### 经济代写|产业经济学代写Industrial Economics代考|ECF5040

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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|产业经济学代写Industrial Economics代考|Analysis of the Industrial Economics Prosperity in 2016

To get an intuitive understanding of the industrial economics development, this report has synthesized the available original data including industrial economics development to derive a composite indicator that reflects the situation of industrial economics, i.e. the composite index. According to the composite index, the industrial economics grew significantly in the first quarter of 2016 , unlike the trend that remained lower last year. According to the prosperity index, the first quarter of 2016 witnessed a significant rise in prosperity index of industrial economics as compared to the previous year, but the lagging index indicates that this high trend seems feeble and the leading index indicates that this trend will continue.
(1) Formation of the composite index
Processing of original data: the composite index needs to eliminate the “redundant” information (or information irrelative to our purpose) from the original data. To eliminate this “redundant” information, it can be differentiated from original data. Firstly, the original data include high-frequency and low-frequency data, and the former contains daily, weekly and monthly data. What is needed in this report is the monthly data, so daily and weekly data are “redundant” to us and all data are necessarily subject to de-frequency processing. Secondly, the original data contain output data and value quantity data, and the value quantity data may lead to incomparability inside data sequence under the influence of changing prices; therefore, the incomparable data caused by changing prices need to be removed in order to accurately describe the trend of industrial economics development. Thirdly, it is the influence of movable holiday effect. Unlike the statistical data that are calculated in accordance with the solar calendar, the traditional Chinese Spring is always celebrated in accordance with the lunar calendar, so the Spring Festival usually takes place in different solar months. As a public holiday in China, the Spring Festival has strong holiday effects: suspended production, additional leisure time, sharp rise in consumption, and abnormality in all economic activities. As a result, some “redundant” information such as “holiday effect” is included in monthly data. When the data are relevant to growth rate, the above three kinds of information are redundant information that needs to be removed in this report. Thanks to coincidence between seasonal information and holiday information, however, the movable holiday information is usually eliminated earlier than is the seasonable information. In this report, the three kinds of redundant information will be eliminated by de-frequency adjustment, price adjustment and movable holiday adjustment methods.

## 经济代写|产业经济学代写Industrial Economics代考|Correlation analysis is a statistical

Correlation analysis is a statistical method commonly used in research on closeness among various variables and in research on the degree of correlation between two or more variables and the mutual relation of phenomena with certain functions. The correlative relation means the stochastic relation of change in two phenomena values that are not completely determined, or a kind of dependence relation that is not yet completely determined, often abbreviated as correlative relation, which is the object of study in correlation analysis. The closeness of correlative relation describes the degree of association among variables through calculation of correlation coefficient, i.e. the correlation coefficient is the statistical magnitude that describes the degree and direction of linear relation between two variables, usually expressed as $r$, without unit, value of which ranges between $-1$ and $+1$. The closer to $r$ the absolute value is, the greater the degree of linear correlation between two variables will be. If $r$ is greater than 0 , it is a positive correlation and variable $\mathrm{Y}$ will increase as variable $\mathrm{X}$ increases; if $\mathrm{r}$ is less than 0 , it is a negative correlation and variable $\mathrm{Y}$ will decrease as variable $\mathrm{x}$ increases.
Based on the principle of correlation coefficient, the cross correlation coefficient method has broken the sequence of two variables’ correlation coefficient arranged by time limit according to time variable. This sequence may give the mutual relation between two variables at different times; accordingly, the maximum cross correlation coefficient is used to determine whether this index is a leading index, concordance index or a lagging index.

Synthesis of index: the composite index may be coded in different ways, e.g. the composite index method of the US Department of Commerce, the composite index method introduced by the Economic Planning Agency of Japan, and the composite index method of the UN Organization for Economic Cooperation and Development (OECD). The Japanese Economic Planning Agency’s composite index method agrees with the US Department of Commerce in basic idea but differs slightly in method while the OECD’s composite index method is developed specific to the leading composite index and seems simpler than the former two methods. In this report, the US Department of Commerce’s composite index method is adopted as an internationally common method. It is basically used in the literature of Chinese development of prosperity index.

## 经济代写|产业经济学代写Industrial Economics代考|Determine and standardize the symmetrical change rate of index

Step 1: Determine and standardize the symmetrical change rate of index
(i) Let index $Y_{i j t}$ be the value of $j$ index in $i$ index group at $t$ time, where $i=1,2,3$, representing the leading, concordance and lagging index groups respectively, $j=1,2,3, \ldots, k_{i}$, representing indexes in three groups, and $k_{i}$ means the number of indexes in $i$ index group. First determine symmetrical change $C_{i j i t}$ of $Y_{i j t}$, where $t=2,3, \ldots, n$.

(ii) To prevent greatly variable indexes from producing significant impact on composite index, the symmetrical change rate $C_{i j t}$ of each index is standardized to make its average absolute value equal to 1 . First determine the normalized factor $A_{i j}$ and then standardize $C_{i j t}$ with $A_{i j}$ to obtain standardized change rate $S_{i j t}$, where $t=2,3, \ldots, n$.
Step 2: Determine standardized average change rate of each index group
(i) Determine average change rates of the leading index, concordance index and lagging index group, with $R_{i, t}$ of $i=1,2,3$ and $t=2,3$, $\ldots, n$.

Where $W_{i j}$ is the weight of $j$ index in $i$ index group. The equal weight is usually used to set weight in composite index. The scoring system may be used to determine weight so that each index is given a score according to its economic importance, statistical adequacy, historical concordance and publishing timeliness, and then each index is weighted. If this step is not followed, the weight will be not as desirable as equal weight due to its strong arbitrariness and subjectivity. In order to keep consistent the numerical values of composite index in three index group, the standardized average change rates of all three index groups need to be calculated by dividing the average change rates of all index groups by normalized factor among index groups.
(ii) Work out the normalized factor $F_{i}$, where $i=1,2,3$.
(iii) Figure out the standardized average change rate $V_{i, t}$, where $t=2,3$, $\ldots, n$.
Step 3: Calculate composite index
(i) Let $I_{i}(1)=100$, then $i=1,2,3$ and $t=2,3, \ldots, n$.
(ii) Synthesize a composite index with 100 as benchmark year, where $I_{i}$ is the average value of $I_{i, t}$ in the benchmark year.

Follow above step in aggregating and synthesizing indexes of three index groups, and then figure out the composite index of the leading index, concordance index and lagging index.

（一）综合指数的形成

## 经济代写|产业经济学代写Industrial Economics代考|Determine and standardize the symmetrical change rate of index

(i) 让指数是一世j吨成为j索引一世索引组在吨时间，地点一世=1,2,3，分别代表领先、一致和落后指数组，j=1,2,3,…,ķ一世，代表三组中的索引，以及ķ一世表示索引的数量一世指数组。首先确定对称变化C一世j一世吨的是一世j吨， 在哪里吨=2,3,…,n.

(ii) 为防止大变动指数对综合指数产生重大影响，对称变化率C一世j吨对每个指标进行标准化，使其平均绝对值等于 1 。首先确定归一化因子一个一世j然后标准化C一世j吨和一个一世j获得标准化的变化率小号一世j吨， 在哪里吨=2,3,…,n.

(i) 确定领先指数、一致性指数和滞后指数组的平均变化率，用R一世,吨的一世=1,2,3和吨=2,3, …,n.

(ii) 计算出归一化因子F一世， 在哪里一世=1,2,3.
(iii) 计算出标准化的平均变化率在一世,吨， 在哪里吨=2,3, …,n.

(i) 让我一世(1)=100， 然后一世=1,2,3和吨=2,3,…,n.
(ii) 以 100 为基准年合成一个综合指数，其中我一世是平均值我一世,吨在基准年。

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