国际贸易冲击效应与中国宏观经济波动_1978_2005
国际贸易对经济周期波动的传导效应
国际贸易对经济周期波动的传导效应全球经济在过去几十年里发生了巨大的变化。
国际贸易的规模不断扩大，成为推动经济增长的重要动力。
然而，国际贸易也与经济周期波动密切相关，它可以传导并加剧这些波动。
首先，国际贸易的不稳定性是经济周期波动的重要传导渠道之一。
国际贸易随着需求和供应的变化而波动，当一个国家的经济低迷时，它的进口需求可能会减少，导致其他国家的出口减少。
这进一步加剧了其他国家的经济放缓，形成了一种连锁反应。
而当全球经济增长放缓时，各国之间的贸易量也会减少，进而加深了经济周期波动。
其次，国际贸易的竞争性也对经济周期波动产生了影响。
国际贸易的竞争性意味着企业必须不断提高产品质量和降低成本，以保持竞争优势。
然而，这种竞争往往会导致企业和产业的兴衰。
当一个国家的产业不再具有竞争力时，它的出口将减少，导致经济放缓。
这种竞争性的传导效应被认为是造成经济周期波动的一个重要原因。
此外，国际贸易也受到经济周期波动的影响。
经济周期波动会导致需求和供应的波动，进而影响国际贸易。
在经济下行周期中，消费者购买力下降，需求减少，导致国际贸易的减少。
而在经济上行周期中，消费者购买力增加，需求增加，国际贸易也会相应增加。
这种供需关系的传导效应使得国际贸易与经济周期波动之间形成了一种互动关系。
然而，国际贸易对经济周期波动的传导效应并不是单向的，它也受到其他因素的影响。
例如，货币政策的变化、贸易政策的调整等都会对国际贸易产生一定的影响和反馈作用。
这些因素的变化可能会加速或缓解国际贸易对经济周期波动的传导效应，使其更加复杂和多样化。
在当前全球化的背景下，国际贸易对经济周期波动的传导效应越发明显，并且往往具有放大效应。
一个国家的经济衰退可能会通过国际贸易渠道传导到其他国家，进而形成全球经济的下行周期。
同时，全球经济的放缓也会通过国际贸易传导到各个国家，进一步加剧经济周期波动。
综上所述，国际贸易对经济周期波动具有重要的传导效应。
其不稳定性、竞争性和供需关系等因素，以及受到其他因素的影响，使得国际贸易与经济周期波动之间形成了错综复杂的关系。
外汇市场波动对我国宏观经济运行的影响
外汇市场波动对我国宏观经济运行的影响近年来，外汇市场的波动越来越频繁，尤其是美元对其他货币的汇率波动。
外汇市场波动对我国宏观经济运行带来了一定的影响，本文将从以下几个方面进行探讨。
一、出口贸易外汇波动的一个基本影响就是对出口贸易的影响。
出口是我国国民经济的重要支柱，而外汇市场波动则会直接影响出口企业的收入和毛利率。
当本币升值时，出口企业的产品价格将变得更高，在国际市场上的竞争力将受到影响；当本币贬值时，出口企业的产品价格将变得更低，竞争力则会增强。
因此，外汇市场波动直接影响了我国出口企业的盈利和国民经济的出口额。
二、进口成本外汇市场波动还会影响我国的进口成本。
当本币升值时，进口商品的价格会因汇率变化而变得更便宜；当本币贬值时，进口商品价格则会变得更贵。
因此，外汇市场波动对我国的进口政策和我国人民的生活水平都会产生一定的影响。
三、国家储备外汇市场波动对我国的国家储备也产生影响。
由于外汇市场波动的影响，外汇市场进入国内的流量的波动会增加，国家的外汇储备也会随之波动。
当本币升值时，外汇储备会增加；当本币贬值时，外汇储备则会减少。
因此，外汇市场波动对我国的国家储备有一定的影响。
四、货币政策外汇市场波动还会产生对我国货币政策的影响。
由于外汇市场波动的影响，我国的货币运用政策也会调整。
例如，在本币升值时，应采取一些措施填补外汇市场波动对国家货币的负面影响；相反，在本币贬值时，则需要采取一些措施来减轻外汇市场波动对国家货币的负面影响。
综上所述，外汇市场波动对我国的宏观经济运行有着显著的影响。
出口贸易、进口成本、国家储备和货币政策等方面均会受其影响。
因此，需要我们密切关注外汇市场波动的情况，并及时采取相应的应对策略，以确保我国经济的稳定和持续发展。
国际贸易冲击、人民币汇率变动与中国宏观经济波动——基于GVAR模
推 出了多轮的量 化宽松 政策 ，向市场 注入 了逾万亿美 挑战具 有重要 的现实意义。 元 规模 的 流动性 ，造成 美元 贬 值 ，导 致 热钱 流 人 中
国 内外文献从 不 同角度分析了 国际贸易对一 国宏
ａ ｃ ｋ ｕ ｓ 等构 建 的 国 ，刺激 大宗商 品价格 上涨 ，带来输入 型通胀压力 的 观经济的影响 。理 论模 型分 析方 面 Ｂ
和 Ｒ ｏ ｓ ｅ 、Ｃ ｌ ａ ｒ ｋ和 Ｗｉ ｎ ｃ ｏ ｏ ｐ实证 结果 表 明国家之 间 的 用宏观 的进出 口总额数据 ，研究人 民币汇率变动对 贸
济 的影响 。广义脉冲响应分析结果表 明，美 国需 求冲击对 中 国 Ｇ Ｄ Ｐ影响较 大 ，波动 周期长 ；中国 Ｇ Ｄ Ｐ对欧 元 区需求 冲击反应迅速 ，波动较 小。而广义预测方差分解发现欧元 区需求冲击对 中国 Ｇ Ｄ Ｐ波动 的贡献 率高于 美国需求冲击。人 民币升值会导致 中国净 出口下降 ，从 而使 Ｇ Ｄ Ｐ下 降，但 Ｇ Ｄ Ｐ下降幅度 小于净 出口，同时 人 民 币升值有助 于缓 解国 内通货膨 胀。推进 产业结构 升级 、完善人 民 币汇率形 成机制是 有效应 对 贸易冲击 、
也就能为更 多 的投 资 融通 资金 ，经济 会 得 到更快 增 光建 、姜子 叶估计 了贸易的长期或短期弹性并检验了 马歇尔一勒纳条件 ” 的适用 性。 驯 二是检 验人 民 长； Ｍ ａ ｚ ｕ ｍ ｄ ａ ｒ 基于索洛模 型和 资本积 累理论 分析 ， “
全球经济危机对中国宏观经济的影响分析
全球经济危机对中国宏观经济的影响分析随着全球经济的不断发展，各国间经济交流的频繁，全球经济体系呈现出高度互动的趋势。
然而，一旦全球经济陷入危机，无论是发达国家还是新兴市场都难以幸免。
而面对全球性的经济危机，中国作为世界第二大经济体，其经济发展状况无疑备受关注。
那么，全球经济危机对中国宏观经济产生了哪些影响呢？我们从几个方面来进行分析。
一、出口受到冲击中国的经济增长主要依赖出口，在全球贸易体系中扮演着重要的角色。
然而，全球经济危机导致了全球需求的下降，国际贸易受到严重冲击。
这也直接影响了中国出口的表现。
根据中国海关统计数据，2008年下半年，中国出口额开始出现下滑，2009年第一季度进一步下降，导致了出口企业严重亏损甚至倒闭。
出口降低也意味着中国对外汇的需求减少，汇率一度波动，人民币贬值。
二、内需萎缩全球经济危机不仅影响了中国的出口，也对内需造成了冲击。
农村经济和城市经济相互依存，城市经济如果受到影响，农村经济也难以幸免。
在危机期间，国内投资环境受到影响，许多企业减少投资甚至停止投资。
不仅如此，由于就业岗位减少，收入水平降低，人们的消费水平也下降了。
购买力的降低导致了许多行业的销售额下降，企业规模也不断萎缩。
而内需的萎缩也意味着就业问题的加重，失业人口的增加是很多社会问题的导火索。
三、政策调整在经济危机的面前，中国政府采取了一系列的政策调整来缓解形势。
包括提高财政支出、调整汇率政策、减免企业税收、加强金融监管等措施，以保持经济增长、促进社会和谐、避免出现大规模的社会动荡。
此外，中国政府在危机期间还加速了国内市场的开放进程，深化了改革开放，提高了市场的竞争力，加速了市场化进程。
四、新兴市场冲击全球经济危机还引发了一场新兴市场的风暴。
在经济全球化的背景下，中国与其他新兴经济体一起，被视为全球经济发展的重心，而且它们相互支持、相互依存、相互促进。
然而，危机的爆发打乱了新兴市场的状态，降低了中国与其他新兴经济体的出口贸易额，也影响了同行之间的贸易竞争。
货币政策冲击对中国经济波动的影响
Price Stickiness and the Effect of Monetary Policy Shocks to Chinese Macroeconomic Fluctuations——Based on a New Keynesian DSGE ModelCourse Number & Title: EC424 Monetary EconomicsWord Count: Approximate 5400Candidate Number: 78219AbstractIn this paper, I estimate a New Keynesian model for the Chinese economy to see the monetary policy effect and focus on different price stickiness levels. Its primary aims are to find out whether monetary policy shock is the source of Chinese macroeconomic fluctuation and what is the most reasonable price stickiness level for China. The conclusion is that the price stickiness level is much lower than advanced economies and monetary policy shocks do have effects when price stickiness exists, but its contribution to output fluctuation is quite small, while it explains most of the fluctuations of inflation of China.Key Words: Price Stickiness Monetary Policy Shocks New Keynesian ModelChinese Macroeconomic Fluctuations1.IntroductionAre monetary policy shocks major sources of Chinese macroeconomic fluctuations? Previous researches have shown that monetary impulses do have an effect on business cycles and some economists believe that monetary policy shock is probably more relevant for the business cycle than technology shocks. As for China, however, previous works have given different results. Chen, Gong and Zou (2004) points out that it is the fundamental shocks rather than sunspot shocks that contribute to China’s macroeconomic fluctuation s and the effect of supply shocks is more relevant than demand shocks. Yang and Li (2011) adopt a New Keynesian Model and apply it to China monetary economic fluctuation analysis, and find out that monetary policy shock is not the source of China’s aggregate economic fluctuation and that fluctuations in inflation are mostly driven by monetary policy shocks.As mentioned above, many New Keynesian Economists believe that monetary policy shocks do have an effect on economic fluctuations. Previous works have presented it theoretically and empirically. The New Keynesian DSGE model is developed from the RBC framework, which has the assumption of "perfect competition" and "flexible price”. These assumptions lead to a problem, that is, a direct result of the "price flexibility" is money neutrality which means monetary policy has no impact on economic fluctuations. In fact, it does not meet the reality and criticized by many scholars (Romer and Romer (1988), Bernanke and Blinder (1992), Gali (1992), Bernanke, Gertler, and Watson (1997)) who hold the view that monetary policy rules have an important impact on the real economy. Then, although the theoretical framework for policy analysis originated from the RBC dynamic stochastic general equilibrium analysis method, unlike RBC, nominal variables and nominal rigidity are introduced into the model to fit the needs of the analysis of monetary policy,and also fit the reality as well. Therefore, the three key elements of the New Keynesian model are added to the RBC model, which are money, monopolistic competition and nominal rigidity. Early study of the New Keynesian model can be found in the work of Gordon (1982) and Taylor (1980), which all have a very important point: the nominal price rigidity is the key friction leading to non-neutral monetary policy. From the work of Goodfriend and King(1997), Rotemberg and Woodford (1995), Clarida, Gali, and Gentler (1999), and Woodford (2003), a small monetary economic model studying on business cycles with sticky prices and sticky wages is gradually popular in monetary policy analysis, which is called New Keynesian model or New neoclassical synthesis model. In sum, for analysis of monetary policy, price stickiness should be focused when adopting a New Keynesian DSGE model, because under which monetary policy shows non-neutrality.Then it follows the estimation of the model. Basic procedures of empirical DSGE are as follows: (1) Setting up the model from micro-foundation assumptions; (2) Finding optimal conditions; (3) Log-linearizing the system of equations around steady states; (4) Solving the system numerically; (5) Doing treatment to data especially isolating trends and cycles of data using HP filters or other filtering methods; (6) Estimating. When it comes to estimation, several methods can be used. First, pure calibration from data can be done and then simulate stochastically to match data. Second, use Kalman filter in state space which allows time varying parameters, stochastic volatility, and regime switching. The third and the most popular one is Bayesian estimation based on GMM or MLE due to identification issues. The Bayesian likelihood estimation of DSGE can be seen in the works of Smets and Wouter (2003), Christiano, Eichenbaum and Evans (2005), and Smets and Wouter (2007).In recent years, Chinese scholars applied DSGE model to Chinese economy to study its fluctuation, and achieved certain progress. Based on the framework of the RBC model with technology shocks, they found out that technology shock explains the major part of China's economic fluctuations (Chen, Gong and Zou (2004), Huang (2005)). However, in terms of monetary policy, the study on China is not sufficient, and there is a big difference among the conclusions.Since reform and opening up in 1978, China’s economy has experienced more than three decades of rapid growth. However, it also experienced significant fluctuations during this period. The GDP growth rate in some years is as high as 15% （the year 1984 and 1992）but in some years less than 4% (the year 1990). Understanding this fluctuation and what it comes from is important for policy implications. At the meantime, the central bank of China (the People’s Bank of China) has gradually formed the modern monetary policy mechanism. Since it is unlikely to have another strong supply shock1as what happened in 2008, the People’s Bank of China (Central Bank) had better play its role in reacting to the financial crisis and its subsequent influence. However, the monetary policy tools are not as powerful as what they are in advanced economies. When analysing the effect of monetary policy using New Keynesian Model, price stickiness is a key point to focus on. Many works on China assume a certain level of price stickiness (usually the same as the United States) since fewer work has done to calculate China’s price stickiness level. However, because of the difference of market environment between China and the United States, the price stickiness level of China is inevitably different from the United States. From this point of view, this paper examines the effect of monetary policy shocks to1The 2008–2009 Chinese economic stimulus plan is a RMB¥ 4 trillion (US$ 586 billion) stimulus package announced by the Central People's Government of the People's Republic of China on 9 November 2008 as an attempt to minimize the impact of the global financial crisis.Chinese macroeconomics fluctuations under different price stickiness rates.The objectives of the paper are threefold. First, it estimates a New Keynesian DSGE model using China data to see where the fluctuations of Chinese macroeconomic come from. Second, it focuses on the importance of price stickiness and examines the effect of monetary shocks under three price stickiness levels. Finally, it shows the effect of monetary policy shock to output, nominal interest rate, real interest rate and inflation, through impulse response and calculates the contribution of the shock to the volatility of these variables through variance decomposition.The rest of the paper is organised as follows. In the next section, I introduce some evidences of China’s price stickiness and emphasis on the work of Cai (2012) which estimates the level of China price stickiness; in section three, I first present a New Keynesian model and then show the description and basic derivation of the model to see where all the shocks come from; section four is the data description and estimation method; results are provided in section five. Conclusion and remarks are made in section six.2.Evidence of China’s Price StickinessThere is no doubt that the assumption of price stickiness is essential for macroeconomic analysis, especially for monetary economic analysis. Price stickiness in New Keynesian Model relies on monopolistic competition. It assumes that many sellers and buyers are in the market and sellers have some monopolistic power because products are slightly different from each other. There are different ways to introduce stick prices, and adjustment cost and Calvo devil are the ways usually adopted in literature.In the real world, we can see that there obviously is some stickiness in prices, but the level of stickiness in different countries may differ a lot. Will the level of price stickiness in China be thesame as the United States? The answer depends on what determines the price stickiness level. The formation of price stickiness is related to market environment, corporate pricing and other factors. Specifically, the key reasons are delivery delay, service and collaboration failures, cost-plus pricing, implicit contracts, contracts with clear fixed price and adjustment cost (Blinder et al, 1998). It is well known that the operation of the market environment in China is quite different from the advanced economies, thus the level of price stickiness should be different. On one hand, due to fluctuations in macroeconomic policies and external shocks, output and price volatility in developing countries is higher than that of developed countries (Agénor & Montiel, 2008). On the other hand, from China's specific national conditions, the reform of China's market economy in the last 30 years has deepened the development of the market environment, which has undergone profound changes. This will inevitably lead to a major shift in enterprise pricing. Therefore, the properties of China’s economy indicate that we cannot set our price stickiness rate by simply following the United States outcome.Cai (2012) did an empirical study on China’s price stickiness. He uses the methods of Snordone (2002) and Gali and Gertler (1999) to estimate the level of China’s price stickiness from the first quarter of 1992 to the first quarter of 2012. The results indicate that the average duration of China’s price level is 3.4-8.1 months, which shows that the level of price stickiness in China is far lower than that in developed countries (the United States), which has an average duration of 9-17 months. This result shows that the average duration of China’s price stickiness is so low that it is close to the flexible price assumption. He also points out that low wage stickiness, lack of long-term business relationships and the volatilities of the macro environment are probably be the key reasons of low price stickiness level in China. Thus, assuming a price stickiness level thesame as the United States might not be reasonable while the assumption of no price rigidity might not be fair too.Therefore, in the section of the estimation of the model, I provide three cases regarding to different price stickiness level. The first one concerned is θ=0.01, which shows no price rigidity; another one concerned is θ=0.66, which is close to the level of advanced economies; the last one concerned is θ=0.33, which is the one I expect to reflect the real level of China’s price stickiness.3.The modelIn this section, I first present a New Keynesian model and then show the description and basic derivation of the model to see where all the shocks come from.I adopt a New Keynesian DSGE model, which assumes that the economy can be summarised as follows:ỹt=E t ỹt+1−σ−1(i t−E tπt+1−r t n)(1)πt=κỹt+βE tπt+1(2)i t=ρ+ϕy ỹt+ϕππt+υt(3) where ỹt denotes the output gap; πt denotes inflation rate; i t denotes nominal interest rate and r t n denotes flexible price real interest rate. Here I follow the benchmark New Keynesian DSGE model designed by Jordi Galíin his popular textbook Monetary Policy, Inflation, and Business Cycle: An Introduction to the New Keynesian Framework (2008).Equation (1) is the IS curve, which is obtained from household’s optimal choice of consumption and bond holdings. It reflects household’s inter-temporal choice of rational consumption. Equation (2) is the New Keynesian Philips Curve, which is derived from firm’soptimal price setting. It presents the relation of output gap and inflation. Equation (3) is a Taylor Rule, through which central bank sets interest rate. The parameter ρ captures the degree of monetary policy inertia, while ϕy denotes the monetary authority’s response to output gap and ϕπ denotes the response to inflation. ϕπ is assumed to be large than 1 according to Taylor ’s Principle. υt is the monetary policy disturbance, which captures exogenous deviations from the systematic policy rule and contains monetary policy shock.The shocks to the economy other than monetary policy shock are technology shock and government spending shock, and these shocks are all assumed to follow an AR(1) process as shown below.v t =ρv v t−1+εv(4) g t =ρg g t−1+εg(5) a t =ρa a t−1+εa(6) The disturbances are assumed to be auto-correlated with coefficient ρi , where i={v, g, a} .The innovations εi are assumed to be i.i.d., zero mean and finite variance, which can be representedby εt i ~N(0,σi 2). From the above equations, we can clearly see where monetary policy shock (εv )come from. In order to see where the other disturbances come from, I give a basic description and deviation of this model from the start of the representative household.3.1. The HouseholdThe representative household seeks to maximise her lifetime utility functionMax E t ∑βt [C t 1−σ1−σ−N t 1+φ1+φ]∞t=0 (7)where:C t =[∫C t (j)ε−1dj 10]ε1−ε (8)subject to the following budget constraint:∫C t (j )P t (j )dj +Q t B t ≤B t−1+W t N t −T t 10 (9)where β is the discount factor, σ is the inverse elasticity of inter-temporal substitution, which measures the responsiveness of the growth rate of consumption to the real interest rate; φ is inverse elasticity of labour supply to real wage; P t (j ) is the price of good j at period t; B t−1 denotes one-period riskless bond, purchased in period t-1 and maturing in period t; W t N t is the labour income; T t is the lump-sum transfer; C t is an aggregate weighted sum of a bunch of different goods with elasticity of substation ε. The form of consumption index is following a Dixit-Stiglitz formulation, which allows firms to have control over the price of their goods.The household here has two problems to solve: one is the utility maximisation problem (the dynamic problem) and the other is the optimal composition consumption problem (the static problem). The first order conditions to these problems are given byN t φC t −σ=W t P t(10) Q t =βE t [(C t+1C t )−σP t P t+1](11) C t (i )=(P t (i)P t )εC t(12) Equation (10) and (11) come from the dynamic problem and the former equation is the household’s optimal labour leisure decision, which determines the supply side of labour and the latter one is the inter-temporal Euler Equation, which determines the household’s optimal consumption-saving decision. Equation (12) comes from the static problem, which gives the demand function for goods C t (i ).3.2. The FirmAssume identical firms with linear technology without capital (or fixed capital)Y i (i )=A t N t (i)1−α(13)where A t denotes the technology level, which contains technology shock to this economy. For price stickiness, I use the Calvo pricing mechanism with a proportion of (1−θ) of all firms freely adjusting its price in each period, while the rest of firms will remain its previous price setting.Then, the firm’s problem is to set optimal price P t ∗ to maximize the present discount value of profit. In each period, the firm has a probability of (1−θ) to re-optimize its price. Thus, the firm’s optimal price setting problem is given byMax ∑θk E t {Q t,t+k [P t ∗Yt+k,t −Ψt+k (Y t+k,t )]}∞k=0 (14)where Q t,t+k is the stochastic discount factor for nominal payoffs; Ψ(·) is the cost function; Y t+k,t is output in period t+k for a firm that last reset it’s price in period t.2 The budget constraint is given byY t+k,t =(P t ∗P t+k)−εC t+k(15)which follows the household static problem of choosing optimal consumption for a specific good.Then, the first order condition to this problem is∑θk E t {Q t,t+k Yt+k,t [P t∗P t−1−εε−1MC t+k,t ∏πt+i k i=0]}∞k=0(16)And importantly, define price index as followP t =[θP t−11−ε+(1−θ)P t ∗1−ε]1(17)which shows that on the macro level, in each period a share of θ of the prices remains unchanged, while the other 1−θ prices are adjusted. The purpose of defining the price index is to obtain the relationship between inflation and output, i.e. the New Keynesian Philips Curve.2The notations here all follow Galí (2008).3.3. EquilibriumIn this model, we have infinite goods markets and a labour market and thus the equilibrium conditions are as follows. Goods market clearing condition requires thatY t (i)=C t (i)+G t (i)(18)and the aggregate level is also assumed to be hold, where G t is government spending at period t, which contains the government spending shock (εg ). Labour market clearing condition requires thatN t =∫N t (i)di 1(19)where the left-hand side N t comes from the household optimality condition for labour and leisure which determines the supply side of the labour and the right-hand side N t (i) comes from the firm’s production function which determines the demand side of the labour. 3.4. Log-linearizationNext, log-linearize the optimal conditions around their steady state values in order to solve the model. The solutions to the model usually have the form of non-linear different equations, which generally have no closed form solution. Therefore, we need numerical methods to find the approximate solutions. Log-linearization is one of the techniques. The outcomes of log-linearization are given below.Log-linearizing household’s problem givesσc t +φn t =w t −p t(20) c t =E t c t+1−σ−1(i t −E t πt+1)(21)Log-linearizing firm’s problem givesy t =a t +(1−α)n t(22)p t ∗=(1−βθ)∑(βθ)kE t (mc t+k,t +p t+k )∞k=0(23) πt =(1−θ)(p t ∗−p t−1)(24)Log-linearizing market equilibrium condition givesy t =c t +g t(25)When α=0, log-linearized marginal cost is given bymc t =(w t −p t )−a t(26)From the above description and derivation, we can see that the monetary policy disturbance comes from the central bank ’s interest rule, which usually assumes a Taylor rule and the monetary policy shock is a deviation of the rule which is unexpected by private sectors. The technology disturbance comes from firm ’s production function, which determines the resource the firm has. The technology shock is from the involvement of the technology disturbance and it affects the agent ’s resources and it helps to predict the future productivity. The government spending disturbance is another policy disturbance and contains the government spending shock. It affects the consumption level of households and the shock is unanticipated.4. Data, calibration and estimation4.1. DataThe model parameters Θ=[θ,β,α,σ,φ,ρ,φy ,φπ,ρv ,ρg ,ρa ,σεv ,σεa ,σεg ] are estimated using maximum likelihood-based Bayesian methods and China data on the output, inflation and interest rate as observable variables.All data are quarterly observations of macroeconomic variables from 1992 Q1 to 2011 Q4 because of the availability of the data. The interest rate is the 3-month deposit rate where t indicates a quarter. This rate is set by the People ’s Bank of China as the most importantbenchmark rate in China. The quarterly inflation rate observation is converted from the monthly observation of the Consumer Price Index (CPI) based inflation rate. The quarterly observation of output gap is tricky. As there is no official data of potential output of China and output gap is difficult to measure, I simply use the cycle part of Gross Domestic Products (GDP) as my observation of output gap of China. Although business cycles are not necessarily a reflection of inefficiency, the cycle part might be a good estimator. Then follow the literature to construct the output gap using the standard filtering procedure initiated by Hodrick and Prescott (1997), as shown in figure 1.3Figure 1Before importing into software for calculation, the following treatment should be done to the data. All data are log difference data for estimation while the time series of GDP is converted to real term and seasonally adjusted before taking log. The source of GDP and CPI is the National Bureau of Statistics of China and the interest rate data comes from the People’s Bank of China4. 4.2.Calibration3Data processing refers to Longzhen Fan a, Yihong Yu a, Chu Zhang, (2011), “An empirical evaluation of China’s monetary policies”, Journal of Macroeconomics, vol 33: 358-371.4Almanac of China's Finance and Banking, 1992-2012.The parameters in the model can be classified into three types. The first one is structuralparameters, e.g. discount factor β, which can be calibrated according to related literature. Thesecond one is the parameters related to exogenous shocks, e.g. the standard error of shocks σεi, which are calibrated according to China data. The third one is the steady state value of variables, which are calibrated by data. The calibration of parameters are summarised in Table 1.TABLE 1: Parameter CalibrationStructural Parameters βθασφyφπρvρa g Value 0.99 - 0 1 1 0.125 1.5 0.5 0.9 0.9Std err σεv σεaσεgValue 0.01 0.01 0.01Variables ỹtπt i t r t n v t a t g tSteady State Value 0 0 0 0 0 0 0The calibration for price stickiness rate θis different according to different cases. Here I examine three cases, which is θ=0.01, θ=0.33, θ=0.66 respectively.4.3.EstimationThere are three observable series and three exogenous shocks; therefore the model is just identified. The econometric approach follows Smets and Wouter (2007). Prior distributions for parameters are summarized in Table 2, which are similar to those in Smets and Wouters. The parameters whose value is between 0 and 1 are assumed to follow a Beta distribution and the standard error of shocks are assumed to follow an Inverse Gamma distribution. Draws from the posterior distribution are generated through Metropolis-Hastings algorithm. The model is estimated using Matlab Toolbox Dynare 4.3.3.TABLE 2Parameter Range Density θ[0, 1) Betaβ[0, 1) Betaα[0, 1) BetaσR+ NormalφR+ Normal φy[0, 1) Betaφπρv ρa ρg [0, 1)[0, 1)[0, 1)[0, 1)BetaBetaBetaBetaσεvR+ InvGammaσεa R+ InvGammaσεgR+ InvGammaGiven data and prior distributions of parameters, I use the methods mentioned above to do thenumerical simulation of two independent Markov chains which contains 10000 samples respectively. Based on these two independent Markov chains, Section 5 shows the results of parameter estimation.5.ResultsThis essay is an empirical study of monetary policy shock to Chinese macroeconomic fluctuations under different price stickiness rate. Thus, the estimation results will give the estimation of the parameters which are mostly concerned by this topic, i.e. stickiness rate θ,discount factor β, and standard errors of shocks σεi. In the following parts, I first give the estimation results of parameters concerned, then analyse the impulse response of output, nominal interest rate, real interest rate, inflation rate after a monetary policy shock, and finally compute the variance decomposition of different shocks to see the contribution of shocks to Chinese macroeconomic fluctuations under the case θ=0.01, θ=0.33, θ=0.66 respectively.5.1.Estimation resultsWhen θ=0.01, there is almost no price stickiness in this economy. And we know that the mechanism of monetary policy affecting the real side of the economy is through price stickiness. Thus, we can expect that monetary policy will have no effect in this case. When θ=0.33, there is some price stickiness in the economy, and the level is chosen for what Cai (2012) found in his paper of estimation of the level of China’s price stickiness. As the exi stence of price stickiness, we can expect the shifts of real variables such as output and real interest rate after a monetary policy shock. When θ=0.66, the price stickiness level is similar to those in advanced economies. As the existence of the high price rigidities, the change in the interest rate concurrently shifts the real interest rate, thereby influencing the demand and consequently changing the level of output of the economy.The priors and posteriors of the parameters concerned are plotted in Figure 2 and the prior distributions and posterior estimation results which contain posterior mean and 95% confidence interval are provided in table 3-5 for the three cases respectively.Figure 2: Priors and PosteriorsThe price stickiness rate θis estimated to be around 0.1 in the first case, which is far from theprior mean. Thus, there is a high possibility that the prior assumption is wrong. When price stickiness exists, the posterior mean is close to the prior mean in both cases, and the one in the second case is much closer. The confidence interval for θin the third case is (0.3889, 0.6419), which is all below 0.66. This indicates that the high stickiness assumption is less likely to be true in China.The discount factor βis estimated to be near 1 in both the second case and the third case when price stickiness exists; however, it is estimated much lower (around 0.7) when the economy is without price stickiness. This indicates that the household discounts the future more when price can be adjusted freely. And the stickier price is, the less household discounts the future. As there is a high possibility that China’s price stickiness level is low, we can expect a lower discount factor, which is consistent with the actual results.The results of estimated standard deviation of shocks show that in all three cases, the government spending shock is the least stable (with standard error around 0.06) one and becomes more stable as the price stickiness rate increases. The standard error of monetary policy shock is estimated to be low (around 0.02) under the three cases. The standard error of technology shock is estimated to be around 0.03.TABLE 3 : Estimation Results (θ=0.01)ParametersDistribution Prior Mean Posterior Mean Confidence interval βBeta 0.99 0.7618 (0.6701, 0.8696)θBeta 0.01 0.0978 (0, 0.2535)Standard Deviation Of ShocksDistribution Prior Mean Posterior Mean Confidence intervalMonetary Policy Shock (εv) Inverse Gamma 0.01 0.0183 (0.0159, 0.0206) Government Spending Shock (εg) Inverse Gamma 0.01 0.068 (0.0588, 0.0767) Technology Shock (εa) Inverse Gamma 0.01 0.0298 (0.0259, 0.0337)TABLE 4: Estimation Results (θ=0.33)ParametersDistribution Prior Mean Posterior Mean Confidence Interval βbeta 0.99 0.9857 (0.9605, 1)θbeta 0.33 0.3633 (0.1628, 0.5459)Standard Deviation Of ShocksDistribution Prior Mean Posterior Mean Confidence IntervalMonetary Policy Shock (εv) Inverse Gamma 0.01 0.0183 (0.0163, 0.0211) Government Spending Shock (εg) Inverse Gamma 0.01 0.0637 (0.0557, 0.0718) Technology Shock (εa) Inverse Gamma 0.01 0.0268 (0.0235, 0.0302)TABLE 5 : Estimation Results (θ=0.66)ParametersDistribution Prior Mean Posterior Mean Confidence Interval βbeta 0.99 0.9934 (0.9787, 1)θbeta 0.66 0.5088 (0.3889, 0.6419)Standard Deviation of ShocksDistribution Prior Mean Posterior Mean Confidence IntervalMonetary Policy Shock (εv) Inverse Gamma 0.01 0.0203 (0.0177, 0.0229) Government Spending Shock (εg) Inverse Gamma 0.01 0.0519 (0.0455, 0.0590) Technology Shock (εa) Inverse Gamma 0.01 0.0303 (0.0264, 0.0343)5.2.Impulse response functionImpulse response functions describe how the economy reacts over time to exogenous impulses.5The fluctuation of the economy concerned in this model is driven by three shocks, the technology shock (εa), the government spending shock (εg) and the monetary policy shock (εv). As the topic is focused on the effect of monetary policy shock, the impulse response functions after a contractionary monetary policy shock are plotted blow in order to see how variables change under different price stickiness level. The smoothed monetary shocks are plotted in figure 3. From the figure, we can see than there is a contractionary monetary policy shock in the first place.5James Hamilton (1994), Time Series Analysis, Chapter 1, page 5. Princeton University Press.。
国际经济外部冲击对中国经济的影响
国际经济外部冲击对中国经济的影响近年来，国际贸易形势风云变幻，各种经济政策引发了一系列的国际经济外部冲击，不仅影响着发达国家的经济发展，也带来了一定的不利影响和挑战，对中国经济的影响尤为明显。
首先，恶性通货膨胀。
由于一些发达国家宽松的货币政策，以及全球主要经济体的财政政策，导致贸易保护主义加剧、各种制裁和贸易战打击，全球贸易秩序陷入动荡，使全球资本市场波动加剧和国际价格体系失衡。
这些积极因素在一定程度上导致了国际性通货膨胀。
当通货膨胀率过高或通货膨胀水平不能维持稳定，将对中国经济产生深刻的影响，使物价水平不稳定，特别是影响到居民的消费能力，导致消费水平下降和企业利润下滑。
其次，国际贸易阻碍。
经过多年的快速发展，中国已经成为全球贸易的重要主体，但传统的贸易模式已经无法适应全球的经济变化。
在国际贸易中，针对中国的贸易壁垒不断增加，造成了严重的阻碍，导致中国的国际贸易逐渐陷入困境。
对中国制造业影响最为明显，因为中国制造业是全球供应链的一部分，但受制于国际贸易壁垒，中国制造业无法顺利地参与全球供应链。
最后，全球资本市场波动。
当前，全球经济面临的挑战日益加剧。
特别是受制于贸易壁垒，各国的经济发展逐渐失去平衡，不同国家的市场产生重大波动和影响，对全球资本市场产生了巨大冲击。
这将不可避免地对中国经济造成一定的影响。
总的来说，国际经济外部冲击对中国经济的影响尤为明显。
应对这种情况，首先需要加强对国际贸易壁垒的破解和转换思路，推进贸易自由化和全球化，实现互利共赢。
同时，加强经济的多元化，把握全球经济新动向，开发新的经济增长点。
其次，加强对经济波动的监管和控制，实行灵活的货币政策和财政政策，有效防范风险，稳定市场信心。
最后，全面提升企业的竞争力和创新能力，改革和优化产业结构，加快经济转型升级和创新驱动发展，实现经济可持续发展。
宏观经济学对国际贸易的影响与解释
宏观经济学对国际贸易的影响与解释随着全球化的不断深化，国际贸易成为各国经济发展的重要组成部分。
而宏观经济学作为研究整体经济运行的学科，对国际贸易起着至关重要的影响。
本文将从汇率、经济政策、国际收支等方面论述宏观经济学对国际贸易的影响，并进行相应解释。
1. 汇率对国际贸易的影响汇率是国际贸易中至关重要的因素之一。
汇率的波动直接影响着不同国家之间商品和服务的价格，从而影响其国际贸易活动。
首先，汇率的升值会使得国内货币变得相对强势，这将导致出口商品价格上升，从而减少了外国人购买本国商品的欲望，进而影响了出口贸易的规模和收益。
相反，汇率贬值会使得出口商品更具竞争力，从而刺激了国际贸易的增长。
其次，汇率的波动也会影响企业的生产成本。
如果本国货币贬值，进口原材料的成本将会上升，对企业的利润产生负面影响；相反，本国货币升值会降低进口成本，有利于企业的盈利。
2. 经济政策对国际贸易的影响宏观经济政策，如货币政策和财政政策，对国际贸易同样具有深远影响。
货币政策的宽松或紧缩会直接影响到利率水平，从而影响了国际资本流动。
高利率会吸引外资流入，从而使本国货币升值，影响出口；低利率则相反。
此外，货币政策的影响还会通过对通货膨胀率的调控，间接地影响着国际贸易的竞争力。
财政政策通过对税收和支出的调控，也会对国际贸易产生影响。
如果一个国家通过减税和增加政府支出来刺激经济，将会提高国内需求，从而导致进口的增加。
相反，通过紧缩政策来抑制通胀，可能会导致国内需求下降，进而影响国际贸易。
3. 国际收支对国际贸易的影响国际收支是一个国家对外经济活动的总体表现，直接反映了一个国家的国际收入与支出状况。
宏观经济政策的调控也会影响到国际收支的平衡。
如果一个国家的国际收入大于支出，将会出现顺差，意味着该国家是一个净出口国。
相反，如果国际支出大于收入，将会出现逆差，意味着该国家是一个净进口国。
国际收支的差异将会影响到国际储备的变化，进而影响到汇率水平。
国际贸易战下的汇率变动对中国经济的影响分析
国际贸易战下的汇率变动对中国经济的影响分析今年以来，全球经济持续低迷，美中贸易争端持续升级，形成了多轮互相加征关税的局面。
这样的贸易战不仅影响着两国的经济，而且会对全球市场造成深远的影响。
在这种情况下，汇率变动成为各家关注的焦点。
本文将分析国际贸易战下的汇率变动对中国经济的影响。
一、汇率变动对贸易的影响汇率变动对贸易有着重要的影响。
一般而言，当一个国家的货币升值时，其出口产品的价格将因为汇率上升而上涨，同时，进口产品的价格将下降。
相反，当一个国家的货币贬值时，其出口产品的价格将因为汇率下降而下降，同时，进口产品的价格将上涨。
因此，汇率变动的影响将直接影响到国际贸易。
从中国的实际情况来看，人民币贬值对出口有利，但同时也会导致原材料等进口成本增加，这将意味着企业成本增加，对民生和经济造成一定负面影响。
特别是在美中贸易争端的背景下，这种影响将更加明显。
当美国加征关税时，中国企业需要支付更高的成本，而且可能会失去市场份额。
此外，美元升值和人民币贬值都将使中国企业的外债压力加大，因此我们需要考虑这些问题。
二、企业决策的影响汇率变动不仅影响国际贸易，还对企业决策产生影响。
比如，人民币贬值将使进口产品变得更昂贵，因此企业可能会选择寻找更多的本地替代产品。
同时，成本上升可能会使企业降低生产水平，这可能会导致员工失业。
相反地，人民币升值将改变出口份额，企业可能会选择提高产品质量或开发新产品以提高竞争力。
当然，这将需要进一步投入研发、人才和技术等资源。
三、政策层面的影响汇率变动对政策层面也有着重要的影响，即需要重点解决汇率变动对人民币稳定的影响，以维护国内社会和经济的稳定。
中国央行可以采取措施来防范汇率剧烈波动，如调整货币政策等。
同时，从监督管理层面来看，还需要加强外汇汇率管理、监管和风险防控建设。
总之，在当前的国际贸易战下，人民币汇率变动对中国经济产生了一定的影响。
贸易下降和企业生产受到影响使得政策制定者需要考虑如何应对汇率变动。
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1978~ 2005 车维汉 贾利军
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内容提要
本文将国际贸易对中国宏观经济波动的影响分解为供给冲击 、 国外需求冲击和名义
冲击三个方面 , 通过构建结构向量自回归模型, 对改革开放以来中国宏观经济波动的贸易冲击效应 进行经验检验 , 识别了国际贸易结构性冲击 , 并考察了各种冲击对中国经济波动的动态效应 。研究 结果表明 : 预测期内 , 在国际贸易三种结构性冲击中, 国外需求冲击与供给冲击能在较大程度上解释 中国经济波动 , 所不同的是前者为正效应, 后者为负效应 , 前者短期效应较明显, 而后者长期效应较 明显 ; 名义变量 (贸易条件 )对经济波动的冲击效应则不显著。 随着中国汇率制度的改变, 贸易条件 冲击成为影响宏观经济波动的主要因素 , 应引起重视 。 关 键 词 国际贸易冲击 经济波动 结构向量自回归 脉冲响应函数 方差分解
车维汉 : 上海 财经 大 学 国际 工 商 管理 学 院 上 海市 国 定 路 777 号 200433 电 子 信箱 : sjjjcw h@ hot m ai. l com 电话 : 021 -
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65162066 ; 贾利军 : 上海大学悉尼工商学院
上海市嘉定区城中路 20号
201800 电子信箱 : jlj1226 @ 163. com。
一
引言
2002年中国经济增长率 升到 8. 3% , 至此宏观 经济波动开始进 入新一轮的周 期, 在 2003、 2004 和 2005年 , 中国经济连续三年保持了 9 % ~ 9 . 5 % 的适度高位平稳增长 ( 刘树成等, 2005) , 2006 年经济增长 率高达 11 . 1 % 。 一些国家的发展经验表明 , 在国内经济增长态势较好的情况下 , 如何防范国际冲击的 负面影响, 是一个应高度重视的问题。尤其是在该国外贸依存度较大的情况下, 更是如此。 有关中国经济波动问题的文献, 大多以封闭经济为条件 , 从需求、 供给等角度进行研究 , 得出了有意 义的结论。从需求角度看 , 刘树成等 ( 2005) 对中国 2002 年以来新一轮经济周期进行了综合分析, 认为消 费结构升级的同时产生经济的长期增长和短期波动; 郭庆旺 ( 2004) 认为, 投资波动是改革开放以来中国 经济波动的冲击源。从供给方面看, 不少学者认为技术冲击和劳动供给的变化是中国经济波动的主要原 因 ( 卜永祥和勒炎 , 2002 ; 陈昆亭等 , 2004 ; 黄赜琳, 2005)。 有关国际贸易与经济波动二者相互作用的机制, 传统观点认为 ( M acbean , 1966 ; K rugm an , 1985) , 国 际贸易对经济波动的冲击主要体现在收入效应和价格信号效应上。出口收入的不稳定会传导到国内经 济 , 使国内需求出现变化, 这种变化以及获得进口原料机会的不确定 , 会挫伤投资者的积极性 , 不利于经
b ( ct+ , lt+ )
( 6)
其中, 0< b < 1, 表示折现因子 , E t 表示在第 t 期的信息集合下求条件期望。在均衡状态下对模型求 解 , 解出的劳动、 消费、 资本、 产出都会随着随机性技术冲击 Z t的波动而波动, 从而为现实宏观经济的波动 提出了理论解释。
三
数据和模型构建
作者感谢匿名审稿人对本文提出的建设性修改意见 , 同时感谢上海财经大学黄赜林、 吉缅周等提出的宝贵建议。当然 , 文责自负。 本文数据如果未特别标注 , 均来自 中国经济信息网统计数据库 。
世界经济 *
2008年第 4 期
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国际贸易冲击效应与中国宏观经济波动: 1978~ 2005 济平稳增长 ; 出口不稳定也可能会扰乱相对价格传递的信号 , 投资者因此无从选择能使产出最大的投资 , 在既定的投资水平上降低了经济增长率。以 F ried m an( 1993) 为代表的货币主义学派的永久性收入假说 , 在微观基础上提出了相反的结论。他们认为收入越是围绕永久性水平上下波动 , 为维持不变的消费水 平 , 就越要储蓄。也就是说如果出口收入的不稳定传导到家庭, 他们将更多储蓄 , 该国也就能为更多的投 资融通资金 , 经济会得到更快增长。 Dav id ( 1993)从计量经济学角度检验了国际贸易波动对 GDP 的冲击效应。通过对一个包括 85 个国 家的 15 年 ( 1970~ 1985 年 ) 样本进行分析 , 发现出口波动确实提高了投资与 GDP 的比率, 但却降低了 GDP的增长率。 上述文献从不同角度分析了国际贸易波动与经济增长的关系 , 但存在以下几方面不足: 首先 , 注重分 析出口额变化与 GDP、 投资增长率之间的短期关系 , 忽视了长期的连续效应。其次, 注重用单方程模型说 明出口或投资对产出的影响。而经验已经证明产出变动反过来也会影响出口的变化 , 进而形成循环效 应。最后, 一些研究认为贸易对经济波动的影响是通过储蓄和投资效应间接实现的 , 但如果一国投资不 仅来源于储蓄, 而且是更大程度依靠其他来源 , 贸易对产出的影响效应可能会改变。 国内有关国际贸易与中国宏观经济关系的研究多集中于进出口对经济增长的影响 (刘学武, 2000 ;潘 向东, 2005), 而对于国际贸易与宏观经济波动之间相关性和作用机制的研究比较欠缺。孙立坚、 孙立行 ( 2005)用 EGARCH - VAR 模型检验了对外开放和中国宏观经济波动的关联性; 杜婷、 庞东 ( 2006) 在分析 国际贸易波动特征及其与经济周期性波动关系的基础上 , 通过国际贸易乘数效应研究国际贸易冲击对经 济周期性波动的影响。上述文献研究表明 , 近年来中国经济波动与国际贸易呈现出较大的相关性。 本文构建结构向量自回归模型 ( SVAR) , 对 1978~ 2005年间中国宏观经济波动的国际贸易冲击效应 进行了研究。本研究与以往研究不同之处 : 第一 , 应用 SVAR 模型研究国际贸易对中国经济波动的短期 和长期冲击效应 ; 第二 , 识别了国际贸易对宏观经济波动的结构性冲击因素 , 包括供给冲击、 国外需求冲 击和名义冲击; 第三, 模拟结果不仅显示出国际贸易对经济波动的冲击效应 , 而且有助于分析经济波动对 国际贸易的影响 ; 第四 , 用脉冲响应和方差分解方法定量研究国际贸易冲击对中国经济波动的动态效应。
国际贸易对宏观经济的冲击主要表现在三个方面: 首先 , 从国外总需求角度看, 贸易伙伴国的产出水
世界经济 * 2008年第 4 期 27
国际贸易冲击效应与中国宏观经济波动: 1978~ 2005 平或者对中国产品偏好影响了对中国产品需求 , 总需求变化影响了中国的出口额 , 出口的变动通过收入 效应对中国宏观经济波动造成冲击 ; 其次 , 从贸易条件看, 贸易条件的变化直接影响中国出口产品的价 值 , 进而通过价格效应影响中国的产出波动; 最后, 通过进口 , 产生技术溢出、 技术扩散等效应 , 对中国生 产率形成冲击, 本文称为供给冲击。 根据以上分析, 本文以国内生产总值 (GDP ) 变动状况衡量中国宏观经济波动, 由于 GDP 反映了一国 居民的收入水平, 可把 GDP 自身的冲击定义为国内居民收入冲击 , 假定其他因素不变 , 居民收入水平的 变化不仅影响了对国内产品的需求, 而且影响对国外产品的需求; 以出口额 (EX )衡量外国居民对中国产 品的总需求 , 把 EX 的冲击定义为国外需求冲击; 用贸易条件 (TOT ) 衡量中国出口产品价值 , 把 TOT 的冲 击定义为名义冲击; 用全要素生产率 (TFP ) 衡量国际贸易对中国经济技术进步的影响, 技术进步提高了 一国劳动生产率 , 增加了产品的供给 , 因此 , 本文把 TFP 的冲击定义为供给冲击。 (一 )变量选取和数据来源 本文涉及的时间序列数据包括中国各年份的 GDP、 出口额 (EX ) 、 贸易条件 (TOT ) 以及全要素生产率 (TFP )。 1 . 在 GDP、 出口额 (EX )的统计中, 为消除价格因素的影响 , 采用 1978 年的不变价格。其中, 各年份 GDP的 1978 年不变价来源于 2006 年 中国统计年鉴 , EX 是根据 2006 年 中国海关统计年鉴 和 中国 统计年鉴 相关数据整理所得。 2 . 贸易条件 ( ter m s of trade, TOT ) 。贸易条件又称交换比价 , 即出口价格指数与进口价格指数的比 率 , 是指一个单位的出口商品可以换回多少进口商品。贸易条件恶化是指出口 1 个单位商品所换回的进 口商品减少。主要包括三种情况: 出口价格下降而进口价格上升; 进出口价格均下降 , 但出口价格下降幅度 大于进口价格下降幅度; 进出口价格均上升 , 但出口价格的上升幅度小于进口价格的上升幅度。本文的贸 易条件获取采用世界银行 世界发展指标数据库 中以 2000年为基期的出口价格指数与进口价格指数之 比。 3 . 全要素生产率 (TFP )。全要素生产率也称总和要素生产率, 是指除劳动和资本这两大物质要素之 外的所有其他要素带来的产出增长率 , 主要是指因技术进步提高了的效率, 理论上全要素生产率与一国 经济增长呈正相关关系。本文全要素生产率的获取采用了张军和施少华 ( 2003) 的方法, 利用 中国统计 年鉴 中资本形成数据 ( 1978 年不变价 )和劳动投入数据 , 用道格拉斯生产函数建立回归模型 , 求出资本、 劳动的投入产出弹性系数后, 代入全要素生产率公式, 计算出 1978~ 2005 年各年全要素生产率。 我们对 TFP 、 EX 、 TOT 和 GDP 四个变量进行 ADF 单位根检验 后发现, 四个变量的时间序列均不为 I ( 0) 序列 , 且存在一个单位根 (即 I( 1)序列 ) ; 对这四个变量的增长率 RTFP、 REX 、 RTOT 和 RGDP 进行检 验 , 发现增长率变量的时间序列是 I( 0) 序列。为保持数据的平稳性 , 本文使用四个变量的增长率来研究 这四个变量的相互关系, 这里增长率为实际增长率。 (二 )模型设定及相关检验 一般的模型只单向描述自变量的改变对因变量产生的影响, 向量自回归模型 ( VAR) 则考虑了模型中 各变量之间的相互作用, 被公认为描述变量间动态关系的一种实用方法。 VAR 是 S i m s( 1980) 首先提出 的研究各个变量之间关系的非结构建模方法。 B lanchard 和 Quah( 1989) 通过对 VAR 模型施加基于经济 理论的长期约束条件 , 开创性地把 VAR 模型应用到宏观经济波动理论, 识别出经济中的总需求和总供给