### 经济代写|产业经济学代写Industrial Economics代考|ECON3400

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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|Debt risks accumulated to pin down industrial operation

Local debt risk remains high. During economic deceleration, the fiscal revenue growth will slow down and the expenditure will rise moderately under the influence of economic fundamentals, enlarging the scale of deficit and debt. Furthermore, the fact that local governments may execute debt financing on various financing platforms to maintain goals of local economic and social development and to accelerate infrastructure construction will initiate a new mode to stimulate economic growth by governments’ leveraging investment. When the government no long provides any guarantee for these debts, some of the debts will be transferred by financing platforms to debts payable by the government; as a result, the debts payable by the local government will increase. According to the results of national debt audit at the end of 2015, RMB $1.9$ trillion debts payable by the government fell due in 2015 . Excessive debt ratio of the local government will put local government under heavy pressure to discharge debts and will also easily lead to crisis of local government debts; in addition, due to different rates of local economic growth and different debt burden of provinces, it is likely to incur local debt crisis. Excessive local government debts may also lead to risk of local government’s bankruptcy and place strict restrictions on local government’s further financing and on continuous investment in infrastructure construction and thus compromise the growth of industrial economics.

Potential risk in industrial sectors remains high. In May 2016, the debt-to-asset ratio of industrial enterprises above designated size was $56.8 \%, 0.6$ percentage point higher than December 2015 , and the leverage ratio reached up to $131 \%$, which increased the operating risk of industrial sectors. It is noted that the excessively high leverage ratio of Chinese enterprises was questioned as the leverage ratio of foreign enterprises maintained at around $70 \%$. In effect, the leverage ratio of Chinese enterprises has been extremely high for many years, for it was closely related to Chinese economic reality: (i) high saving rate that means relatively adequate supply of capitals in China, and (ii) high leverage that results from two realistic bases – relatively lagging development of China’s capital market and credit financing used as the main financing channel by China’s industrial sectors. However, these two bases are slowly collapsing as the consumer savings declines and the capital market expands and plays more financing functions. In recent years, the de-leverage ratio of industrial enterprises has paced up, reducing from $178 \%$ at the beginning of 1998 to $128 \%$ at the end of December 2015 . The current uptrend of leverage has no realistic base. Furthermore, the local government’s debt risks are constantly discharged. China as a whole suffers a very high debt ratio, so the increasing leverage will intensify the risk pressure.

## 经济代写|产业经济学代写Industrial Economics代考|Prediction of Industrial Growth Trend

HP filtering method adopted in this report separates the growth trend of industrial value added year on year from cyclical factors to analyze roles of different factors in industrial value added. According to the results, the growth rate of industrial economics has slowed down since the year of 2010 ; the slowing growth rate has continued in the first half of 2016 but the decreasing amplitude was narrowing; and it is predicted that the industrial growth will highly likely hit the bottom in the second half of 2016
(1) Data source and interpolation of missing values
In this report, the value added year-on-year growth data of industries above designated size are used as observing indicators of industrial growth, with the samples ranging between January 2008 and June 2016 . Data are sourced from National Bureau of Statistics website. The industrial value added year-on-year growth rate is: (i) calculated by comparable prices, independent of price factors and free from price adjustment, and (ii) value added growth data of industries above designated size with January growth data missed, which needs interpolation. The traceability method is adopted in this report to interpolate data. Specific steps are as follows.
Firstly, the monthly year-on-year growth rate data and the monthly accumulative growth rate data of industrial value added as well as the monthly actual data in 2005 of industrial value added ${ }^{2}$ are obtained from National Bureau of Statistics website. Secondly, the monthly industrial value actually added in 2005 is used to figure out the monthly accumulative growth rates of industrial value added in 2005 . Thirdly, the monthly accumulative industrial value added in 2005 and the monthly accumulative growth rates of industrial value added in 2006 are used to calculate the monthly accumulative industrial value added in 2006 , and by analogy get the monthly accumulative industrial value added from 2007 to 2016 with January data missed. Fourthly, the monthly industrial value actually added in 2005 and the monthly year-on-year growth rates of industrial value added in 2006 are used to figure out the monthly industrial value actually added in 2006 , and by analogy get the monthly industrial value actually added from 2007 to 2016 with January data missed. Fifthly, the industrial values actually added in January from 2006 to 2013 are obtained by the accumulative number in February of industrial value added from 2006 to 2016 with January data missed minus the actual industrial value added in February from 2006 to 2016 . Finally, all monthly data calculated above are used to directly get the missing January data about the monthly year-on-year growth rates of industrial value added (Fig. 2.5).

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

In order to separate the long-term trend factors from the cyclical (irregular) factors of industrial growth and obtain estimation of unobservable potential factors, either the moving average method or the frequency domain estimation method may be used for the original data of single time sequence; the filtering method has a unique advantage, i.e. simple, intuitive and easy for implementation, and can also avoid the problem caused by production function method, i.e. whether the product function can be stable in the economic transition period, and the problem caused by variable structure decomposition method, i.e. whether there exists the Phillips curve of conventional form in China. Therefore, the HP filtering method is adopted in this section to predict the growth trend of industrial economics.

The HP filtering de-trending method may regard economic operation as a certain combination of potential growth and short-term fluctuations and use metrological technology to decompose the actually output sequence into trend components and cyclical components; the former means potential output while the latter means output gap or fluctuation. For growth rate of industrial operation, the time sequence $y_{t}$ consists of industrial operation trend $g_{t}$ and industrial operation fluctuation $c_{t}$, namely:
$$y_{t}=g_{t}+c_{t} \quad t=1, \ldots T$$
Hodrick and Prescott $(1980,1997)^{3}$ designed HP filter by following the logarithm data moving average method. The filter can obtain a smooth sequence $g_{t}$ from the time sequence $y_{t}$, i.e. trend component, and $g_{t}$ is the solution to the formula below:
$$\operatorname{Min}\left{\sum_{t=1}^{T}\left(y_{t}-g_{t}\right)^{2}+\lambda \sum_{t=1}^{T}\left[\left(g_{t}-g_{t-1}\right)\left(g_{t}-g_{t-2}\right)\right]\right}$$
where, $\sum_{t=1}^{T}\left(y_{t}-g_{t}\right)^{2}$ represents fluctuations, $\sum_{t=1}^{T}\left[\left(g_{t}-g_{t-1}\right)\left(g_{t}-g_{t-2}\right)\right]$ represents trend, and $\lambda$ is smooth parameter with a positive value used to adjust proportions of fluctuation and trend. Selection of the smooth parameter $\lambda$ is an important problem in the HP filtering method. Different smooth parameters mean different filters that determine different fluctuating modes and smoothness. According to Hodrick and Prescott $(1980,1997)$, the value of smooth parameter is taken as 100 in processing annual data, as 1600 in processing quarterly data and as 14,400 in processing monthly data. According to Ravn and Uhlig (2002), ${ }^{4}$ the smooth parameter should be 4th power of the observed data frequency, i.e. $6.25$ for annual data, 1600 for quarterly data and 129,600 for monthly data. In this report, the data used are growth rates of industrial value added from January 2010 to September 2015, sourced from National Bureau of Statistics website. It is important to note that the missing data on growth rates of industrial value added in January on National Bureau of Statistics website are supplemented by point linear interpolation in this report. Above two types of filters are selected for use in this report: $\lambda=14,400$ and $\lambda=129,600$.

（1）数据来源及缺失值插值

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

HP滤波去趋势法可以将经济运行视为潜在增长和短期波动的某种组合，利用计量技术将实际产出序列分解为趋势成分和周期成分；前者是潜在产出，后者是产出缺口或波动。工业运行增长率的时间序列是吨由产业运行趋势构成G吨和工业运行波动C吨，即：

\operatorname{Min}\left{\sum_{t=1}^{T}\left(y_{t}-g_{t}\right)^{2}+\lambda \sum_{t=1}^{ T}\left[\left(g_{t}-g_{t-1}\right)\left(g_{t}-g_{t-2}\right)\right]\right}\operatorname{Min}\left{\sum_{t=1}^{T}\left(y_{t}-g_{t}\right)^{2}+\lambda \sum_{t=1}^{ T}\left[\left(g_{t}-g_{t-1}\right)\left(g_{t}-g_{t-2}\right)\right]\right}

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