)的加權(quán)平均指數(shù)預(yù)測(cè)精度較高,能夠分別有效預(yù)測(cè)月度和季度通貨膨脹。再基于預(yù)期性指標(biāo)法和ARIMA模型,構(gòu)建科學(xué)性和藝術(shù)性都較強(qiáng)的組合預(yù)測(cè)模型,預(yù)測(cè)我國(guó)月度和季度通貨膨脹。利用三個(gè)預(yù)測(cè)精度指標(biāo),比較預(yù)期性指標(biāo)法、ARIMA模型和組合預(yù)測(cè)模型的預(yù)測(cè)精度。結(jié)果發(fā)現(xiàn),組合預(yù)測(cè)模型的預(yù)測(cè)精度最高,預(yù)期性指標(biāo)法的預(yù)測(cè)精度較高。政策建議是使用大數(shù)據(jù)技術(shù)進(jìn)行通貨膨脹數(shù)據(jù)挖掘,以提高通貨膨脹預(yù)測(cè)中所需的數(shù)據(jù)質(zhì)量;對(duì)于中央銀行,因?yàn)樨泿耪咝Ч饕谡邔?shí)施的半年后顯現(xiàn),需要構(gòu)建出高精度的半年度和年度通貨膨脹預(yù)期性指標(biāo),而且運(yùn)用組合預(yù)測(cè)模型進(jìn)行通貨膨脹預(yù)測(cè);對(duì)于中小企業(yè)和家庭,簡(jiǎn)單方便起見(jiàn),可以運(yùn)用預(yù)期性指標(biāo)法預(yù)測(cè)通貨膨脹。
[Abstract]:The effective implementation of central bank monetary policy and rational evasion of inflation risk by enterprises and households all need to predict inflation accurately. Because the mechanism and influencing factors of inflation are very complicated, the forecast of inflation should be scientific and artistic. First, Arima model is used to forecast inflation in China. Arima model was established for monthly and quarterly CPI data from 2008 to 2015. The mean square prediction error, the average absolute prediction error and the average correct forecast direction are used to test the forecast accuracy of the monthly and quarterly inflation forecast values. Then, the author predicts China's inflation by using the artistically strong expectation index method. This paper selects four typical inflation expectation indexes and makes a comparative analysis by using three prediction precision indicators. The results show that the weighted average index predicted by Wande CPI and Runrun is more accurate and can effectively forecast monthly and quarterly inflation respectively. Then, based on the expected index method and Arima model, a combination forecasting model with strong scientific and artistic character is constructed to forecast the monthly and quarterly inflation in China. The prediction accuracy of Arima model and combined prediction model is compared by using three prediction precision indexes. The results show that the combined prediction model has the highest prediction accuracy, and the predictive index method has higher prediction accuracy. Policy advice is to use big data technology to mine inflation data to improve the quality of data needed for inflation forecasting; for central banks, the effect of monetary policy is mainly apparent half a year after the implementation of the policy. There is a need to construct high-precision semi-annual and annual inflation expectations indicators and to use composite forecasting models to forecast inflation; for small and medium-sized enterprises and households, simplicity and convenience, We can forecast inflation by using the expected index method.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F822.5

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