金融代写|金融实证代写Financial Empirical 代考|Fl4003

statistics-lab™ 为您的留学生涯保驾护航 在代写金融实证Financial Empirical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。我们的专家在代写金融实证Financial Empirical股权市场金融实证Financial Empirical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

金融代写|金融实证代写Financial Empirical 代考|Representation of Solutions

As we already noted in the introduction the solutions $\hat{x}1$ and $\hat{x}_1$ for trend and season are natural polynomial and trigonometric spline functions. For each point in time with an observation $t_k$ polynomial and trigonometric function are changed appropriately by the additional functions which are “cut” there \begin{aligned} &g_1\left(t-t_k\right)=\left(t-t_k\right)^{2 p-1} \ &g_2\left(t-t_k\right)=\sum{j=1}^q a_j\left(b_j \sin \omega_j\left(t-t_k\right)-\left(t-t_k\right) \cos \omega_j\left(t-t_k\right)\right) \end{aligned}
für $t>t_k$ und 0 für $t \leq t_k, k=1, \ldots, n$, mit
$$a_j=\frac{1}{2 \omega_j^2 \prod_{\substack{i=1 \ i \neq j}}^q\left(\omega_i^2-\omega_j^2\right)^2}, \quad b_j=\frac{1}{\omega_j}-4 \omega_j \sum_{\substack{i=1 \ i \neq j}}^q \frac{1}{\omega_i^2-\omega_j^2}, \quad j=1, \ldots, q .$$
To find a solution also the weight function $w_{1 k}$ and $w_{2 k}$ of the representation theorem are chosen as natural polynomial and trigonometric spline functions. Written as vectors and matrices with
$\mathbf{f}_1(t)^{\prime}=\left(\begin{array}{llll}1 & \ldots & t^{p-1}\end{array}\right) \quad \mathbf{g}_1(t)^{\prime}=\left(g_1\left(t-t_1\right) \cdots g_1\left(t-t_n\right)\right)$
$F_1=\left(\begin{array}{cccc}1 & t_1 & \cdots & t_1^{p-1} \ \vdots & \vdots & & \vdots \ 1 & t_n & \ldots & t_n^{p-1}\end{array}\right) \quad G_1=\left(\begin{array}{ccc}g_1\left(t_1-t_1\right) & \cdots & g_1\left(t_1-t_n\right) \ \vdots & & \vdots \ g_1\left(t_n-t_1\right) & \cdots & g_1\left(t_n-t_n\right)\end{array}\right)$

and
$\mathbf{f}_2(t)^{\prime}=\left(\begin{array}{lll}\cos \omega_1 t & \sin \omega_1 t \ldots \cos \omega_q t \sin \omega_q t\end{array}\right)$
$\mathbf{g}_2(t)^{\prime}=\left(g_2\left(t-t_1\right) \ldots g_2\left(t-t_n\right)\right)$
$G_2=\left(\begin{array}{ccc}g_2\left(t_1-t_1\right) & \ldots & g_2\left(t_1-t_n\right) \ \vdots & & \vdots \ g_2\left(t_n-t_1\right) & \ldots & g_2\left(t_n-t_n\right)\end{array}\right) .$
The following representations hold (with real-valued coefficient matrices)

金融代写|金融实证代写Financial Empirical 代考|Values of Smoothness of Solutions

The solutions $\hat{x}_1(t)=\mathbf{w}_1(t)^{\prime} \mathbf{y}, \hat{x}_2(t)=\mathbf{w}_2(t)^{\prime} \mathbf{y}$ have smoothness values (cf. measurement of smoothness of weight functions)
\begin{aligned} &Q_1\left(\hat{x}_1\right)=\int_a^b\left|T_1 \hat{x}_1(t)\right|^2 \mathrm{~d} t=\frac{1}{\lambda_1^2} \mathbf{y}^{\prime} A^{\prime} G_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \mathbf{y}^{\prime} W_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \hat{\mathbf{x}}_1^{\prime} \mathbf{u} \geq 0, \ &Q_2\left(\hat{x}_2\right)=\int_a^b\left|T_2 \hat{x}_2(t)\right|^2 \mathrm{~d} t=\frac{1}{\lambda_2^2} \mathbf{y}^{\prime} A^{\prime} G_2^{\prime} A \mathbf{y}=\frac{1}{\lambda_2} \mathbf{y}^{\prime} W_2^{\prime} A \mathbf{y}=\frac{1}{\lambda_2} \hat{\mathbf{x}}_2^{\prime} \hat{\mathbf{u}} \geq 0 . \end{aligned}
If follows that estimations of components in observation points are always nonnegative correlated with the empirical rests $\hat{\mathbf{u}}=\mathbf{y}-\hat{\mathbf{x}}$. Furthermore holds
\begin{aligned} \lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right) &=\mathbf{y}^{\prime} W^{\prime} A \mathbf{y}=\hat{\mathbf{x}}^{\prime} \hat{\mathbf{u}} \geq 0, \quad W=W_1+W_2, \hat{\mathbf{x}}=\hat{\mathbf{x}}_1+\hat{\mathbf{x}}_2 \ Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right) &=\left|\mathbf{y}-\hat{\mathbf{x}}_1^{\prime}-\hat{\mathbf{x}}_2\right|^2=|\mathbf{y}-\hat{\mathbf{x}}|^2=|\hat{\mathbf{u}}|^2=\hat{\mathbf{u}}^{\prime} \hat{\mathbf{u}}=\mathbf{y}^{\prime} A^{\prime} A \mathbf{y} \geq 0 \end{aligned}
and therefore for the minimum
\begin{aligned} S\left(\hat{x}_1, \hat{x}_2 ; \mathbf{y}\right) &=\lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right)+Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right)=\hat{\mathbf{x}}^{\prime} \mathbf{u}+\hat{\mathbf{u}}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} A \mathbf{y} \ &=\mathbf{y}^{\prime} \mathbf{y}-\mathbf{y}^{\prime} W \mathbf{y} \leq \mathbf{y}^{\prime} \mathbf{y} \end{aligned}

金融代写|金融实证代写Financial Empirical 代考|Representation of Solutions

$\mathbf{f}_1(t)^{\prime}=\left(\begin{array}{lll}1 & \ldots & t^{p-1}\end{array}\right) \quad \mathbf{g}_1(t)^{\prime}=\left(g_1\left(t-t_1\right) \cdots g_1\left(t-t_n\right)\right)$

$\mathbf{f}_2(t)^{\prime}=\left(\cos \omega_1 t \quad \sin \omega_1 t \ldots \cos \omega_q t \sin \omega_q t\right)$
$\mathbf{g}_2(t)^{\prime}=\left(g_2\left(t-t_1\right) \ldots g_2\left(t-t_n\right)\right)$

金融代写|金融实证代写Financial Empirical 代考|Values of Smoothness of Solutions

$$Q_1\left(\hat{x}_1\right)=\int_a^b\left|T_1 \hat{x}_1(t)\right|^2 \mathrm{~d} t=\frac{1}{\lambda_1^2} \mathbf{y}^{\prime} A^{\prime} G_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \mathbf{y}^{\prime} W_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \hat{\mathbf{x}}_1^{\prime} \mathbf{u} \geq 0, \quad Q_2\left(\hat{x}_2\right)=\int_a^b \mid T_2$$

$$\lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right)=\mathbf{y}^{\prime} W^{\prime} A \mathbf{y}=\hat{\mathbf{x}}^{\prime} \hat{\mathbf{u}} \geq 0, \quad W=W_1+W_2, \hat{\mathbf{x}}=\hat{\mathbf{x}}_1+\hat{\mathbf{x}}_2 Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right) \quad=1$$

$$S\left(\hat{x}_1, \hat{x}_2 ; \mathbf{y}\right)=\lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right)+Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right)=\hat{\mathbf{x}}^{\prime} \mathbf{u}+\hat{\mathbf{u}}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} A \mathbf{y} \quad=\mathbf{y}^{\prime} \mathbf{y}-\mathbf{y}^{\prime}$$

广义线性模型代考

statistics-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。

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。

金融代写|金融实证代写Financial Empirical 代考|CMSE11509

statistics-lab™ 为您的留学生涯保驾护航 在代写金融实证Financial Empirical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。我们的专家在代写金融实证Financial Empirical股权市场金融实证Financial Empirical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

金融代写|金融实证代写Financial Empirical 代考|Base Model

Based on the above discussion a time series $x(t)$ with possibly continuous time index $t$ in an interval $[a, b]$ will be analysed and additively decomposed in the unobservable important and interpretable components trend (and economic cycle) $x_1(t)$ and season (and calendar) $x_2(t)$. The rest $u(t)$ contains the unimportant, irregular unobservable parts, maybe containing additive outliers.

An “ideal” trend $\tilde{x}1(t)$ is represented by a polynomial of given degree $p-1$ and an “ideal” season $\tilde{x}_2(t)$ is represented by a linear combination of trigonometric functions of chosen frequencies (found by exploration) $\omega_j=2 \pi / S_j$ with $S_j=$ $S / n_j$ and $n_j \in \mathbb{N}$ for $j=1, \ldots, q$. Here $S$ is the known base period and $S_j$ leads to selected harmonics, which can be defined by Fourier analysis. Therefore holds $$\tilde{x}_1(t)=\sum{j=0}^{p-1} a_j t^j \quad \text { and } \quad \tilde{x}2(t)=\sum{j=1}^q\left(b_{1 j} \cos \omega_j t+b_{2 j} \sin \omega_j t\right), \quad t \in[a, b] .$$
In applications the components $x_1(t)$ and $x_2(t)$ won’t exist in ideal representation. They will be additively superimposed by random disturbances $u_1(t)$ and $u_2(t)$. Only at some points in time $t_1, \ldots, t_n$ in the time interval $[a, b]$ the sum $x(t)$ of components is observable, maybe flawed by further additive errors $\varepsilon_1, \ldots, \varepsilon_n$. The respective measurements are called $y_1, \ldots, y_n$.
Now we have following base model
$x_1(t)=\ddot{x}_1(t)+u_1(t)$
$x_2(t)=\tilde{x}_2(t)+u_2(t) \quad t \in[a, b] \quad$ state equation
$y_k=x_1\left(t_k\right)+x_2\left(t_k\right)+\varepsilon_k, \quad k=1, \ldots, n \quad$ observation equation,
cf. Fig. 1 .

金融代写|金融实证代写Financial Empirical 代考|Construction of the Estimation Principle

For evaluation of smoothness (in contrast to flexibility) the following smoothness measures are constructed (actually these are roughness measures).

By differentiation $\mathrm{D}=\frac{\mathrm{d}}{\mathrm{d} t}$ the degree of a polynomial is reduced by 1 . Therefore for a trend $x_1(t)$ as polynomial of degree $p-1$ always holds $\mathrm{D}^p x_1(t)=0$. On the

other hand, every function $x_1(t)$ with this feature is a polynomial of degree $p-1$. Therefore
$$Q_1\left(x_1\right)=\int_a^b\left|\mathrm{D}^p x_1(t)\right|^2 \mathrm{~d} t \quad \text { measure of smoothness of trend }$$
is a measure of the smoothness of an appropriately chosen function $x_1$.
For any sufficiently often differentiable and quadratically integrable function $x_1$ in interval $[a, b] Q_1\left(x_1\right)$ is zero iff $x_1$ is there a polynomial of degree $p-1$, i.e. $x_1(t)=\sum_{j=0}^{p-1} a_j t^j$, a smoothest (ideal) trend. The larger the value of $Q_1$ for a function $x_1$ in $[a, b]$ the larger is the deviation of $x_1$ from a (ideal) trend polynomial of degree $p-1$.

Two times differentiation of the functions $\cos \omega_j t$ and $\sin \omega_j t$ gives $-\omega_j^2 \cos \omega_j t$ and $-\omega_j^2 \sin \omega_j t$ such that $\prod_{j=1}^q\left(\mathrm{D}^2+\omega_j^2 \mathrm{I}\right)$ (I: identity) nullifies any linear combination $x_2(t)$ of all functions $\cos \omega_j t$ and $\sin \omega_j t, j=1, \ldots, q$. That is because the following
\begin{aligned} &\left(\mathrm{D}^2+\omega_j^2 \mathrm{I}\right)\left(b_{1 k} \cos \omega_k t+b_{2 k} \sin \omega_k t\right)= \ &=b_{1 k}\left(\omega_j^2-\omega_k^2\right) \cos \omega_k t+b_{2 k}\left(\omega_j^2-\omega_k^2\right) \sin \omega_k t \quad \text { for } \quad j, k=1, \ldots, q, \end{aligned}
nullifies for the case $j=k$ the respective oscillation. This also proves the exchangeability of the operators $\mathrm{D}^2+\omega_j^2 \mathrm{I}, j=1, \ldots, q$.

If inversely $\prod_{j=1}^q\left(\mathrm{D}^2+\omega_j^2 \mathrm{I}\right) x_2(t)=0$ holds, the function $x_2(t)$ is a linear combination of the trigonometric functions under investigation. Consequently
$Q_2\left(x_2\right)=\int_a^b\left|\prod_{j=1}^q\left(\mathrm{D}^2+\omega_j^2 \mathrm{I}\right) x_2(t)\right|^2 \mathrm{~d} t \quad$ measure of seasonal smoothness is a measure for seasonal smoothness of the chosen function $x_2$.

金融代写|金融实证代写Financial Empirical 代考|Base Model

“理想”的趋势 $\tilde{x} 1(t)$ 由给定次数的多项式表示 $p-1$ 和一个“理想”的李节 $\tilde{x}2(t)$ 由选定频率的三角函数的线性组合表 示 (通过探索发现) $\omega_j=2 \pi / S_j$ 和 $S_j=S / n_j$ 和 $n_j \in \mathbb{N}$ 为了 $j=1, \ldots, q$. 这里 $S$ 是已知的基期和 $S_j$ 导致选 定的谐波，可以通过傅里叶分析来定义。因此成立 $$\tilde{x}_1(t)=\sum j=0^{p-1} a_j t^j \quad \text { and } \quad \tilde{x} 2(t)=\sum j=1^q\left(b{1 j} \cos \omega_j t+b_{2 j} \sin \omega_j t\right), \quad t \in[a, b] .$$

$x_1(t)=\ddot{x}_1(t)+u_1(t)$
$x_2(t)=\tilde{x}_2(t)+u_2(t) \quad t \in[a, b] \quad$ 状态方程
$y_k=x_1\left(t_k\right)+x_2\left(t_k\right)+\varepsilon_k, \quad k=1, \ldots, n$ 观察方程，

金融代写|金融实证代写Financial Empirical 代考|Construction of the Estimation Principle

$$Q_1\left(x_1\right)=\int_a^b\left|D^p x_1(t)\right|^2 \mathrm{~d} t \quad \text { measure of smoothness of trend }$$
is a measure of the smoothness of an appropriately chosen function $x_1$.

$$\left(\mathrm{D}^2+\omega_j^2 \mathrm{I}\right)\left(b_{1 k} \cos \omega_k t+b_{2 k} \sin \omega_k t\right)=\quad=b_{1 k}\left(\omega_j^2-\omega_k^2\right) \cos \omega_k t+b_{2 k}\left(\omega_j^2-\omega_k^2\right) \sin \omega_k t$$

广义线性模型代考

statistics-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。

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。

金融代写|金融实证代写Financial Empirical 代考|FIN826

statistics-lab™ 为您的留学生涯保驾护航 在代写金融实证Financial Empirical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。我们的专家在代写金融实证Financial Empirical股权市场金融实证Financial Empirical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

金融代写|金融实证代写Financial Empirical 代考|Components of an Econometric Time Series

Trend Trend is explained by effects which change only slowly and continuously. Examples for such a long-term effect are slow changes in population or improvements in productivity.

Economic Cycle Economic cycle names the medium-term up and down movement of economic life. The phenomenons which are described by the whole multitude of the theory of the economic cycle show themselves as multi-year, not repeating fluctuations around the trend figure. The periods for these fluctuations are between 2 and 12 years, mostly $5-7$ years.

Season All (nearly) regular periodic fluctuations with periods below 1 year (one base period) are called seasonal effects or season. The cause of these seasonal effects is mostly natural or institutional influences which unfold cyclically. Most important is the earth moving around the sun in $365.25$ days. As is well known this period shows up in all kinds of natural variables like temperature, daily sunshine duration, rainfall, etc. Equally well known is the $24 \mathrm{~h}$ day-night sequence showing up in a lot of mostly ecological time series. More seldom the moon cycle shows up in data, i.e. the tides. Institutional causes contain regular dates, e.g. quarterly, tax or interest dates.

Calender Effects There are effects caused by the structure of the used calendar. Months have different lengths, the number of working days changes from month to month, holidays, etc. Sometimes a correction for these effects is possible. A simple factor may be enough to correct for different month lengths or number of working days. Nowadays these corrections are harder to perform, because working weekends or clerical holidays is much more common.

Rest The rest component subsumes all irregular movements, which are caused by inexplicable causes and do not work constantly in one direction. Most important are short-lived, unexpected influences and variances like special weather conditions, errors in the data collection processes, measurement errors and/or erroneous reactions.

金融代写|金融实证代写Financial Empirical 代考|Components of the Decomposition Model

What is described in the following for economic time series is easily transferred into other domains by changing the natural base period length of a “season”. The length of the base period helps to distinguish between trend (long-term) and (economic) cycle (medium-term).

We have to note that trend does not necessarily mean trend line. This often leads to discussions after analysis. Therefore it is strongly recommended to discuss these notions beforehand.

VBV assumes an additive composition of preferable three components in the variable under investigation. If the composition is multiplicative, the logarithm must be applied first.

Often there are arguments against a strict distinction between trend and economic cycle. Such a distinction would only seem appropriate, if there were different sets of variables influencing trend and cycle. That is normally not the case. Therefore these two components, the long-term and the medium-term economic variations, are consolidated into one smooth component. In this paper the term smooth component is used in the context of smoothing a time series and is therefore reserved for a combined component of trend and season. Trend and cycle are one component in the following description. That component may contain singular innovative outliers. Splitting the component further would easily possible, cf. Michel (2008). Note that level changes remain in this component. So we call that combined variable in the following trend and it contains the mid-term and long-term course of a time series.

金融代写|金融实证代写Financial Empirical 代考|Components of the Decomposition Model

VBV 假定在所研究的变量中具有优选的三种成分的添加剂组合物。如果组合是乘法，则必须先应用对数。

广义线性模型代考

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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。