### 金融代写|金融计量经济学代写Financial Econometrics代考|ECOM90011

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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

## 金融代写|金融计量经济学Financial Econometrics代考|How to Test One Model Versus the Other

Testing one model versus the other model is vital in asset pricing studies. All of the available notable asset pricing models are not absolutely accurate. It is also true that if there is not enough data then any asset pricing model can be easily rejected by the standard tests. Every so often it is tacit that in the data which model is the better model than some other model? To do so numerous statistical inferences are used.

Let’s consider the Fama and French five factor model as shown in Eq. $5.8 .$
\begin{aligned} E\left(r_{i}\right)=& r_{f}+\beta_{i m} *\left(r_{m}-r_{f}\right)+\beta_{i s m b} *(\mathrm{SMB})+\beta_{i h m l} *(\mathrm{HML}) \ &+\beta_{i c m a} *(\mathrm{CMA})+\beta_{i r m w} *(\mathrm{RMW})+\varepsilon_{i} \end{aligned}

Can a factor be dropped from the above model?
For instance, consider the HML factor dropped from the above model. How to respond to such critical problems? Very often such critical problems are responded to or approached wrongly as conversed below.
Can a factor (HML) be dropped from the above model just by looking into the obtained time series regressions t-statistics or $p$-values of the factor coefficients?

The answer is no as the statistical inference certainly provides measure on whether the coefficients (Betas) are significant or not. In general, it does not provide any measures on whether the factor (HML) should be dropped or deleted from the model or not. Thus, the valid question here is whether dropping or deletion of the factor (HML) from the model significantly changes the $(\alpha)$ alpha values or not.

Can $R$-squared values provide adequate measure on whether a factor (HML) can be dropped from the model?

Similarly $R$-squared values provide measures on whether the model is a good model of variance. But it does not provide measures on whether dropping or deletion of the factor (HML) from the model noticeably reduced $R$-squared values as the model of mean.

Compare the model with the factor (HML) and without the factor (HML). Then check for if the combined ( $\alpha$ ) alpba values is equal to zero $\left(\alpha \sum^{-1} \alpha\right)$

This statistical test only tests if the model is true or not. But it does not test if one model is better than the other model. To examine it appropriately if one model is better than the other model, proper comparison of the Chi-square tests must be done. Accordingly check if the Chi-square value increases on dropping or deletion of the factor (HML) using the same residual covariance $(\Sigma)$ matrix.

## 金融代写|金融计量经济学Financial Econometrics代考|Panel Regression

In this section, panel regression is covered in detail. The panel regressions use longitudinal data. Thus the panel data consists of both the cross section and time series characteristics. As a result of which panel data has certain advantages over the time series and cross-sectional data as mentioned below.

• Panel data can deal with the heterogeneity issues related to the crosssectional units.
• Panel data are enhanced quality data.
• Panel data allow to examine more complex models
For instance, panel data can be used to examine the reason behind the country-wise variations in banks net profits (NP) due to non-interest (NI) and interest incomes (INTI) of the banks. The above problem can be mathematically represented as shown in the Eq. $5.15$.
$$\mathrm{NP}{i, t}=\alpha+\beta{1} \mathrm{NI}{i, t}+\beta{2} \mathrm{NTI}{n, t}+\varepsilon{i, t}$$
where
$$I=1,2,3, \ldots, N \text { and } t=1,2,3, \ldots, T$$
In the above Eq. $5.15$, the subscript $i$ captures the cross-sectional aspects whereas $t$ captures the time series aspects of the data. Next task is to choose the correct panel data model.
Panel Data Models
There are three different types of the panel data models namely:
• Constant coefficients model (CCM)
• Fixed effects model
• Random effects model
The above three models differ in terms of the assumptions made for the intercept, slope coefficients, and error terms of the panel data model. All of these three different types of the panel data models are discussed below in detail.

## 金融代写|金融计量经济学Financial Econometrics代考|Finance in Action

To examine the CAPM and Fama-French three factor models in Indian context. Based on the obtained regression estimates comment on these two models.
Data Description:
BSE 500 companies are used to construct the study portfolios based on the size and value factors by means of the Fama-French (1993) technique. For risk free rate 91 days T-bills returns are used. BSE-200 index mean excess returns used as proxy for the market. The study period is between July 2000 and June 2014. All data points are in monthly frequency.
Regression models used are as follows:

CAPM or Fama-French Three Factor Model
$$E\left(r_{i}\right)-r_{f}=\alpha_{i}+\beta_{i m} *\left(r_{m}-r_{f}\right)+\varepsilon_{i}$$
$E\left(r_{i}\right)-r_{f}=\alpha_{i}+\beta_{i m} *\left(r_{m}-r_{f}\right)+\beta_{i s} *(\mathrm{Sizc})+\beta_{i v} *($ Valuc $)+\varepsilon_{i}$
EViews stepwise implementations
Above regression models can be executed casily in EViews by following the below steps.
Step 1: Import data into the EViews Workfile window.
Step 2: Then click on Quick $\rightarrow$ Estimate Equation $\rightarrow$ The Equation Estimation window will appear as shown in Fig. 5.5.

## 金融代写|金融计量经济学Financial Econometrics代考|Panel Regression

• 面板数据可以处理与横截面单位相关的异质性问题。
• 面板数据是增强的质量数据。
• 面板数据允许检查更复杂的模型
例如，面板数据可用于检查由于银行的非利息 (NI) 和利息收入 (INTI) 而导致的银行净利润 (NP) 的国家/地区差异背后的原因. 上述问题可以用数学表示，如等式所示。5.15.
ñ磷一世,吨=一个+b1ñ我一世,吨+b2ñ吨我n,吨+e一世,吨
在哪里
我=1,2,3,…,ñ 和 吨=1,2,3,…,吨
在上面的方程式中。5.15, 下标一世捕获横截面方面，而吨捕获数据的时间序列方面。下一个任务是选择正确的面板数据模型。
面板数据模型
有三种不同类型的面板数据模型，即：
• 常系数模型 (CCM)
• 固定效应模型
• 随机效应模型
上述三个模型在面板数据模型的截距、斜率系数和误差项的假设方面有所不同。下面将详细讨论所有这三种不同类型的面板数据模型。

## 金融代写|金融计量经济学Financial Econometrics代考|Finance in Action

BSE 500 公司用于通过 Fama-French (1993) 技术根据规模和价值因素构建研究组合。对于无风险利率，使用 91 天国库券收益。BSE-200 指数表示用作市场代表的超额收益。研究期间为 2000 年 7 月至 2014 年 6 月。所有数据点均按月计算。

CAPM 或 Fama-French 三因素模型

EViews 逐步实现

## 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。