基于支持向量回歸的通脹預期及對中國菲利普斯曲線的實證研究
發(fā)布時間：2018-11-25 16:31
【摘要】：對代理人學習行為的刻畫以及通過學習行為分析通脹預期形成過程成為近期宏觀金融學特別是貨幣金融學的前沿研究領域,同時計算機技術與人工智能的發(fā)展也為學術研究提供了更新更高效的研究工具——機器學習技術。機器學習技術專門研究計算機怎樣模擬或實現(xiàn)人類的學習行為,這使得機器學習技術自然而然地成為研究宏觀經(jīng)濟學中的學習型預期或者說公眾學習行為的首選工具。在機器學習技術中,支持向量機(SVM)及支持向量回歸(SVR)以其較為完備的理論基礎以及在解決小樣本、非線性及高維模式識別中表現(xiàn)出許多特有的優(yōu)勢,成為目前最為廣泛使用的機器學習算法之一。本文所提出的SVR通脹預期借鑒了適應性學習預期通過每期不斷納入新信息來刻畫的學習機制,同時對信息根據(jù)其獲取是否存在滯后性進行劃分,再采用的支持向量回歸(SVR)算法生成通脹預期,并采用GMM方法對五種不同預期形式的菲利普斯曲線進行實證分析。實證分析結果表明:(1)我國菲利普斯曲線中產(chǎn)出缺口和通貨膨脹的權衡機制失效,不同通脹預期項的系數(shù)均十分顯著,因此中央銀行在制定貨幣政策時,應當注重貨幣政策的獨立性,同時應依據(jù)通脹預期來進行相應的預期管理。產(chǎn)出缺口項系數(shù)均為負值或不顯著,這表明我國以菲利普斯曲線為基礎的貨幣政策傳導機制失效,即中央銀行無法通過改變產(chǎn)出缺口來調(diào)控通脹率。同時不同通脹預期項的系數(shù)均十分顯著,這表明我國菲利普斯曲線具有典型的預期增廣的特征或混合預期增廣的特征。(2)SVR預期相對于適應性學習預期是一種更"高級"的預期學習方式。SVR通脹預期比理性預期表現(xiàn)出滯后特征,而比適應性預期表現(xiàn)出先行特征。適應性學習預期以適應性預期為基礎,因而學習速度較慢。SVR通脹預期的均值、中位數(shù)、標準差和偏度都最小,因而SVR通脹預期相對于理性預期、適應性預期以及以適應性預期為基礎的適應性學習預期更為合理,也適宜作為央行制定通脹目標區(qū)間的合理選擇。(3)我國菲利普斯曲線同時具有SVR通脹預期與理性預期的混合學習特征,且SVR通脹預期特征顯著強于理性預期特征�；旌蠈W習特征表明我國通脹預期不完全向前看,而是有限理性的,因而貨幣政策調(diào)整時不能完全前瞻,而應隨學習預期的不斷遞歸進行微調(diào)。SVR通脹預期顯著表明,我國菲利普斯曲線不僅具有學習特征,而且公眾對通脹預期有區(qū)別于適應性學習的更"高級"的學習方式,因而信息獲取能力較強,信息維度較高。因此,針對SVR通脹預期,中央銀行應從兩個方面展開預期管理:一方面,中央銀行需要引導公眾對通脹的學習行為,應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加強與經(jīng)濟個體的信息溝通,從而使公眾盡可能多地掌握學習過程中所需要的信息,加快通脹預期的學習速度;另一方面,中央銀行除提高信息溝通效率之外,還應加強與財政政策、匯率政策、產(chǎn)業(yè)政策等其他宏觀經(jīng)濟政策的政策協(xié)調(diào),從而使其他經(jīng)濟變量能夠充分及時地反映通脹預期形成的信息。本文的創(chuàng)新之處在于:第一,提出SVR通脹預期以刻畫代理人面對存在滯后效應的高維信息樣本時的通脹預期形成機制,并使用支持向量回歸(SVR)算法估計出SVR通脹預期。在估計過程中,本文創(chuàng)新性地將變量分為存在滯后效應的變量和不存在滯后效應的變量。進一步本文將SVR通脹預期與理性通脹預期和適應性通脹預期進行比較,通過分析三種通脹預期的時序圖和統(tǒng)計特征,認為SVR通脹預期更能合理刻畫公眾對通脹預期的學習行為,從而更適宜作為貨幣政策通脹目標的選擇,這一點為本文實證分析我國不同預期的菲利普斯曲線時,將菲利普斯曲線形式區(qū)分為適應性預期的菲利普斯曲線、理性預期的菲利普斯曲線、SVR預期的菲利普斯曲線、適應性預期與理性預期混合的菲利普斯曲線、SVR預期與理性預期混合的菲利普斯曲線提供了基礎。第二,本文基于GMM方法,對我國五種不同預期形式的菲利普斯曲線進行實證分析,并依據(jù)實證結果對不同預期形式的菲利普斯曲線進行比較,認為SVR通脹預期與理性通脹預期混合的菲利普斯曲線更能刻畫我國貨幣政策的傳導機制,同時由于SVR通脹預期更能刻畫公眾的學習行為,因而通過實證分析混合菲利普斯曲線中SVR預期項系數(shù)與理性預期項系數(shù),本文進一步分析了菲利普斯曲線的混合預期中,公眾對通脹預期的學習行為相對于理性預期行為的重要性。
[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.
【學位授予單位】：東北財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F822.5
本文編號：2356751
[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.
【學位授予單位】：東北財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F822.5
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，本文編號：2356751
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