【摘要】：對(duì)代理人學(xué)習(xí)行為的刻畫以及通過學(xué)習(xí)行為分析通脹預(yù)期形成過程成為近期宏觀金融學(xué)特別是貨幣金融學(xué)的前沿研究領(lǐng)域,同時(shí)計(jì)算機(jī)技術(shù)與人工智能的發(fā)展也為學(xué)術(shù)研究提供了更新更高效的研究工具——機(jī)器學(xué)習(xí)技術(shù)。機(jī)器學(xué)習(xí)技術(shù)專門研究計(jì)算機(jī)怎樣模擬或?qū)崿F(xiàn)人類的學(xué)習(xí)行為,這使得機(jī)器學(xué)習(xí)技術(shù)自然而然地成為研究宏觀經(jīng)濟(jì)學(xué)中的學(xué)習(xí)型預(yù)期或者說公眾學(xué)習(xí)行為的首選工具。在機(jī)器學(xué)習(xí)技術(shù)中,支持向量機(jī)(SVM)及支持向量回歸(SVR)以其較為完備的理論基礎(chǔ)以及在解決小樣本、非線性及高維模式識(shí)別中表現(xiàn)出許多特有的優(yōu)勢,成為目前最為廣泛使用的機(jī)器學(xué)習(xí)算法之一。本文所提出的SVR通脹預(yù)期借鑒了適應(yīng)性學(xué)習(xí)預(yù)期通過每期不斷納入新信息來刻畫的學(xué)習(xí)機(jī)制,同時(shí)對(duì)信息根據(jù)其獲取是否存在滯后性進(jìn)行劃分,再采用的支持向量回歸(SVR)算法生成通脹預(yù)期,并采用GMM方法對(duì)五種不同預(yù)期形式的菲利普斯曲線進(jìn)行實(shí)證分析。實(shí)證分析結(jié)果表明:(1)我國菲利普斯曲線中產(chǎn)出缺口和通貨膨脹的權(quán)衡機(jī)制失效,不同通脹預(yù)期項(xiàng)的系數(shù)均十分顯著,因此中央銀行在制定貨幣政策時(shí),應(yīng)當(dāng)注重貨幣政策的獨(dú)立性,同時(shí)應(yīng)依據(jù)通脹預(yù)期來進(jìn)行相應(yīng)的預(yù)期管理。產(chǎn)出缺口項(xiàng)系數(shù)均為負(fù)值或不顯著,這表明我國以菲利普斯曲線為基礎(chǔ)的貨幣政策傳導(dǎo)機(jī)制失效,即中央銀行無法通過改變產(chǎn)出缺口來調(diào)控通脹率。同時(shí)不同通脹預(yù)期項(xiàng)的系數(shù)均十分顯著,這表明我國菲利普斯曲線具有典型的預(yù)期增廣的特征或混合預(yù)期增廣的特征。(2)SVR預(yù)期相對(duì)于適應(yīng)性學(xué)習(xí)預(yù)期是一種更"高級(jí)"的預(yù)期學(xué)習(xí)方式。SVR通脹預(yù)期比理性預(yù)期表現(xiàn)出滯后特征,而比適應(yīng)性預(yù)期表現(xiàn)出先行特征。適應(yīng)性學(xué)習(xí)預(yù)期以適應(yīng)性預(yù)期為基礎(chǔ),因而學(xué)習(xí)速度較慢。SVR通脹預(yù)期的均值、中位數(shù)、標(biāo)準(zhǔn)差和偏度都最小,因而SVR通脹預(yù)期相對(duì)于理性預(yù)期、適應(yīng)性預(yù)期以及以適應(yīng)性預(yù)期為基礎(chǔ)的適應(yīng)性學(xué)習(xí)預(yù)期更為合理,也適宜作為央行制定通脹目標(biāo)區(qū)間的合理選擇。(3)我國菲利普斯曲線同時(shí)具有SVR通脹預(yù)期與理性預(yù)期的混合學(xué)習(xí)特征,且SVR通脹預(yù)期特征顯著強(qiáng)于理性預(yù)期特征�；旌蠈W(xué)習(xí)特征表明我國通脹預(yù)期不完全向前看,而是有限理性的,因而貨幣政策調(diào)整時(shí)不能完全前瞻,而應(yīng)隨學(xué)習(xí)預(yù)期的不斷遞歸進(jìn)行微調(diào)。SVR通脹預(yù)期顯著表明,我國菲利普斯曲線不僅具有學(xué)習(xí)特征,而且公眾對(duì)通脹預(yù)期有區(qū)別于適應(yīng)性學(xué)習(xí)的更"高級(jí)"的學(xué)習(xí)方式,因而信息獲取能力較強(qiáng),信息維度較高。因此,針對(duì)SVR通脹預(yù)期,中央銀行應(yīng)從兩個(gè)方面展開預(yù)期管理:一方面,中央銀行需要引導(dǎo)公眾對(duì)通脹的學(xué)習(xí)行為,應(yīng)提高貨幣政策的透明度和信息披露水平,建立貨幣政策信息平臺(tái)加強(qiáng)與經(jīng)濟(jì)個(gè)體的信息溝通,從而使公眾盡可能多地掌握學(xué)習(xí)過程中所需要的信息,加快通脹預(yù)期的學(xué)習(xí)速度;另一方面,中央銀行除提高信息溝通效率之外,還應(yīng)加強(qiáng)與財(cái)政政策、匯率政策、產(chǎn)業(yè)政策等其他宏觀經(jīng)濟(jì)政策的政策協(xié)調(diào),從而使其他經(jīng)濟(jì)變量能夠充分及時(shí)地反映通脹預(yù)期形成的信息。本文的創(chuàng)新之處在于:第一,提出SVR通脹預(yù)期以刻畫代理人面對(duì)存在滯后效應(yīng)的高維信息樣本時(shí)的通脹預(yù)期形成機(jī)制,并使用支持向量回歸(SVR)算法估計(jì)出SVR通脹預(yù)期。在估計(jì)過程中,本文創(chuàng)新性地將變量分為存在滯后效應(yīng)的變量和不存在滯后效應(yīng)的變量。進(jìn)一步本文將SVR通脹預(yù)期與理性通脹預(yù)期和適應(yīng)性通脹預(yù)期進(jìn)行比較,通過分析三種通脹預(yù)期的時(shí)序圖和統(tǒng)計(jì)特征,認(rèn)為SVR通脹預(yù)期更能合理刻畫公眾對(duì)通脹預(yù)期的學(xué)習(xí)行為,從而更適宜作為貨幣政策通脹目標(biāo)的選擇,這一點(diǎn)為本文實(shí)證分析我國不同預(yù)期的菲利普斯曲線時(shí),將菲利普斯曲線形式區(qū)分為適應(yīng)性預(yù)期的菲利普斯曲線、理性預(yù)期的菲利普斯曲線、SVR預(yù)期的菲利普斯曲線、適應(yīng)性預(yù)期與理性預(yù)期混合的菲利普斯曲線、SVR預(yù)期與理性預(yù)期混合的菲利普斯曲線提供了基礎(chǔ)。第二,本文基于GMM方法,對(duì)我國五種不同預(yù)期形式的菲利普斯曲線進(jìn)行實(shí)證分析,并依據(jù)實(shí)證結(jié)果對(duì)不同預(yù)期形式的菲利普斯曲線進(jìn)行比較,認(rèn)為SVR通脹預(yù)期與理性通脹預(yù)期混合的菲利普斯曲線更能刻畫我國貨幣政策的傳導(dǎo)機(jī)制,同時(shí)由于SVR通脹預(yù)期更能刻畫公眾的學(xué)習(xí)行為,因而通過實(shí)證分析混合菲利普斯曲線中SVR預(yù)期項(xiàng)系數(shù)與理性預(yù)期項(xiàng)系數(shù),本文進(jìn)一步分析了菲利普斯曲線的混合預(yù)期中,公眾對(duì)通脹預(yù)期的學(xué)習(xí)行為相對(duì)于理性預(yù)期行為的重要性。
[Abstract]:The characterization of the agent's learning behavior and the analysis of the expected formation of inflation through learning behavior have become the frontier research field of recent macro-finance, especially the monetary finance, At the same time, the development of computer technology and artificial intelligence also provides a more efficient research tool _ machine learning technology for academic research. The machine learning technology is a special study on how the computer can simulate or realize the human learning behavior, which makes the machine learning technology become the preferred tool for studying the learning-type expectation or the public learning behavior in the macroeconomics. In the machine learning technology, the support vector machine (SVM) and the support vector regression (SVR) have many unique advantages in solving small samples, non-linearity and high-dimensional pattern recognition, and become one of the most widely used machine learning algorithms. The proposed SVR inflation is expected to draw on the learning mechanism that the adaptive learning is expected to be characterized by the continuous inclusion of new information, and the information is divided according to the existence of the hysteresis, and the support vector regression (SVR) algorithm is used to generate the inflation expectation. The method of GMM is used to analyze the Phillips curve of five different expected forms. The results of the empirical analysis show that (1) The trade-off mechanism of the output gap and inflation in the Phillips curve of our country has failed, and the coefficients of different inflation expectations are all significant, so the central bank should pay attention to the independence of the monetary policy in the formulation of monetary policy. At the same time, the corresponding expected management should be carried out according to the inflation expectations. The coefficient of output gap term is negative or insignificant, which indicates that the monetary policy transmission mechanism based on the Phillips curve has failed, that is, the central bank cannot control the inflation by changing the output gap. At the same time, the coefficients of the different inflation expectations are significant, which suggests that the Phillips curve in our country has a typical expected augmented feature or a mix of expected augmented features. (2) SVR is expected to be a more "high-level"-expected learning approach with respect to adaptive learning. SVR inflation is expected to show a hysteresis characteristic than the rational expectation, and it shows the leading feature more than that of the adaptive expectation. Adaptive learning is expected to be based on an adaptive expectation, so the learning speed is slow. The expected mean, median, standard deviation, and bias of SVR inflation are the smallest, so that SVR inflation is expected to be more reasonable with respect to rational expectations, adaptive expectations, and adaptive learning based on adaptive expectations, as well as a reasonable choice of the central bank to develop an inflation target interval. (3) The Phillips curve of our country also has the mixed learning characteristics of the expected and rational expectation of the SVR inflation, and the expected characteristics of the SVR inflation are significantly stronger than the rational expected characteristics. The mixed learning characteristics show that the inflation expectations of our country are not completely forward, but are limited and rational, so the adjustment of the monetary policy cannot be fully forward, and the fine adjustment should be made with the continuous recursion of learning expectation. SVR inflation is expected to show that the Phillips curve of our country has not only the learning characteristics, but also the public's higher "high-level" of learning that the inflation is expected to be different from the adaptive learning, so the information acquisition ability is strong and the information dimension is high. Thus, for SVR inflation expectations, the central bank should expand its intended management in two ways: on the one hand, the central bank needs to guide the public's learning of inflation and should increase the transparency of monetary policy and the level of information disclosure, To set up a monetary policy information platform to strengthen the information communication with the economic individual, so that the public can master the information needed in the learning process as much as possible, and speed up the expected learning speed of the inflation; on the other hand, the central bank should strengthen the financial policy in addition to improving the information communication efficiency, The policy coordination of other macroeconomic policies, such as the exchange rate policy, the industrial policy, and the like, allows other economic variables to reflect the information expected to be formed in a full and timely manner. The innovation of this paper is that the first, it is expected that SVR inflation is expected to depict the expected formation mechanism of the inflation expectations when the agent faces the high-dimensional information samples with a lag effect, and the SVR inflation expectations are estimated using the support vector regression (SVR) algorithm. In the estimation process, the variable is divided into a variable with a hysteresis effect and a variable which does not have a hysteresis effect. In this paper, SVR inflation is expected to be compared with the expected and adaptive inflation expectations of the rational inflation. By analyzing the timing chart and the statistical feature of the three inflation expectations, it is considered that the SVR inflation is expected to more reasonably characterize the public's expected learning behavior of inflation. Therefore, it is more suitable for the choice of the target of monetary policy inflation. The Phillips curve, which is expected by the SVR, is based on the Phillips curve, which is expected to be mixed with the rational expectation, and SVR is expected to be based on the Phillips curve that is expected to be mixed with the rational expectation. Secondly, based on the GMM method, this paper makes an empirical analysis of the Phillips curves of five different expected forms in China, and compares the Phillips curves in different expected forms according to the empirical results. The Phillips curve, which is thought to be mixed with the expected combination of the inflation of the SVR and the rational inflation, can describe the conduction mechanism of the monetary policy in our country, and at the same time, as the SVR inflation is expected to be more capable of portraying the public's learning behavior, Therefore, through the empirical analysis of the expected coefficient of SVR and the factor of rational expectation in the mixed Phillips curve, this paper further analyses the importance of the public's expected behavior in the expectation of inflation relative to the expected behavior of the rational expectation in the mixed expectation of the Phillips curve.
【學(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F822.5

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