经济代写|计量经济学代写Econometrics代考|ECON2271

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• 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

经济代写|计量经济学代写Econometrics代考|Panel data

A panel data set consists of a time series for each cross-sectional member in the data set; as an example we could consider the sales and the number of employees for 50 firms over a five-year period. Panel data can also be collected on a geographical basis; for example, we might have GDP and money supply data for a set of 20 countries and for a 20 -year period.

Panel data are denoted by the use of both $i$ and $t$ subscripts, which we have used before for cross-sectional and time series data, respectively. This is simply because panel data have both cross-sectional and time series dimensions. So, we might denote GDP for a set of countries and for a specific time period as:
$$Y_{i t} \quad \text { for } t=1,2,3, \ldots, T \text { and } i=1,2,3, \ldots, N$$
To better understand the structure of panel data, consider a cross-sectional and a time series variable as $N \times 1$ and $T \times 1$ matrices, respectively:

$$Y_t^{\text {ARGENTINA }}=\left(\begin{array}{c} Y_{1990} \ Y_{1991} \ Y_{1992} \ \vdots \ Y_{2012} \end{array}\right), \quad Y_i^{1990}=\left(\begin{array}{c} Y_{\text {ARGENTINA }} \ Y_{\text {BRAZIL }} \ Y_{U R U G U A Y} \ \vdots \ Y_{\text {VENEZUELA }} \end{array}\right)$$
Here $Y_t^{A R G E N T I N A}$ is the GDP for Argentina from 1990 to 2012 and $Y_i^{1990}$ is the GDP for 20 different Latin American countries.

经济代写|计量经济学代写Econometrics代考|The classical linear regression model

The classical linear regression model is a way of examining the nature and form of the relationships between two or more variables. In this chapter we consider the case of only two variables. One important issue in the regression analysis is the direction of causation between the two variables; in other words, we want to know which variable is affecting the other. Alternatively, this can be stated as which variable depends on the other. Therefore, we refer to the variables as the dependent variable (usually denoted by $Y$ ) and the independent or explanatory variable (usually denoted by $X$ ). We want to explain/predict the value of $Y$ for different values of the explanatory variable $X$. Let us assume that $X$ and $Y$ are linked by a simple linear relationship:
$$E\left(Y_t\right)=a+\beta X_t$$
where $E\left(Y_t\right)$ denotes the average value of $Y_t$ for given $X_t$ and unknown population parameters $a$ and $\beta$ (the subscript $t$ indicates that we have time series data). Equation (3.1) is called the population regression equation. The actual value of $Y_t$ will not always equal its expected value $E\left(Y_t\right)$. There are various factors that can ‘disturb’ its actual behaviour and therefore we can write actual $Y_t$ as:
$$Y_t=E\left(Y_t\right)+u_t$$
or
$$Y_t=a+\beta X_t+u_t$$
where $u_t$ is a disturbance. There are several reasons why a disturbance exists:
1 Omission of explanatory variables. There might be other factors (other than $X_t$ ) affecting $Y_t$ that have been left out of Equation (3.2). This may be because we do not know these factors, or even if we know them we might be unable to measure them in order to use them in a regression analysis.
2 Aggregation of variables. In some cases it is desirable to avoid having too many variables and therefore we attempt to summarize in aggregate a number of relationships in only one variable. Therefore, eventually we have only a good approximation of $Y_t$, with discrepancies that are captured by the disturbance term.
3 Model specification. We might have a misspecified model in terms of its structure. For example, it might be that $Y_t$ is not affected by $X_t$, but it is affected by the value of $X$ in the previous period (that is, $X_{t-1}$ ). In this case, if $X_t$ and $X_{t-1}$ are closely related, the estimation of Equation (3.2) will lead to discrepancies that are again captured by the error term.
4 Functional misspecification. The relationship between $X$ and $Y$ might be non-linear. We shall deal with non-linearities in other chapters of this text.
5 Measurement errors. If the measurement of one or more variables is not correct then errors appear in the relationship and these contribute to the disturbance term.

计量经济学代考

经济代写|计量经济学代写Econometrics代考|Panel data

$Y_{i t} \quad$ for $t=1,2,3, \ldots, T$ and $i=1,2,3, \ldots, N$

$Y_t^{\text {ARGENTINA }}=\left(Y_{1990} Y_{1991} Y_{1992} \vdots Y_{2012}\right), \quad Y_i^{1990}=\left(Y_{\text {ARGENTINA }} Y_{\text {BRAZIL }} Y_{\text {URUGUAY }}\right.$

经济代写|计量经济学代写Econometrics代考|The classical linear regression model

$$E\left(Y_t\right)=a+\beta X_t$$

$$Y_t=E\left(Y_t\right)+u_t$$

$$Y_t=a+\beta X_t+u_t$$

1 解释变量的遗漏。可能还有其他因素 (除了 $X_t$ ) 影响 $Y_t$ 已被排除在等式 (3.2)之外。这可能是因为我 们不知道这些因素，或者即使我们知道它们也可能无法测量它们以便在回归分析中使用它们。
2 变量的聚合。在某些情况下，希望避免有太多变量，因此我们试图仅在一个变量中汇总总结许多关系。 因此，最终我们只有一个很好的近似值 $Y_t$ ，具有由扰动项捕获的差异。
3 型号说明。就其结构而言，我们可能有一个错误指定的模型。例如，它可能是 $Y_t$ 不受 $X_t$ ，但它受值的影 响 $X$ 在上一时期 (即 $X_{t-1}$ ). 在这种情况下，如果 $X_t$ 和 $X_{t-1}$ 密切相关，方程 (3.2) 的估计将导致误差项 再次捕获的差异。
4 功能性错误说明。之间的关系 $X$ 和 $Y$ 可能是非线性的。我们将在本文的其他章节中处理非线性问题。
5 测量误差。如果一个或多个变量的测量不正确，则关系中会出现错误，这些错误会导致干扰项。

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。