经济代写|宏观经济学代写Macroeconomics代考|ECOS3007

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宏观经济学,对国家或地区经济整体行为的研究。它关注的是了解整个经济的事件,如商品和服务的生产总量、失业水平和价格的一般行为。

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我们提供的宏观经济学Macroeconomics及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
经济代写|宏观经济学代写Macroeconomics代考|ECOS3007

经济代写|宏观经济学代写Macroeconomics代考|Geography

This is somewhat related to the luck hypothesis, but certainly distinctive: perhaps the deepest source of heterogeneity between countries is the natural environment they happened to be endowed with. From a very big picture perspective, geographical happenstance of this sort is a very plausible candidate for a determinant of broad development paths, as argued for instance by Jarred Diamond in his 1999 Pulitzer-Prize-winning book Guns, Germs and Steel ${ }^8$. As an example, Diamond suggests that one key reason Europe conquered America, and not the other way around, was that Europe had an endowment of big animal species that were relatively easy to domesticate, which in turn led to improved immunisation by humans exposed to animal-borne diseases, and more technological advances. But can geography also explain differences in economic performance at the scale on which we usually think about them, say between different countries over decades or even a couple of centuries?

On some level, it is hard to think that the natural environment would not affect economic performance, on any time frame. Whether a country is in the middle of the Sahara desert, the Amazon rain forest, or some temperate climate zone must make some difference for the set of economic opportunities that it faces. This idea becomes more compelling when we look at the correlation between certain geographical variables and economic performance, as illustrated by the Figure (7.2), again taken from Acemoglu (2009). It is clear from that picture that countries that are closer to the equator are poorer on average. At the very least, any explanation for economic performance would have to be consistent with this stylised fact. The question, once again, is to assess to what extent these geographical differences underlie the ultimate performance, and this is not an easy empirical question.

Let us start by considering the possible conceptual arguments. The earliest version of the geography hypothesis has to do with the effect of the climate on the effort – the old idea that hot climates are not conducive to hard work. While this seems very naive (and not too politically correct) to our 21 st century ears, the idea that climate (and geography more broadly) affects technological productivity, especially in agriculture, still sounds very plausible. If these initial differences in turn condition subsequent technological progress (as argued by Jared Diamond, as we have seen, and as we will see, in different forms, by Jeffrey Sachs), it just might be that geography is the ultimate determinant of the divergence between societies over the very long run.

A big issue with this modern version of the geography hypothesis is that it is much more appealing to think of geography affecting agricultural productivity, but modern growth seems to have a lot more to do with industrialisation. While productivity in agriculture might have conditioned the development of industry to begin with, once industrial technologies are developed we would have to explain why they are not adopted by some countries. Geography is probably not enough to account for that, at least in this version of the story.

经济代写|宏观经济学代写Macroeconomics代考|Culture

What do we mean by culture? The standard definition used by economists, as spelled out by Guiso et al. (2006), refers to “those customary beliefs and values that ethnic, religious, and social groups transmit fairly unchanged from generation to generation” (p. 23). In other words, culture is something that lives inside people’s heads – as opposed to being external to them – but it is not something idiosyncratic to individuals; it is built and, importantly, transmitted at the level of groups.

It is hard to argue against the assertion that people’s beliefs, values, and attitudes affect their economic decisions. It is just as clear that those beliefs, values and attitudes vary across countries (and over time). From this it is easy to conclude that culture matters for economic performance, an argument that goes back at least to Max Weber’s thesis that Protestant beliefs and values, emphasising hard work and thrift, and with a positive view of wealth accumulation as a signal of God’s grace, were an important factor behind the development of capitalism and the modern industrial development. In his words, the “Protestant ethic” lies behind the “spirit of capitalism”.

Other arguments in the same vein have suggested that certain cultural traits are more conducive to economic growth than others (David Landes is a particularly prominent proponent of this view, as in Landes (1998)), and the distribution of those traits across countries is the key variable to ultimately understand growth. “Anglo-Saxon” values are growth-promoting, compared to “Latin” or “Asian” values, and so on. More recently, Joel Mokyr (2018) has argued that Enlightenment culture was the key driving force behind the emergence of the Industrial Revolution in Europe, and hence of the so-called “Great Divergence” between that continent and the rest of the world.

A number of issues arise with such explanations. First, culture is hard to measure, and as such may lead us into the realm of tautology. A country is rich because of its favourable culture, and a favourable culture is defined as that which is held by rich countries. This doesn’t get us very far in understanding the causes of good economic performance. This circularity is particularly disturbing when the same set of values (say, Confucianism) is considered inimical to growth when Asian countries perform poorly, and suddenly becomes growth-enhancing when the same countries perform well. Second, even if culture is indeed an important causal determinant of growth, we still need to figure out where it comes from if we are to consider implications for policy and predictions for future outcomes.

These empirical and conceptual challenges have now been addressed more systematically, as better data on cultural attitudes have emerged. With such data, a vibrant literature has emerged, with economists developing theories and testing their predictions on the role that specific types of values (as opposed to a generic “culture” umbrella) play in determining economic performance. Many different types of cultural attitudes have been investigated: trust, collectivism, gender roles, beliefs about fairness, etc. This literature has often exploited historical episodes – the slave trade, the formation of medieval self-governing cities, colonisation, immigration, recessions – and specific cultural practices – religious rites, civic festivities, family arrangements – to shed light on the evolution of cultural attitudes and their impact on economic outcomes. Our assessment is that this avenue of research has already borne a lot of fruit, and remains very promising for the future. (For an overview of this literature, see the surveys by Guiso et al. (2006), Alesina and Giuliano (2015), and Nunn (2020).

经济代写|宏观经济学代写Macroeconomics代考|ECOS3007

宏观经济学代考

经济代写|宏观经济学代写宏观经济代考|地理


这在一定程度上与运气假说有关,但肯定是与众不同的:也许国家间异质性的最深层根源是他们碰巧被赋予的自然环境。从一个非常大的角度来看,这种地理上的偶然事件是广泛发展道路的决定因素的一个非常合理的候选者,例如贾里德·戴蒙德(Jarred Diamond)在他1999年获得普利策奖的书《枪炮、细菌和钢铁${ }^8$》中提出的观点。戴蒙德举了个例子,他认为欧洲征服美洲而不是美洲征服欧洲的一个关键原因是,欧洲拥有相对容易驯化的大型动物物种,这反过来又导致了接触动物传播疾病的人类的免疫力提高,以及更多的技术进步。但是,在我们通常认为的范围内,比如几十年甚至几个世纪的不同国家之间,地理位置也能解释经济表现的差异吗?


在某种程度上,很难想象在任何时间框架内,自然环境不会影响经济表现。无论一个国家是在撒哈拉沙漠、亚马逊雨林还是温带气候地带,它所面临的一系列经济机会都会有所不同。当我们观察某些地理变量与经济绩效之间的相关性时,这一观点变得更加令人信服,如图(7.2)所示,图(7.2)同样来自Acemoglu(2009)。从这张图中可以清楚地看出,平均而言,靠近赤道的国家更穷。至少,任何对经济表现的解释都必须与这个程式化的事实相一致。问题还是要评估这些地理差异在多大程度上构成了最终表现的基础,这不是一个简单的经验问题


让我们从考虑可能的概念论证开始。地理假说的最早版本与气候对努力的影响有关——即炎热的气候不利于努力工作的旧观点。虽然在21世纪的我们听来,这似乎非常幼稚(而且在政治上也不太正确),但气候(以及更广泛的地理位置)影响技术生产力,尤其是农业生产力的观点,听起来仍然很有道理。如果这些最初的差异反过来影响了随后的技术进步(正如贾里德·戴蒙德所论证的,我们已经看到了,我们还将看到杰弗里·萨克斯以不同的形式提出的),那么从长远来看,地理位置可能是社会间差异的最终决定因素


这个现代版本的地理假说的一个大问题是,认为地理因素影响农业生产力更有吸引力,但现代增长似乎与工业化有更多的关系。虽然农业生产力可能首先决定了工业的发展,但一旦工业技术得到发展,我们就必须解释为什么有些国家没有采用这些技术。至少在这个版本的故事中,地理因素可能不足以解释这一点

经济代写|宏观经济学代写宏观经济代考|文化


我们所说的文化是什么意思?经济学家使用的标准定义,如Guiso等人(2006)所述,指的是“种族、宗教和社会群体代代相传的习惯信仰和价值观”(第23页)。换句话说,文化是存在于人们头脑中的东西,而不是外在的东西,但它不是个人特有的东西;它是在群体层次上建立和传播的,这一点很重要


人们的信仰、价值观和态度会影响他们的经济决策,这一论断很难反驳。同样清楚的是,这些信仰、价值观和态度在不同的国家(以及不同的时间)是不同的。由此,我们很容易得出这样的结论:文化对经济表现很重要,这一论点至少可以追溯到马克斯•韦伯(Max Weber)的论点:新教的信仰和价值观强调努力工作和节俭,并积极地认为财富积累是上帝恩典的象征,是资本主义发展和现代工业发展背后的一个重要因素。用他的话说,“新教伦理”隐藏在“资本主义精神”的背后


其他类似的观点认为,某些文化特征比其他文化特征更有利于经济增长(大卫·兰德斯(David Landes)是这一观点的特别杰出的支持者,如兰德斯(Landes, 1998)),而这些特征在国家间的分布是最终理解增长的关键变量。与“拉丁”或“亚洲”价值观相比,“盎格鲁-撒克逊”价值观是促进增长的。最近,Joel Mokyr(2018)认为,启蒙文化是欧洲工业革命出现背后的关键驱动力,因此也导致了欧洲大陆与世界其他地区之间所谓的“大分化”


这样的解释产生了许多问题。首先,文化是难以衡量的,因此可能会导致我们进入同义重复的领域。一个国家因其有利的文化而富有,有利的文化被定义为富裕国家所拥有的文化。这并不能帮助我们深入理解良好经济表现的原因。当同一套价值观(比如儒家)在亚洲国家表现不佳时被认为不利于增长,而当同样的国家表现良好时,这种循环尤其令人不安。第二,即使文化确实是增长的一个重要的因果决定因素,如果我们要考虑对政策的影响和对未来结果的预测,我们仍然需要弄清楚它从何而来


随着关于文化态度的更好数据的出现,这些经验和概念上的挑战现在得到了更系统的解决。有了这样的数据,一种充满活力的文献出现了,经济学家们发展了理论,并测试了他们的预测,即特定类型的价值观(与通用的“文化”伞相反)在决定经济表现方面所起的作用。许多不同类型的文化态度都被调查过:信任、集体主义、性别角色、关于公平的信念等等。这些文献经常利用历史事件——奴隶贸易、中世纪自治城市的形成、殖民、移民、经济衰退——和具体的文化实践——宗教仪式、公民庆典、家庭安排——来阐明文化态度的演变及其对经济结果的影响。我们的评估是,这一研究途径已经产生了许多成果,并且对未来仍然非常有希望。(有关该文献的概述,请参见Guiso等人(2006)、Alesina和Giuliano(2015)和Nunn(2020)的调查。

经济代写|宏观经济学代写Macroeconomics代考 请认准statistics-lab™

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金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

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随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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