数学代写|计量经济学原理代写Principles of Econometrics代考|An Introduction to Econometrics

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计量经济学是以数理经济学和数理统计学为方法论基础,对于经济问题试图对理论上的数量接近和经验(实证研究)上的数量接近这两者进行综合而产生的经济学分支。

statistics-lab™ 为您的留学生涯保驾护航 在代写计量经济学Principles of Econometrics方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写计量经济学Principles of Econometrics代写方面经验极为丰富,各种代写计量经济学Principles of Econometrics相关的作业也就用不着说。

我们提供的计量经济学Principles of Econometrics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
数学代写|计量经济学原理代写Principles of Econometrics代考|An Introduction to Econometrics

数学代写|计量经济学原理代写Principles of Econometrics代考|The Econometric Model

What is an econometric model, and where does it come from? We will give you a general overview, and we may use terms that are unfamiliar to you. Be assured that before you are too far into this book, all the terminology will be clearly defined. In an econometric model we must first realize that economic relations are not exact. Economic theory does not claim to be able to predict the specific behavior of any individual or firm, but rather describes the average or systematic behavior of many individuals or firms. When studying car sales we recognize that the actual number of Hondas sold is the sum of this systematic part and a random and unpredictable component $e$ that we will call a random error. Thus, an econometric model representing the sales of Honda Accords is
$$
Q^{d}=f\left(P, P^{s}, P^{c}, I N C\right)+e
$$
The random error $e$ accounts for the many factors that affect sales that we have omitted from this simple model, and it also reflects the intrinsic uncertainty in economic activity.

To complete the specification of the econometric model, we must also say something about the form of the algebraic relationship among our economic variables. For example, in your first economics courses quantity demanded was depicted as a linear function of price. We extend that assumption to the other variables as well, making the systematic part of the demand relation
$$
f\left(P, P^{s}, P^{c}, I N C\right)=\beta_{1}+\beta_{2} P+\beta_{3} P^{s}+\beta_{4} P^{c}+\beta_{5} I N C
$$
The corresponding econometric model is
$$
Q^{d}=\beta_{1}+\beta_{2} P+\beta_{3} P^{s}+\beta_{4} P^{c}+\beta_{5} I N C+e
$$
The coefficients $\beta_{1}, \beta_{2}, \ldots, \beta_{5}$ are unknown parameters of the model that we estimate using economic data and an econometric technique. The functional form represents a hypothesis about the relationship hefween the variahles In any particular prohlem, ane challenge is tn determine a functional form that is compatible with economic theory and the data.

In every econometric model, whether it is a demand equation, a supply equation, or a production function, there is a systematic portion and an unobservable random component. The systematic portion is the part we obtain from economic theory, and includes an assumption about the functional form. The random component represents a “noise” component, which obscures our understanding of the relationship among variables, and which we represent using the random variable e.

We use the econometric model as a basis for statistical inference. Using the econometric model and a sample of data, we make inferences concerning the real world, learning something in the process. The ways in which statistical inference are carried out include the following:

  • Estimating economic parameters, such as elasticities, using econometric methods
  • Predicting economic outcomes, such as the enrollment in two-year colleges in the United States for the next 10 years
  • Testing economic hypotheses, such as the question of whether newspaper advertising is better than store displays for increasing sales

Econometrics includes all of these aspects of statistical inference. As we proceed through this book, you will learn how to properly estimate, predict, and test, given the characteristics of the data at hand.

数学代写|计量经济学原理代写Principles of Econometrics代考|Causality and Prediction

A question that often arises when specifying an econometric model is whether a relationship can be viewed as both causal and predictive or only predictive. To appreciate the difference, consider an equation where a student’s grade in Econometrics $G R A D E$ is related to the proportion of class lectures that are skipped SKIP.
$$
G R A D E=\beta_{1}+\beta_{2} S K I P+e
$$
We would expect $\beta_{2}$ to be negative: the greater the proportion of lectures that are skipped, the lower the grade. But, can we say that skipping lectures causes grades to be lower? If lectures are captured by video, they could be viewed at another time. Perhaps a student is skipping lectures because he or she has a demanding job, and the demanding job does not leave enough time for study, and this is the underlying cause of a poor grade. Or, it might be that skipping lectures comes from a general lack of commitment or motivation, and this is the cause of a poor grade. Under these circumstances, what can we say about the equation that relates GRADE to SKIP? We can still call it a predictive equation. $G R A D E$ and $S K I P$ are (negatively) correlated and so information about $S K I P$ can be used to help predict GRADE. However, we cannot call it a causal relationship. Skipping lectures does not cause a low grade. The parameter $\beta_{2}$ does not convey the direct causal effect of skipping lectures on grade. It also includes the effect of other variables that are omitted from the equation and correlated with $S K I P$, such as hours spent studying or student motivation.
Economists are frequently interested in parameters that can be interpreted as causal. Honda would like to know the direct effect of a price change on the sales of their Accords. When there is technological improvement in the beef industry, the price elasticities of demand and supply have important implications for changes in consumer and producer welfare. One of our tasks will be to see what assumptions are necessary for an econometric model to be interpreted as causal and to assess whether those assumptions hold.

An area where predictive relationships are important is in the use of “big data.” Advances in computer technology have led to storage of massive amounts of information. Travel sites on the Internet keep track of destinations you have been looking at. Google targets you with advertisements based on sites that you have been surfing. Through their loyalty cards, supermarkets keep data on your purchases and identify sale items relevant for you. Data analysts use big data to identify predictive relationships that help predict our behavior.

In general, the type of data we have impacts on the specification of an econometric model and the assumptions that we make about it. We turn now to a discussion of different types of data and where they can be found.

数学代写|计量经济学原理代写Principles of Econometrics代考|Experimental Data

One way to acquire information about the unknown parameters of economic relationships is to conduct or observe the outcome of an experiment. In the physical sciences and agriculture, it is easy to imagine controlled experiments. Scientists specify the values of key control variables and then observe the outcome. We might plant similar plots of land with a particular variety of wheat, and then vary the amounts of fertilizer and pesticide applied to each plot, observing at the end of the growing season the bushels of wheat produced on each plot. Repeating the experiment on $N$ plots of land creates a sample of $N$ observations. Such controlled experiments are rare in business and the social sciences. A key aspect of experimental data is that the values of the explanatory variables can be fixed at specific values in repeated trials of the experiment.

One business example comes from marketing research. Suppose we are interested in the weekly sales of a particular item at a supermarket. As an item is sold it is passed over a scanning unit to record the price and the amount that will appear on your grocery bill. But at the same time, a data record is created, and at every point in time the price of the item and the prices of all its competitors are known, as well as current store displays and coupon usage. The prices and shopping environment are controlled by store management, so this “experiment” can be repeated a number of days or weeks using the same values of the “control” variables.

There are some examples of planned experiments in the social sciences, but they are rare because of the difficulties in organizing and funding them. A notable example of a planned experiment is Tennessee’s Project Star.” This experiment followed a single cohort of elementary school children from kindergarten through the third grade, beginning in 1985 and ending in 1989 . In the experiment children and teachers were randomly assigned within schools into three types of classes: small classes with 13-17 students, regular-sized classes with 22-25 students, and regular-sized classes with a full-time teacher aide to assist the teacher. The objective was to determine the effect of small classes on student learning, as measured by student scores on achievement tests. We will analyze the data in Chapter 7 and show that small classes significantly increase performance. This finding will influence public policy toward education for years to come.

数学代写|计量经济学原理代写Principles of Econometrics代考|An Introduction to Econometrics

计量经济学代考

数学代写|计量经济学原理代写Principles of Econometrics代考|The Econometric Model

什么是计量经济学模型,它从何而来?我们将为您提供一般概述,我们可能会使用您不熟悉的术语。请放心,在您深入本书之前,所有的术语都会被明确定义。在计量经济模型中,我们必须首先认识到经济关系并不精确。经济理论并不声称能够预测任何个人或公司的具体行为,而是描述了许多个人或公司的平均或系统行为。在研究汽车销售时,我们认识到实际销售的本田数量是这个系统部分和一个随机且不可预测的部分的总和和我们称之为随机错误。因此,代表本田雅阁销量的计量经济模型是

问d=F(磷,磷s,磷C,我ñC)+和
随机误差和解释了我们从这个简单模型中忽略的许多影响销售的因素,它也反映了经济活动的内在不确定性。

为了完成计量经济模型的规范,我们还必须谈谈我们的经济变量之间的代数关系的形式。例如,在您的第一门经济学课程中,需求量被描述为价格的线性函数。我们也将该假设扩展到其他变量,使需求关系的系统部分

F(磷,磷s,磷C,我ñC)=b1+b2磷+b3磷s+b4磷C+b5我ñC
对应的计量经济模型是

问d=b1+b2磷+b3磷s+b4磷C+b5我ñC+和
系数b1,b2,…,b5是我们使用经济数据和计量经济学技术估计的模型的未知参数。函数形式代表关于变量之间关系的假设 在任何特定问题中,一个挑战是确定与经济理论和数据兼容的函数形式。

在每一个计量经济学模型中,无论是需求方程、供给方程还是生产函数,都有系统部分和不可观察的随机分量。系统部分是我们从经济理论中得到的部分,包括关于函数形式的假设。随机分量代表一个“噪声”分量,它模糊了我们对变量之间关系的理解,我们使用随机变量 e 来表示。

我们使用计量经济学模型作为统计推断的基础。使用计量经济学模型和数据样本,我们对现实世界进行推断,并在此过程中学到一些东西。进行统计推断的方式包括以下几种:

  • 使用计量经济学方法估计经济参数,例如弹性
  • 预测经济成果,例如未来 10 年美国两年制大学的入学人数
  • 检验经济假设,例如报纸广告是否比商店展示更能增加销售额

计量经济学包括统计推断的所有这些方面。随着我们继续阅读本书,您将学习如何根据手头数据的特征正确地估计、预测和测试。

数学代写|计量经济学原理代写Principles of Econometrics代考|Causality and Prediction

在指定计量经济模型时经常出现的一个问题是,关系是否可以被视为既是因果关系又是预测性的,还是只能被视为预测性的。要了解差异,请考虑一个等式,其中学生在计量经济学中的成绩GR一个D和与被跳过的课堂讲课比例有关。

GR一个D和=b1+b2小号ķ我磷+和
我们期望b2是否定的:被跳过的讲座比例越大,成绩越低。但是,我们可以说逃课导致成绩降低吗?如果讲座是通过视频捕获的,则可以在其他时间观看。也许一个学生逃课是因为他或她的工作要求高,而要求高的工作没有足够的时间学习,这就是成绩差的根本原因。或者,可能是由于普遍缺乏承诺或动力而逃课,这就是成绩不佳的原因。在这种情况下,关于将 GRADE 与 SKIP 相关联的方程式,我们能说些什么呢?我们仍然可以称其为预测方程。GR一个D和和小号ķ我磷是(负)相关的,所以关于小号ķ我磷可用于帮助预测 GRADE。但是,我们不能称之为因果关系。逃课不会导致成绩低。参数b2没有传达逃课对成绩的直接因果影响。它还包括其他变量的影响,这些变量从方程中省略并与小号ķ我磷,例如学习时间或学生动机。
经济学家经常对可以解释为因果关系的参数感兴趣。本田想知道价格变化对其雅阁销售的直接影响。当牛肉行业的技术进步时,需求和供应的价格弹性对消费者和生产者福利的变化具有重要影响。我们的任务之一是查看哪些假设对于将计量经济学模型解释为因果关系是必要的,并评估这些假设是否成立。

预测关系很重要的一个领域是“大数据”的使用。计算机技术的进步导致了海量信息的存储。Internet 上的旅游网站会跟踪您一直在寻找的目的地。Google 根据您浏览过的网站向您投放广告。通过他们的会员卡,超市可以保存您的购买数据并识别与您相关的销售商品。数据分析师使用大数据来识别有助于预测我们行为的预测关系。

一般来说,我们对计量经济模型的规范和我们做出的假设有影响的数据类型。我们现在转向讨论不同类型的数据以及在哪里可以找到它们。

数学代写|计量经济学原理代写Principles of Econometrics代考|Experimental Data

获取有关经济关系的未知参数的信息的一种方法是进行或观察实验的结果。在物理科学和农业中,很容易想象受控实验。科学家指定关键控制变量的值,然后观察结果。我们可能会在类似的土地上种植特定品种的小麦,然后改变施用于每个土地的肥料和杀虫剂的量,在生长季节结束时观察每个土地上生产的蒲式耳小麦。重复实验ñ地块创造了一个样本ñ观察。这种受控实验在商业和社会科学中很少见。实验数据的一个关键方面是解释变量的值可以在实验的重复试验中固定为特定值。

一个商业例子来自市场研究。假设我们对超市中特定商品的每周销售额感兴趣。当一件物品被出售时,它会通过一个扫描单元来记录价格和出现在您的杂货账单上的金额。但与此同时,会创建数据记录,并且在每个时间点都知道商品的价格和所有竞争对手的价格,以及当前的商店展示和优惠券使用情况。价格和购物环境由商店管理层控制,因此可以使用相同的“控制”变量值重复此“实验”数天或数周。

社会科学中有一些计划实验的例子,但由于难以组织和资助这些实​​验,因此很少见。计划中的实验的一个显着例子是田纳西州的星计划。” 这项实验从 1985 年开始,到 1989 年结束,跟踪了从幼儿园到三年级的一组小学生。在实验中,孩子和老师在学校内被随机分配到三种类型的班级:13-17 名学生的小班、22-25 名学生的普通班和有专职助教的普通班。老师。目的是确定小班教学对学生学习的影响,通过学生在成绩测试中的分数来衡量。我们将在第 7 章分析数据,并证明小类显着提高了性能。这一发现将影响未来几年的公共教育政策。

数学代写|计量经济学原理代写Principles of Econometrics代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

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。

tatistics-lab作为专业的留学生服务机构,多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务,包括但不限于Essay代写,Assignment代写,Dissertation代写,Report代写,小组作业代写,Proposal代写,Paper代写,Presentation代写,计算机作业代写,论文修改和润色,网课代做,exam代考等等。写作范围涵盖高中,本科,研究生等海外留学全阶段,辐射金融,经济学,会计学,审计学,管理学等全球99%专业科目。写作团队既有专业英语母语作者,也有海外名校硕博留学生,每位写作老师都拥有过硬的语言能力,专业的学科背景和学术写作经验。我们承诺100%原创,100%专业,100%准时,100%满意。

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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