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好 现在我们进入第五讲
Now let's start Lecture 5
时间数列的构成因素分析
compositions of time series
那我们观察了很多很多的
We’ve observed a lot of
这个时间数列
time series
那我们看到
and we see that
在分析较长时间变化周期中这个
in analyzing the changing process
经济指标变化过程中
of economic indicators with a long cycle
它的这个经济指标
why do the economic indicators
为什么会呈现这种变化过程
present such a changing process
让我们考虑一下
Let's think about it
其实在分析的过程中
In the analysis, in fact
我们其实能对这个经济指标
many factors can be identified
能分析出很多很多因素
from the economic indicators
比如说因素A
such as factor A
对这个经济指标影响是什么呢
Its effect on economic indicators
上下波动
is fluctuation
因素B的话可能是一个平缓作用
Factor B may play a moderating role
而因素C的话可能又是一个
while factor C may show the effect
往下的一个趋势的影响
of a downward trend
所以在分析经济指标的过程中
So, in the analysis of economic indicators
我们要把影响的因素把它抽离出来
the influencing factors should be extracted
这就是我们这一讲中所要关注的内容
These are the main content of this lecture
那在很多这个分析过程中
When analyzing
我们实际上
we actually
把这些因素作了一个划分别类
have these factors categorized
那我们说 有这样四大类的这个因素
There are four types of factors
第一类因素叫长期趋势
The first type is called secular trend
也就是说时间数列它随着这个
which is a long-term trend
时间的变化而逐渐增加或减少
of the time series that gradually increases
这个长期变化的趋势
or decreases with time
第二种因素叫季节变动
The second factor is seasonal variation
也就是说时间数列在一年中
which is a time series that
或者在固定时间内呈现出非常明显的
shows obvious regular changes
这个固定规则的变动
over a year or a period of time
它是由时间 季节变动引起的
It is caused by changes in time or seasons
第三类因素叫循环变动因素
The third type is cyclical variation
那我们说 指的是时间数列
The time series is based on
在若干年为一个周期
a period of several years
做一个循环波动这样一个变动
and shows cyclical fluctuations
第四种因素叫随机变动因素
The fourth factor is called random variation
那我们一般指的就是这种不规则变动
or it is generally referred to as erratic variation
也就是说经济社会发展过程中
That is, some uncontrollable and unpredictable factors
有一些你无法控制
in economic and social development
没有办法预测的一些因素
have an impact on the time series
对经济指标的时间数列产生影响
of economic indicators
那这四个因素的话
These 4 factors
我们会用四个不同的字符来进行代替
are represented by 4 letters
长期趋势我们一般用T来进行代替
Generally, secular trend is represented by T
积极变动我们一般用S来进行代替
seasonal variation is represented by S
循环变动我们一般用C 字符C
cyclical variation is represented
来进行代替
by C
而随机变动我们应该用I来进行代替
and random variation is represented by I
那我们看到这个时候的话
Here is a graph
我们有一个图形
that reflects the changes
搜集到的是我国从一定时间长度的
in real estate investment in China
这个房地产投资额的一个变化情况
over a certain period of time
那我们看到 在这个图形中
In this graph, we see that
它非常鲜明的有一个
there is a very clear, upward
向上的一个这个长期趋势 非常明显
secular trend, which is very obvious
那这个实际上同学们
After finishing this lecture
你们回去思考一下
you may think about
在你们现实生活中
if there are any
是不是也有一些长期趋势
secular trends in real life
你想想看
For example
你的大一的每月的生活费
Has your living expenditure
和大二的和现在进行比较的话
gradually increased
是不是逐渐在增长的过程
during first three years of college
那对于长期趋势来讲的话
There is also
还有比较显著的 我们一般提到说
a more significant secular trend
经济社会的发展的话
an indicator for economic
我们说经济的这个水平
and social development
一般从一定程度上来讲的话
in general, to a certain extent
或者说从一定长度
or a certain length
这个时间维度来看的话
in the dimension of time
它的长期趋势都是一个增长的趋势
its secular trend is an increasing trend
那还有一个指标
There is another indicator
也是非常显著的这个
which is also a very significant
明显的这个长期趋势
secular trend
我们大家思考一下看 哪个指标
Please think about which index is it
在前面这个时间数列表中
In fact, it was also mentioned
其实也提到了
in the previous table of time series
对了 我们说这个人口指标
Yes, it is the population
其实也是一个显著的 什么呀
which is a significant index
我们国家的人口指标
Our country's population index
它是显著的一个增长趋势
is also a significant secular trend
对不对
right
所以实际上在这个时间数列中
So, in fact
我们说长期趋势它其实是一个
the effect of secular trend
影响作用特别大
is particularly great
或者说影响比重对于经济指标
or it is a factor that has a greater
以及发展过程中
impact on economic indicators
影响比重特别大的一个因素
and the development processes
那第二个因素
The second factor
就是我们刚刚所提到的这个季节趋势
is seasonal variation just mentioned
那我们这里我们看到
We can see
这里有两个图都来反映的
from these two graphs
是这个季节变动
that reflect the seasonal variation
那我们这一个用的是90平米以下
We use the data of housing investment
这个住房投资的一个变化情况
for houses below 90 square meters
我们发现使用了两年的这个
We find that the seasonal
季节数据的话
and monthly data of two years
发现 月份数据的话
are used here
都体现出了它这个季节的变动
which reflect the seasonal changes
那实际上我们看到
In fact, we can see that
这两年的数据它都是一个什么
the data of the two years
在3 4月份的话是一个高峰
showed a peak during March and April
然后到了第三季度和第四季度
Then, in the third and fourth quarter
逐渐往下 下滑的一个趋势
it gradually declined, showing a downward trend
那实际上我们看到这两个图的话
The general trends demonstrated
它的这个大致趋势是一样
in the two graphs are similar
但是不是完全雷同
but not exactly the same
那其实也告诉我们说
It tells us that
季节变动它可能在某年
seasonal variation may be roughly the same
和另外一年之间 可能趋势大致一样
between two years
但不可能这么完全是一致的
but not exactly the same
但它也体现出一定的这个
But the trend of seasonal variation
季节变动的一个趋势
is reflected to a certain degree
那我们看到第三个因素是什么
The third factor is the cyclical variation
循环变动 这个循环变动的数据
the data of which
就非常难取得了
is very difficult to obtain
因为 在经济社会中
Because, in economic life
循环变动找了非常多数据指标
we have researched a lot of indicators
最后我们选择了这个猪肉价格的指标
Finally, we selected the indicator of pork price
在这个国家统计局的这个50个商品
We have collected data of 50 commodities
50个食品的这个搜集过程中
or 50 foods from the database of National Bureau of Statistics
选择了非常长的一个时间数列
A time series for a very long period was selected
那我们发现
and we found
我首先是把这个时间数列
first of all, by displaying the time series
把它集中的
of a smaller dimension
这个比较小的维度给它展示的话
in a centralized manner
你们会发现这个循环变动的幅度
we found the amplitude of the cyclical variation
强烈一些或者说明显一些
is bigger or is more obvious
那我们看到
We see that
如果我们是把它这个图形
if we extend the graph a little
把它展开一些的话
extending its time
可能这个循环变动
maybe this cyclical variation
它不是那么明显
becomes not so obvious
那实际上在我们现实生活中
In actual life
我们去 当然了可能大家在座的同学
as college students
可能说 同学们可能不一定会去
you may not necessarily
买这个食品
buy these foods
但如果说你以后 步入社会的话
But after graduating from universities
你去购买这些食品一类的商品的话
when you buy commodities like these foods
你会发现对于猪肉商品来讲
you will find that the price of pork products
它确实存在这么一个一年半
indeed has a cycle of
这样一个周期 大概
about one and a half years
它一年半周期之间的话
Within the one-and-a-half-year cycle
它是随着这个猪肉的这个价值的话
the prices of pork
类似在上下波动
fluctuate with the its value
这个概念其实咱们在这个经济学中
In fact, in economics
其实也有学习到
we have learned this concept
就是说我们在讲
that is, the relationship
价格和价值的关系的时候曾经提到过
between price and value is that
价格是随着价值在上下波动的
price fluctuates up and down with value
注意这一点
Please pay attention to this
所以我们循环变动其实也是
So, cyclical variation actually
非常好的体现了这么一个变化的情况
reflects such changes very well
最后一个因素
The last factor
我们说是这个不规则变动
is erratic variation
其实对于这个不规则变动来讲
Actually, for erratic variation
我们就比较难以取得
it is more difficult for us to obtain
这个比较长的一个时间数列一个数据
data of a longer time series
那我举一个比较显著的一个例子
Here is an example
就是我们曾经有感受到
We once observed that
就是我们说2008年
after the Wenchuan earthquake
我们说汶川地震的时候
in 2008
那实际上在汶川地震以后
there was a big change
我们这个四川省
in the data of one index
有一项这个指标数据是变化幅度
of Sichuan Province
是非常大的
The change is huge
你们想想看
Please think about it
可能是什么经济指标变化幅度比较大
what economic indicator may it be
很多同学可能都纷纷在思考
Now, you are thinking about it
那实际上 我们马上直观能想到
Actually, we can easily see that
就是这种大灾大难面前的话
in face of such a catastrophe
什么行业受影响是比较大的
which industry was affected the most
保险行业对不对
The insurance industry, right
所以在那一年这个四川省
so, in 2008, the profit margin
它这个保险行业
of the insurance industry
它这个利润率的这个变化幅度
in Sichuan Province
是很大的
had changed greatly
因为对于这个保险行业来讲的话
For the insurance industry
它受这个不规则的影响
it is greatly affected
变动因素的影响是很大的
by erratic variation
所以我们对于这个不规则因素
Therefore, erratic variation
或者说随机因素的这个界定的话
or random variation mainly includes
主要是战争 政治
wars, politics
还有一些这个气侯
climate changes
或者说一些灾难一些影响
and some disasters
它这种变动方向我们是没有办法
We cannot judge
进行判断的
the direction of changes
所以这种因素的话
so this factor
是难以进行预测的
is difficult to predict
就是比较 当然它这个因素
Of course, sometimes this factor
有些时候对这个经济指标这个影响
is very influential
也是非常大的
with economic indicator
那我们说对于这四个因素而言的话
So, what is the relationship among
它们之间的关系是什么
these four factors
长期趋势它是非常重要的
Secular trend is very important
我们是预测 把握 程度是比较高的
Its predictability is relatively high
季节变动也是我们要去进行研究的
Seasonal variation is also a factor to be researched
而剩下的这两个循环变动
And the remaining two factors
和这个不规则变动
cyclical variation and erratic variation
实际上我们在本科阶段
are actually more difficult to study in depth
它是比较难以去深入研究的
at an undergraduate level
那我们对于这四个因素的话
To analyze these four factors
我们有两种模型来进行分析
we have two models
一种模型是我们看到的这个加法模型
One is the additive model
另外一种模型叫乘法模型
The other is the multiplicative model
那我们看到这个加法模型
In the additive model
它这个关系是什么呢
what is the relationship
也就是说我这四个因素
The relationship among these four factors
是T+S+C+I
is T+S+C+I
那么我们说乘法模型是什么呢
while in the multiplicative model
乘法模型是我们说这个四个因素之间
these four factors
它们是交互影响的
interact with each other
所以它们的关系是T×S×C×I
with their relationship as T×S×C×I
那么我们说在现实生活中
Now, let's think about
我们思考一下看 同学们想想看
whether there are more additive models
究竟是加法模型更多
or more multiplicative models
还是乘法模型更多
in real life
也就是我们考虑一下
That is
这些因素它是交互影响的多
are these factors more interactive
还是相互独立的要更多
or independent
那我想很多同学 想一想看
I believe many of you want to better understand
这个复杂因素之间的关系
the relationship among these complicated factors
其实可能乘法模型它这种
In fact, multiplicative models
普遍性要强一些
are more common
那我们根据这个模型我们知道
Since there are two types of models
如果说 这个时候
if we say
我要去对各个因素来进行测定的时候
we want to measure various factors
那我们说那就会存在两种思路
there are two methods
一种是如果说它这个
If the factors
因素之间是相互独立的话
are independent of each other
那我们说我们就选择这个加法模型
choose the additive model
如果说它这个因素之间
if the factors interact
是相互影响的话
with each other
那就是说我们就选择乘法模型
then we choose the multiplicative model
那乘法模型的话
If the multiplicative model is adopted
如果我知道
Then we say
这个T的一个因素影响的话
the influencing factors of T
它自然等于这个
is equivalent to the time series
经济指标的时间数列(公式如上)
of an economic indicator (formula as the above)
然后如果是加法模型
Then, if an additive model is adopted
那我们说这个T的这个测定的话
we use this economic indicator
那自然就是我们说这个经济指标
to the measure T
(formula as the above)
好 这个是我们看到的
Okay, this is
这个时间数列的构成因素分析
the analysis of the components of the time series
主要是四个方面的因素分析
Mainly 4 factors are included
四个方面的因素
These four factors are
一个是长期趋势
secular trend
第二个是我们说的这个季节变动
seasonal variation
第三个是循环变动
cyclical variation
第四个是不规则变动
and erratic variation
好 这一讲内容结束
Okay, that’s all for this lecture
-1.1 Applications in Business and Economics
--1.1.1 Statistics application: everywhere 统计应用:无处不在
-1.2 Data、Data Sources
--1.2.1 History of Statistical Practice: A Long Road 统计实践史:漫漫长路
-1.3 Descriptive Statistics
--1.3.1 History of Statistics: Learn from others 统计学科史:博采众长
--1.3.2 Homework 课后习题
-1.4 Statistical Inference
--1.4.1 Basic research methods: statistical tools 基本研究方法:统计的利器
--1.4.2 Homework课后习题
--1.4.3 Basic concepts: the cornerstone of statistics 基本概念:统计的基石
--1.4.4 Homework 课后习题
-1.5 Unit test 第一单元测试题
-2.1Summarizing Qualitative Data
--2.1.1 Statistical investigation: the sharp edge of mining raw ore 统计调查:挖掘原矿的利刃
-2.2Frequency Distribution
--2.2.1 Scheme design: a prelude to statistical survey 方案设计:统计调查的前奏
-2.3Relative Frequency Distribution
--2.3.1 Homework 课后习题
-2.4Bar Graph
--2.4.1 Homework 课后习题
-2.6 Unit 2 test 第二单元测试题
-Descriptive Statistics: Numerical Methods
-3.1Measures of Location
--3.1.1 Statistics grouping: from original ecology to systematization 统计分组:从原生态到系统化
--3.1.2 Homework 课后习题
-3.2Mean、Median、Mode
--3.2.2 Homework 课后习题
-3.3Percentiles
--3.3 .1 Statistics chart: show the best partner for data 统计图表:展现数据最佳拍档
--3.3.2 Homework 课后习题
-3.4Quartiles
--3.4.1 Calculating the average (1): Full expression of central tendency 计算平均数(一):集中趋势之充分表达
--3.4.2 Homework 课后习题
-3.5Measures of Variability
--3.5.1 Calculating the average (2): Full expression of central tendency 计算平均数(二):集中趋势之充分表达
--3.5.2 Homework 课后习题
-3.6Range、Interquartile Range、A.D、Variance
--3.6.1 Position average: a robust expression of central tendency 1 位置平均数:集中趋势之稳健表达1
--3.6.2 Homework 课后习题
-3.7Standard Deviation
--3.7.1 Position average: a robust expression of central tendency 2 位置平均数:集中趋势之稳健表达2
-3.8Coefficient of Variation
-3.9 unit 3 test 第三单元测试题
-4.1 The horizontal of time series
--4.1.1 Time series (1): The past, present and future of the indicator 时间序列 (一) :指标的过去现在未来
--4.1.2 Homework 课后习题
--4.1.3 Time series (2): The past, present and future of indicators 时间序列 (二) :指标的过去现在未来
--4.1.4 Homework 课后习题
--4.1.5 Level analysis: the basis of time series analysis 水平分析:时间数列分析的基础
--4.1.6Homework 课后习题
-4.2 The speed analysis of time series
--4.2.1 Speed analysis: relative changes in time series 速度分析:时间数列的相对变动
--4.2.2 Homework 课后习题
-4.3 The calculation of the chronological average
--4.3.1 Average development speed: horizontal method and cumulative method 平均发展速度:水平法和累积法
--4.3.2 Homework 课后习题
-4.4 The calculation of average rate of development and increase
--4.4.1 Analysis of Component Factors: Finding the Truth 构成因素分析:抽丝剥茧寻真相
--4.4.2 Homework 课后习题
-4.5 The secular trend analysis of time series
--4.5.1 Long-term trend determination, smoothing method 长期趋势测定,修匀法
--4.5.2 Homework 课后习题
--4.5.3 Long-term trend determination: equation method 长期趋势测定:方程法
--4.5.4 Homework 课后习题
-4.6 The season fluctuation analysis of time series
--4.6.1 Seasonal change analysis: the same period average method 季节变动分析:同期平均法
-4.7 Unit 4 test 第四单元测试题
-5.1 The Conception and Type of Statistical Index
--5.1.1 Index overview: definition and classification 指数概览:定义与分类
-5.2 Aggregate Index
--5.2.1 Comprehensive index: first comprehensive and then compare 综合指数:先综合后对比
-5.4 Aggregate Index System
--5.4.1 Comprehensive Index System 综合指数体系
-5.5 Transformative Aggregate Index (Mean value index)
--5.5.1 Average index: compare first and then comprehensive (1) 平均数指数:先对比后综合(一)
--5.5.2 Average index: compare first and then comprehensive (2) 平均数指数:先对比后综合(二)
-5.6 Average target index
--5.6.1 Average index index: first average and then compare 平均指标指数:先平均后对比
-5.7 Multi-factor Index System
--5.7.1 CPI Past and Present CPI 前世今生
-5.8 Economic Index in Reality
--5.8.1 Stock Price Index: Big Family 股票价格指数:大家庭
-5.9 Unit 5 test 第五单元测试题
-Sampling and sampling distribution
-6.1The binomial distribution
--6.1.1 Sampling survey: definition and several groups of concepts 抽样调查:定义与几组概念
-6.2The geometric distribution
--6.2.1 Probability sampling: common organizational forms 概率抽样:常用组织形式
-6.3The t-distribution
--6.3.1 Non-probability sampling: commonly used sampling methods 非概率抽样:常用抽取方法
-6.4The normal distribution
--6.4.1 Common probability distributions: basic characterization of random variables 常见概率分布:随机变量的基本刻画
-6.5Using the normal table
--6.5.1 Sampling distribution: the cornerstone of sampling inference theory 抽样分布:抽样推断理论的基石
-6.9 Unit 6 test 第六单元测试题
-7.1Properties of point estimates: bias and variability
--7.1.1 Point estimation: methods and applications 点估计:方法与应用
-7.2Logic of confidence intervals
--7.2.1 Estimation: Selection and Evaluation 估计量:选择与评价
-7.3Meaning of confidence level
--7.3.1 Interval estimation: basic principles (1) 区间估计:基本原理(一)
--7.3.2 Interval estimation: basic principles (2) 区间估计:基本原理(二)
-7.4Confidence interval for a population proportion
--7.4.1 Interval estimation of the mean: large sample case 均值的区间估计:大样本情形
--7.4.2 Interval estimation of the mean: small sample case 均值的区间估计:小样本情形
-7.5Confidence interval for a population mean
--7.5.1 Interval estimation of the mean: small sample case 区间估计:总体比例和方差
-7.6Finding sample size
--7.6.1 Determination of sample size: a prelude to sampling (1) 样本容量的确定:抽样的前奏(一)
--7.6.2 Determination of sample size: a prelude to sampling (2) 样本容量的确定:抽样的前奏(二)
-7.7 Unit 7 Test 第七单元测试题
-8.1Forming hypotheses
--8.1.1 Hypothesis testing: proposing hypotheses 假设检验:提出假设
-8.2Logic of hypothesis testing
--8.2.1 Hypothesis testing: basic ideas 假设检验:基本思想
-8.3Type I and Type II errors
--8.3.1 Hypothesis testing: basic steps 假设检验:基本步骤
-8.4Test statistics and p-values 、Two-sided tests
--8.4.1 Example analysis: single population mean test 例题解析:单个总体均值检验
-8.5Hypothesis test for a population mean
--8.5.1 Analysis of examples of individual population proportion and variance test 例题分析 单个总体比例及方差检验
-8.6Hypothesis test for a population proportion
--8.6.1 P value: another test criterion P值:另一个检验准则
-8.7 Unit 8 test 第八单元测试题
-Correlation and regression analysis
-9.1Correlative relations
--9.1.1 Correlation analysis: exploring the connection of things 相关分析:初探事物联系
--9.1.2 Correlation coefficient: quantify the degree of correlation 相关系数:量化相关程度
-9.2The description of regression equation
--9.2.1 Regression Analysis: Application at a Glance 回归分析:应用一瞥
-9.3Fit the regression equation
--9.3.1 Regression analysis: equation establishment 回归分析:方程建立
-9.4Correlative relations of determination
--9.4.1 Regression analysis: basic ideas
--9.4.2 Regression analysis: coefficient estimation 回归分析:系数估计
-9.5The application of regression equation