此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子一次的事！外文标题：U.S. Agricultural Export Competitiveness and Export Market Diversiftcation外文作者：Jaya Jha,Terry L. Roe文献出处:Agricultural export trade.2018.12(4):256-268(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文1982单词，11784字符(字符就是印刷符)，中文3189汉字。U.S. Agricultural Export Competitiveness and Export Market DiversiftcationJaya Jha,Terry L. RoeAbstractThis paper examines the structural relationship between U.S. agricultural exports, foreign GDP growth, and real exchange rate volatility, and the impact of exogenous shocks on the evolution of export growth to examine the sector’s international competitiveness and opportunities for export extensification. The long- and short-run dynamics of export demand are analyzed within the structural cointegrating vectorautoregressive framework. Principal findings are that: 1. Exports of high-value processed agricultural products are more sensitive to changes in foreign income and exchange rate fluctuations than exports of low-value grains and bulk commodities. Specifically, a 10% growth in trade-adjusted GDP across all importing countries leads to a 7.8% increase in U.S. exports of bulk commodities compared to 33% increase in exports of high-value processed commodities. Similarly, a 10% increase in the value of the trade-weighted exchange rate (i.e., an appreciation of the U.S. dollar) reduces bulk exports by 8.4% compared to a whopping 35% decline in high-value processed food exports; 2. In response to exogenous shocks, deviations from the predicted equilibrium level of exports are corrected at a much faster rate for grains and other bulk commodity exports than export of high value commodities. For example, more than 75% of the disequilibrium in aggregate bulk commodity exports is corrected within one year; less than 15% of the disequilibrium in high-value processed exports is corrected within a year. 3. The present concentration of U.S. agricultural commodity exports to a few developed countries is increasingly problematic, U.S. agricultural exports may benefit not only from policies intended to increase trade with existing developing country importers but also from policies that aim to export agricultural commodities to emerging markets. Our paper also highlights the importance of including the long-run relationship when modeling the short-run dynamics.Keywords: U.S. agricultural exports, foreign income, exchange rates, cointegrating VAR, bounds test, income and price elasticities, export demand, structural impulse response functions1IntroductionEconomic growth in developing countries has been accompanied by a dramatic rise in developing countries’ share of world trade [World Trade Organization,2014]. Growth in world food demand, pulled by rising incomes and the rising opportunity cost of household member time, is changing the composition as well as destination of U.S. agricultural exports.The expansion of U.S. agricultural exports along the extensive margin is plotted in figure1. The upper segment of the bars represents the fraction of countries that do not import agricultural commodities from the U.S: thus, in 2010 U.S. agricultural exports reached 85% of the 219 countries in the sample compared to 65% in 1967, indicative of the expansion of agricultural exports along the extensive margin. However, inspection of the intensive margin of U.S. agricultural exports (figure2) reveals that for every year in the sample, 25 countries have accounted for at least 80% of all agricultural exports. While the countries in the top-25 list have changed remarkably1, U.S. agricultural exports have remained concentrated in a handful of countries. Thus, expansion of agricultural trade along the extensive margin has not been a major factor in the growth of U.S. agricultural exports.2Conceptual FrameworkThe basic setup of the analytical model follows that of Senhadji and Montenegro (1998). Consider a two-country world: a home country (exporter) and a foreign country (importer). Following the typical growth model structure (e.g. Barro and Sala-i-Martin, 2004), households consist of finitely lived agents, behaving altruistically: they provide transfers to their future generations, whose welfare they discount, who in turn provide transfers to their future generations, and so on.The optimization problem facing the representative agent in the foreign country is to maximize the discounted present value of inter-temporal utility:subject to the budget constraintHere, u(d∗t , m∗t ) is the felicity function, and ρ is the consumer’s rate of time-preference, assumed to be constant to ensure that the discount rate is the same across generations. If b∗t+1 is positive, the foreign country holds a stock of home bonds in the next period; conversely, if b∗t+1 is negative, the home country holds a stock of foreign bonds in the next period.In addition, we assume that , which implies that the net present value of assets is asymptotically negative. This is the familiar ‘no-Ponzi games’ condition to prevent households from running Ponzi schemes by accumulating debts forever. Furthermore, to ensure that the felicity function is strongly separable, we assume that u(·) is addilog3 and satisfies:where At and Bt are scale parameters, and α and β are curvature parameters.We can solve this optimization problem by setting up the present-value Hamiltonianand taking partial derivatives with respect to the choice variables, d∗t and m∗t , and the co-state variable, λt. Solving for m∗t and taking log on both sides, we can express foreign country’s demand for home country’s goods as:3Econometric MethodologyThe principal steps in the research methodology consist of: first, establishing the order of integra- tion of variables in the export demand equation; second, selecting an appropriate error correction specification of export demand that passes model diagnostic tests (serial correlation, normally distributed errors, dynamic stability); third, testing for the presence of a long-run relationship un- derlying the core variables; and, finally, conditional on the null of no long-run relationship being rejected, estimating parameters of the export demand model and examining short-run dynamics.3.1A Long-Run Model of Export DemandTo keep notation simple, commodity subscripts are suppressed. Assuming that the structural export demand equation (7) can be well-approximated by a log-linear vector autoregression (VAR) model, let be the vector of endogenous variables: quantity exported by the home country, index of importing countries’ trade-adjusted GDP, and index of importing countries’ trade-weighted real exchange rate, respectively, all expressed in natural logs.This VAR model can be rewritten in its conditional vector error correction (VECM) form as:Here, a is a vector of constant terms, ϑ is a vector of trend coefficients, Φi is a matrix of VAR parameters for the i’th lag, and εt is a vector of error terms, εt ∼ IN (0, Ω), Ω is positive definite.The unrestricted vector error correction model has the following representationwhere is the vector of endogenous variables,II and Fi are matrices of long-run multipliers and i’th-lag short-run response parameters, respectively:∆ is the difference operator, b = (bx, bg, br)j is a vector of intercepts; θ = (θx, θg, θr)j is a vector of trend coefficients; p is the number of lagged differences of the endogenous variables; and ut = (ux, t, ug, t, ur, t)j is a vector of serially-uncorrelated zero-mean stationary errors. Thus, the VECM form of the export demand equation can be expressed as：4 Aggregate Analysis: U.S. agricultural exports to all countriesTo allow comparison of regression estimates across models with varying lag structures, we begin our analysis with the sample from 1971 to 2010, i.e., 40 observations. The rationale is that the lag order of the underlying VAR should be sufficient to remove residual serial correlation without sacrificing too many degrees of freedom due to over-parametrization. One rule of thumb is to start with the maximum lag order p, such that p = √4 T , where T is the sample size [Baum,2006, p.140]. Having 44 observations (1967 – 2010), our analysis begins with a maximum lag length of 3 (≈ √4 44). The first observation is used to construct first differences of the variables, the next three, to construct the lagged series. This leaves a uniform sample with 40 observations.4.1A Long-run Model of Export DemandThe AIC, SBC, and HQIC values reported in table5suggest that for most commodities and commodity categories, a VAR with two lags, or equivalently, a VECM with one lag, is sufficient. Table6reports theF − and t−statistics for testing the existence of the long-run export demand equation for models with and without a deterministic linear trend, and alternate lag specifications. Overall, a structural export demand equation can be established for 21 out of 32 commodities and commodity groups13. A trend in the cointegrating relationship is selected for total value of agricultural exports, soybean, tobacco, and vegetable juices; for all others, an error correction specification with unrestricted intercept and no deterministic trend is selected.5ConclusionWe develop a structural model of foreign demand for U.S. agricultural exports, foreign GDP, and real exchange rate volatility to examine the sectors international competitiveness and opportunities for export extensification.Estimates of long-run multipliers suggest that exports of high-value processed agricultural products are more sensitive to changes in foreign income and exchange rate fluctuations than exports of low-value grains and bulk commodities. Thus, equal growth across all importing countries leads to a smaller increase in U.S. exports of bulk commodities than high-value processed commodities, and real appreciation of the dollar leads to a more than proportionate decline in U.S. exports of processed meats and vegetables relative to bulk exports.Finally, interactions among variables in a macroeconomic model are often far more complex than what is captured by long-run equilibrium relations alone; studying the short-run transition dynamics provides a richer understanding of the underlying structure of the model. For example, while depreciation of developed countries’ currencies may produce a larger increase in exports of processed foods relative to grains, we have shown that relative to developed countries, developing countries are more resilient to exogenous shocks and disequilibrium errors are corrected quickly.ReferencesHirotogu Akaike. Information Theory and an Extension of the Maximum Likelihood Principle. In Emanuel Parzen, Kunio Tanabe, and Genshiro Kitagawa, editors, Selected Papers of Hirotugu Akaike, Springer Series in Statistics. Springer New York, New York, NY, 1998. ISBN 978- 1-4612-7248-9. doi: 10.1007/978-1-4612-1694-0. URL http://link.springer.com/10.1007/978-1-4612-1694-0.Anindya Banerjee, Juan Dolado, and Ricardo Mestre. Error-correction Mechanism Tests for Coin- tegration in a Single-equation Framework. Journal of Time Series Analysis, 19(3):267–283, may 1998. ISSN 0143-9782. doi: 10.1111/1467-9892.00091. URL http://doi.wiley.com/10.1111/1467-9892.00091.Christopher F Baum. Stata: The language of choice for time-series analysis? The Stata Journal, 5(1):46–63, 2005. URL http://ideas.repec.org/a/tsj/stataj/v5y2005i1p46-63.html.Christopher F Baum. An Introduction to Modern Econometrics using Stata. StataCorp LP, 2006.URL http://ideas.repec.org/b/tsj/spbook/imeus.html.Robert G. Chambers. Agricultural and Financial Market Interdependence in the Short Run. 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Hashem Pesaran, and Yongcheol Shin. A Structural Cointegrat- ing VAR Approach to Macroeconometric Modelling. Edinburgh School of Economics Discussion Paper 8, 1998. URL http://ideas.repec.org/p/cam/camdae/9823.html.David E. Giles. Stationarity Conditions for an AR(2) Process. Notes, Department of Eco- nomics, UniversityofVictoria,Canada,2013.URLhttp://davegiles.blogspot.com/2013/06/when-is-autoregressive-model.html.Kristian Skrede Gleditsch. Expanded Trade and GDP Data. Journal of Conflict Resolution, 46 (5):71–724, 2002. URL http://econpapers.repec.org/RePEc:sae:jocore:v:46:y:2002:i: 5:p:712-724.Jason H. Grant and Dayton M. Lambert. Do Regional Trade Agreements Increase Members’ Agricultural Trade? American Journal of Agricultural Economics, 90(3):765–782, aug 2008.美国农产品出口竞争力以及出口市场的多元化Jaya Jha,Terry L. Roe摘要：在本文中，考察了美国农业出口、国外GDP增长和实际汇率波动之间的结构关系，以及外部冲击对出口增长演变的影响，从而去检验该部门的国际竞争力和出口扩张的机会。本文在结构向量自回归框架内分析出口需求的长期和短期动态。 主要的调查结果如下：1.高价值加工农产品的出口对外国收入和汇率波动的变化比低价值谷物和大宗商品的出口更为敏感。 具体而言，也就是所有进口国贸易调整后，其国内生产总值增长了10％，并给美国大宗商品出口带来了7.8％的增长，相比之下高价值加工商品出口增长了33％。类似的情况是，贸易加权汇率（即美元升值）增加了10％，使大宗出口减少了8.4％，而高价值加工食品出口则下降高达35％; 2.为了应对外部冲击，相比较于出口高价值商品，粮食和其他大宗商品出口的出口偏差远远超过预测的出口平均水平。例如，超过75％的总体商品出口不平衡在一年内得到纠正;不到15％的高价值加工出口不平衡在一年内得以纠正。 3.目前美国农产品对少数发达国家的出口比较集中，且日益成为问题，美国农产品出口不仅可能受益于旨在增加与现有发展中国家进口商的贸易的政策，而且还有利于旨在向新兴市场出口农产品的政策。在文中，我们还强调了在短期动态建模时应该要包含长期关系的重要性。关键词: 美国农业出口、外国收入、汇率、协整向量自回归、边界检验、收入和价格弹性、出口需求，结构性脉冲响应函数1引言发展中国家的经济增长总是会伴随着发展中国家在世界贸易中的份额急剧上升[世界贸易组织，2014]。受家庭人均收入增加和家庭成员时间机会成本上升的拉动，世界粮食需求增长正在改变美国农产品出口的构成和目的地。在图1中，显示的是美国农业出口伴随着粗放边际得以扩张。条形图的上半部分代表不从美国进口农产品的国家的比例：因此，在2010年，美国农业出口达到样本219个国家的85％，而1967年为65％，表明农产品出口是伴随着粗放边际。然而，对美国农产品出口密集边际的检查（图2）表明样本中每年有25个国家占所有农产品出口的至少80％。虽然排在前25位的国家发生了显著的变化，但美国的农产品出口仍然集中在少数几个国家。因此，伴随着粗放边际去扩大农业贸易并不是美国农产品出口增长的主要原因。 图1：样本中从美国进口农产品国家的份额图二 美国农业出口值前25名进口地区的份额分解2概念框架本文中所用分析模型的基本设置是遵循Senhadji和Montenegro（1998）的基本设置。 把世界一分为二：一个是本国（出口国），另一个是外国（进口国）。 按照典型增长模型结构（例如Barro和Sala-i-Martin，2004）的观点，家庭是由有限的生活自然人组成，他们的行为都是利他主义的：他们会为子孙后代提供转移支付，也许他们享受的福利会打折扣，但反过来，他们的后代也会为未来的他们的下一代提供转移支付等等。外国进口商面临的优化问题是要把跨期效用的贴现现值最大化：受预算约束在这里，u（d * t，m * t）是费利西蒂函数，ρ是消费者的时间偏好
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