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

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。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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|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|The Econometric Model

F(磷,磷s,磷C,我ñC)=b1+b2磷+b3磷s+b4磷C+b5我ñC

• 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

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

GR一个D和=b1+b2小号ķ我磷+和

## 有限元方法代写

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## 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。