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Research Article
Research on the Relationship between Business Cycle and
Industrial Fluctuations in Northeast China Based on Complete
Ensemble Empirical Mode Decomposition with Adaptive Noise
Yinan Zhou , Guofeng Gu , and Qiushuang Ren
School of Geographical Sciences, Northeast Normal University, Changchun 130024, China
Correspondence should be addressed to Guofeng Gu; gugf@nenu.edu.cn
Received 24 August 2020; Revised 1 November 2020; Accepted 23 December 2020; Published 8 January 2021
Academic Editor: Jun Yang
Copyright ©2021 Yinan Zhou et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e Chinese economy has developed rapidly since the reform and opening up, but economic growth in Northeast China has
declined dramatically after the 21st century. In this context, exploring the characteristics of economic and industrial fluctuations
in the northeast of China and their relationship is beneficial to alleviating economic fluctuations and promoting stable economic
development from the perspective of industrial development. e relationship between economic and industrial fluctuations in
the three provinces of Northeast China was reexamined from the angle of fluctuation components with the complete ensemble
empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. e results obtained are as follows: (1) In the three
northeastern provinces of China, economic fluctuations were almost free from the influence of the primary industry, most affected
by the secondary industry, and gradually influenced by the tertiary industry after the 21st century. (2) Regarding the short-term
business cycle of each province, economic development was the most stable when the market and government participated in the
development of the secondary industry simultaneously. (3) e midterm business cycle of Jilin Province was affected by the
investment of equipment in secondary and tertiary industries, while that of Liaoning Province was affected by the investment of
equipment in the secondary industry. (4) Investment in the equipment of the secondary industry and the construction of
secondary and tertiary industries was the key to maintaining the stability of long-term business cycle in Heilongjiang Province,
and that in the construction of secondary and tertiary industries was the key to maintaining the stability of long-term business
cycles in Jilin and Liaoning Provinces.
1. Introduction
Economy has maintained steady and rapid growth in China
since the reform and opening up but experienced severe
economic downturns in the three northeastern provinces of
China in the 1990s and 2010s. e growth rate of gross
domestic product (GDP) in the three northeastern provinces
of China is much less than the national average, and obvious
problems exist in various indicators of economic operation.
According to the historical experience of all countries
around the world in economic development, an economic
system always shows fluctuations regardless of system type
or development stage, which is specifically manifested in the
fluctuation level of economic activities, namely, business
cycle. As two basic forms of economic system evolution,
economic and industrial fluctuations are inseparable from
each other. In this context, studying the relationship be-
tween economic and industrial fluctuations in the three
northeastern provinces of China is of important practical
significance to make policy recommendations to alleviate the
problems of Northeast China from the perspective of in-
dustry, thereby reducing economic fluctuations and main-
taining the sustainable development of economy in the
northeast of China.
e academic circles have three different viewpoints
about the relationship between economic fluctuations and
industrial structure changes. e first point of view comes
from the community of mainstream economics, believing
that economic fluctuations determine industrial fluctuations
and industrial structure changes [1]. Kuznets analyzed the
Hindawi
Complexity
Volume 2021, Article ID 8832201, 16 pages
https://doi.org/10.1155/2021/8832201
impact of the economic growth rate on the range of changes
in the production structure and concluded that structural
change is an important fact in the fluctuation of modern
economic growth [2]. Berman and Pfleeger studied the
industries where demand and employment are sensitive to
economic fluctuations, which can predict the industries
changing in sync with business cycle in the future and
identify industrial fluctuations [3]. Lin et al. believed that the
reason for different industrial structures in different coun-
tries at different stages of business cycle is that the upgrading
of industrial structure is up to the structure change of the
factor endowment of economy, which is mainly affected by
the stage of economic growth. Fundamentally speaking,
economic growth fluctuations cause the change of industrial
structure [4, 5]. Ma conducted a theoretical and empirical
study on the relationship between economic fluctuations
and industrial structure changes and found that a variety of
industries are sensitive to business cycles to varying degrees,
with the secondary industry showing the highest sensitivity
[6–9].
e second point of view comes from development
economics with structuralism as the basic idea, believing that
the change of industrial structure brings about economic
fluctuations. Li held that macroeconomic fluctuations are
mainly caused by the fluctuations of primary and secondary
industries [10]. Chen maintained that economic fluctuations
have a high degree of synergy with the fluctuations of the
three industries and subindustry sectors in China. Tertiary,
secondary, and primary industries, respectively, have an
impact on business cycle, whose influence degree decreases
in order [11]. Sonobe and Otsuka demonstrated the im-
portant relationship between industrial structure changes
and economic growth fluctuations in Japan before the
Second World War [12]. Peneder examined the connection
between industrial structure changes and economic fluc-
tuations using the data from Organization for Economic Co-
operation and Development (OECD) countries and ob-
served that industrial structure explained 30% of the re-
duction of economic volatility [13]. Fan et al. came to the
conclusion that structural change can explain 17% of the
fluctuation of economic growth in China, which was in line
with the research conclusions of Li et al. [14–16]. In addition,
Eggers and Ioannides pointed out that the structural shift
from manufacturing to the service industry effectively re-
duced economic volatility in the United States (US) [17].
Burns demonstrated the impact of industrial structure
changes on the fluctuations of business cycle in the US [18].
Gordon emphasized the role of economic structural changes
in the stabilization of business cycle in his collection of
essays on the business cycle of the US [19]. Moreover, some
researchers analyzed the relationship between industrial
structure changes and economic fluctuations in China and
believed that industrial structure changes have a “smoothing
effect” on macroeconomic fluctuations [20–23]. Ding and
Zhang reached similar conclusions by analyzing the asso-
ciation between industrial structure changes and economic
fluctuations in Japan [24].
e third view believes that economic fluctuations and
industrial structure changes influence each other [25–29].
Zhang and Liu found that the fluctuations of industrial
output growth are an important component of business
cycle fluctuations which have a feedback effect on industrial
output fluctuations [30]. Li et al. demonstrated the causal
and dynamic relationships between industrial and economic
fluctuations in Taiwan Province [31, 32]. Jiang and Jiao
investigated the influence of industrial structure evolution
on economic fluctuations in Shanxi Province and concluded
that three industrial and economic fluctuations in Shanxi
Province have Granger causality after the reform and
opening up [33]. Jiang, Zhang, and Jiang analyzed the
correlation between the fluctuations of the producer service
industry and the economy in China and reached the con-
clusion that developing the producer service industry can
stabilize economic fluctuations which however are not
conducive to developing the producer service industry [34].
Conclusions about the relationship between economic
and industrial fluctuations are inconsistent due to different
research areas and methods. From the perspective of the
frequency domain, filtering algorithms like collaborative
filtering (CF) and Hodrick-Prescott (HP) filtering were
mostly used in the past and require selecting a basis function
at first, which is highly subjective. Different basis functions
will produce different research results. By contrast, the
complete ensemble empirical mode decomposition with
adaptive noise (CEEMDAN) algorithm has no need to select
a basis function and require the stability and linearity of data
without losing data information, which is beneficial to
obtain more objective and accurate research results. In order
to determine the relationship between economic and in-
dustrial fluctuations in the northeast of China, the time-
series data of economic and industrial fluctuations in the
three northeastern provinces of China from 1978 to 2018
were used, and the EMD algorithm was selected to analyze
the following main issues: (1) What are the characteristics of
economic and industrial fluctuations in the three northeast
provinces of China since the reform and opening up? (2)
What is their relationship? (3) What are the similarities and
differences between economic and industrial fluctuations in
the three provinces of Northeast China?
e remainder of this paper was organized as follows.
Section 2 examined economic and industrial fluctuations in
the three northeastern provinces of China since the reform
and opening up, made an introduction of data sources, and
expounded the basic ideas and steps of the CEEMDAN
algorithm. Section 3 decomposed the sequences of economic
and industrial fluctuations in the three northeastern prov-
inces of China to obtain components and analyzed the
characteristics of each fluctuation component. Section 4
examined the relationship between the components of
economic and industrial fluctuations in each province.
Section 5 discussed the research methods and content of this
paper. Section 6 made a summary of research conclusions.
2. Materials and Methods
2.1. Materials. In this paper, the data from 1978 to 2018 were
used to conduct empirical analysis. e data from 1978 to
1992 were selected from the China Compendium of
2Complexity
Statistics 1949–2008, and those between 1993 and 2018 were
sourced from provincial-level statistics on the website of the
National Bureau of Statistics. Provincial GDP data (previous
year �100) were processed to obtain the real GDP growth
rate of each province from 1978 to 2018 to measure the
economic fluctuations of each province. e fluctuation
cycles of each industry in each province were measured by
using the value-added data (previous year �100) of primary,
secondary, and tertiary industries in each province from
1978 to 2018. Figure 1 shows a line chart of the GDP growth
rate of each province and the growth rates of primary,
secondary, and tertiary industries.
Fluctuation curves of the GDP growth rates of Hei-
longjiang, Jilin, and Liaoning Provinces are shown in
Figures 1(a)–1(c) which demonstrate that the GDP growth
rates of the three provinces were slightly different in different
periods but presented similar overall trends after the reform
and opening up. Before the 21st century, they showed a trend
of uneven growth. After the 21st century, economy showed a
steady growth trend until approximately 2010 due to the
decrease in the fluctuations in GDP growth rates. en, GDP
growth rates began to decline, which produced the so-called
“New Northeast Phenomenon.” From the perspective of
industrial fluctuations, first of all, the natural environment
played a decisive in the development of the primary industry
which therefore experienced the largest fluctuations among
the three industries of each province. Second, the fluctuation
trends of secondary and tertiary industries were similar to
that of the GDP growth rate due to the planned economy,
and the fluctuation range was wide during the period be-
tween the reform and opening up and the 1990s when
government decisions were of decisive importance for
economic development. In 1992, a socialist market eco-
nomic system was established in China. With the function of
self-regulation, the market played a leading part in economic
development. Various industries saw stable growth and a
decline in fluctuations. e main driving force for the
Chinese economy at the time was the secondary industry
which showed a more obvious growth trend than the tertiary
industry. By 2010, the growth rates of secondary and tertiary
industries declined to varying degrees, and the decline in the
growth rate of the secondary industry was significantly
higher than that of the tertiary industry.
From the above, it can be seen that the industrial
fluctuations in the three provinces have certain similarities
with the economic fluctuations in corresponding provinces,
especially in secondary and tertiary industries. Cycles were
further subdivided below for analysis and discussion.
2.2. Methods. e CEEMDAN algorithm is the main re-
search method in the paper. is algorithm is a frequency
domain analysis method for analyzing time-series data. It is
based on the empirical mode decomposition (EMD) algo-
rithm proposed by Huang et al.
e basic idea of the EMD is the following: time-series
data are composed of many different coexisting internal
mode functions (IMFs) at any time and the final complex
data can be obtained by superimposing each mode function.
e algorithm is different from other frequency domain
processing methods since it does not need to set any basis
functions in advance. In addition, it can be applied to de-
compose various time-series data, and it especially has a very
obvious advantage when dealing with nonstationary and
nonlinear data [35, 36].
e steps of CEEMDAN algorithm are as follows.
Step 1. Assuming that the original time-series data is x(t),
let i�1,. . .,I. en decompose the time-series data using
EMD algorithm which added the adaptive white noise and
getting the first IMF component:
imf1�1
2
1
I
I
i�1
E1+x(t)t
⎛
⎝⎞
⎠+1
I
I
i�1
E1−x(t)t
,
i�1,· · · , I .
(1)
Step 2. Calculate the remainder by excluding the first IMF
component:
r1(t) � x(t) − imf1.(2)
Step 3. Treat r1(t)as a new original sequence and repeat
Steps 1 and 2 to obtain the remaining components
imf2,· · · ,and imfnand remaining terms rn(t). e condi-
tion for stopping the decomposition is that the remaining
term rn(t)cannot be decomposed, which is the trend term
res �rn(t).
After the above steps, the original sequence can be
decomposed into nIMFs and a trend term; that is,
x(t) �
n
i�1
imfi+res,(3)
where IMF
i
is the ith component obtained by decomposing
original time-series data and res is the trend term.
3. Empirical Analysis
3.1. Business Cycle Analysis. With the help of MATLAB
R2016b, the GDP growth rate data of Heilongjiang, Jilin, and
Liaoning Provinces were decomposed using CEEMDAN
algorithm to obtain the cycle components and trend terms of
economic fluctuations in each province, as shown in Fig-
ure 2. e results of Heilongjiang, Jilin, and Liaoning
Provinces are shown in the first, second, and third columns,
respectively. e first to fourth lines are economic cycle
components, and the fifth line is the trend term.
From the shapes of business cycles and trend terms in
Figure 2, it can be intuitively seen that business cycles
corresponding to the frequencies between provinces are
similar. From the perspective of fluctuation amplitude, high-
frequency components (IMFs 1 and 2) fluctuated violently
from 1978 to the 1990s and gradually turned into small-
amplitude fluctuations after the 1990s. e fluctuation
amplitudes of low-frequency components (IMFs 3 and 4)
were slight before the 21st century and gradually increased
Complexity 3
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(a)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(b)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(c)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(d)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(e)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(f)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(g)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(h)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(i)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(j)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(k)
–20
0
20
40
1978 1983 1988 1993 1998 2003 2008 2013 2018
(l)
Figure 1: Fluctuation curves of GDP growth rate and value added of each industry in the three provinces: (a) GDP of Heilongjiang, (b) GDP
of Jilin, (c) GDP of Liaoning, (d) primary industry of Heilongjiang, (e) primary industry of Jilin, (f) primary industry of Liaoning, (g)
secondary industry of Heilongjiang, (h) secondary industry of Jilin, (i) secondary industry of Liaoning, (j) tertiary industry of Heilongjiang,
(k) tertiary industry of Jilin, and (l) tertiary industry of Liaoning.
–9
–6
–3
0
3
6
9
1978 1988 1998 2008 2018
(a)
–9
–6
–3
0
3
6
9
1978 1988 1998 2008 2018
(b)
–9
–6
–3
0
3
6
9
1978 1988 1998 2008 2018
(c)
–8
–5
–2
1
4
7
1978 1988 1998 2008 2018
(d)
–8
–5
–2
1
4
7
1978 1988 1998 2008 2018
(e)
–8
–5
–2
1
4
7
1978 1988 1998 2008 2018
(f)
–7
–4
–1
2
5
1978 1988 1998 2008 2018
(g)
–7
–4
–1
2
5
1978 1988 1998 2008 2018
(h)
–7
–4
–1
2
5
1978 1988 1998 2008 2018
(i)
Figure 2: Continued.
4Complexity
over time. e trend term in each province had an “inverted-
U” shape and reached a peak in the early 21st century.
In addition to the above common features that can be
intuitively observed from Figure 2, business cycles have
differences. To more fully understand the features of busi-
ness cycle components and the trend terms of economic
fluctuations, relevant characteristics are summarized in
Table 1.
e frequencies of business cycles changed with time
without a fixed period. erefore, the amount of data
contained in each business cycle component was divided by
the number of the extreme points of corresponding com-
ponents to represent the average period. According to the
difference in the average cycle duration and influencing
factors, business cycle can be divided into Kitchin, political
economy, Juglar, Kuznets, and Kondratiev cycles. Also called
the inventory or short cycle, the Kitchin cycle is a regular
short-term fluctuation discovered by American economist
Kitchin, whose fluctuation is related to the changes in
commercial inventory and duration is approximately 40
months. e political economic cycle is caused by the
promotion system cycle of China, government changes, the
Five-Year Plan, and the influence of macrocontrol. Also
known as the equipment investment cycle or midcycle, the
Juglar cycle was found by French economists Juglar, whose
fluctuation is approximately 10 years and results from the
fluctuations in equipment investment. Also known as the
construction cycle or the middle cycle, the Kuznets cycle is a
20-year economic cycle caused by the cyclical changes in
construction activities and discovered by American econ-
omist Kuznets. Found by Russian economist Kondratiev, the
Kondratiev cycle is a long cycle of 50–60 years that is at-
tributed to technological progress and innovation. Different
business cycle fluctuations have formed in a variety of re-
gions because of the above five cycles and their superposition
[37].
As shown in Table 1, the business cycle components of
Heilongjiang Province include Kitchin, Juglar, Kuznets, and
Kondratiev cycles. All cyclical components and trend terms
were significantly associated with the original time-series
sequence. From the perspective of the variance contribution
rate, the Kitchin cycle had the greatest impact on economic
fluctuations and achieved a variance contribution rate of as
high as 51.95%. e Juglar cycle had the smallest impact on
economic fluctuations, which can be ignored. e rest of the
components and trend terms had similar effects on eco-
nomic fluctuation series. Business cycle components in Jilin
and Liaoning Provinces were different from those in Hei-
longjiang Province because of excluding Kitchin and
Kondratiev cycles and being replaced by the political eco-
nomic cycle. In addition, they include Juglar and Kuznets
cycles. Each component was significantly related to the
original sequence which had the weakest relationship with
trend terms. e political economic cycle had the greatest
impact on the economic fluctuations of Jilin Province, while
the Kuznets cycle had the greatest impact on the economic
fluctuations of Liaoning Province.
3.2. Industry Cycle Analysis. e fluctuation components
and trend terms of various industries were obtained by
dealing with the growth rates data on the value added of
primary, secondary, and tertiary industries of each province.
Due to space limitations, the visual map of decomposition
results was omitted here. Table 2 summarizes the statistical
information of industrial fluctuation cycle components and
trend terms by province.
As seen from Table 2, the fluctuations of each industry in
each province include four periodic components and one
trend term reflecting the development trend of industries. In
addition, only a few trend terms were statistically correlated
with the corresponding original sequence or had a greater
–1
0
1
2
3
1978 1988 1998 2008 2018
(j)
6
8
10
12
14
1978 1988 1998 2008 2018
(k)
6
8
10
12
14
1978 1988 1998 2008 2018
(l)
6
8
10
12
14
1978 1988 1998 2008 2018
(m)
Figure 2: Economic fluctuation components and trend items of various provinces (a). IMF1 of Heilongjiang (b). IMF1 of Jilin (c). IMF1 of
Liaoning (d). IMF2 of Heilongjiang (e). IMF2 of Jilin (f). IMF2 of Liaoning (g). IMF3 of Heilongjiang (h). IMF3 of Jilin (i). IMF3 of Liaoning
(j). IMF4 of Heilongjiang (k). RES of Heilongjiang (l). RES of Jilin (m). RES of Liaoning.
Complexity 5
Table 1: Statistical features of the business cycle components and trend terms.
Province Component Average
period (year) Frequency Type Person correlation
coefficient Var. Contribution rate of
var. (%)
Impact
ranking
Heilongjiang
IMF 1 2.2 High Kitchin cycle 0.57∗∗∗ 2.27 51.95 1
IMF 2 7.0 Medium Juglar cycle 0.42∗∗∗ 0.03 0.76 5
IMF 3 21.0 Low Kuznets cycle 0.70∗∗∗ 0.69 15.69 3
IMF 4 41.0 Low Kondratiev cycle 0.74∗∗∗ 0.59 13.59 4
RES ∞Low 0.60∗∗∗ 0.79 18.01 2
Jilin
IMF 1 4.2 High Political
economic cycle 0.64∗∗∗ 9.90 48.10 1
IMF 2 10.5 Medium Juglar cycle 0.46∗∗∗ 5.91 28.73 2
IMF 3 14.0 Low Kuznets cycle 0.55∗∗∗ 3.23 15.70 3
RES ∞Low 0.33∗∗ 1.54 7.47 4
Liaoning
IMF 1 5.3 High Political
economic cycle 0.53∗∗∗ 5.83 33.51 2
IMF 2 8.4 Medium Juglar cycle 0.57∗∗∗ 3.23 18.58 3
IMF 3 21.0 Low Kuznets cycle 0.69∗∗∗ 7.74 44.44 1
RES ∞Low 0.36∗∗ 0.61 3.48 4
Note. Asterisks mean that the figure is statistically significant; ∗,∗∗, and ∗∗∗ indicate P<0.1, P<0.05, and P<0.1, respectively.
Table 2: Statistical features of components and trend terms of industrial cycle.
Province Industry Component Average period
(year) Type Person correlation
coefficient
Contribution rate of var.
(%)
Heilongjiang
Primary
industry
IMF 1 2.1 Kitchin cycle 0.78∗∗∗ 60.75
IMF 2 4.7 Political economic
cycle 0.47∗∗∗ 28.48
IMF 3 10.5 Juglar cycle 0.25 10.00
IMF 4 21.0 Kuznets cycle 0.05 0.26
RES ∞0.03 0.51
Secondary
industry
IMF 1 2.2 Kitchin cycle 0.67∗∗∗ 61.71
IMF 2 4.7 Political economic
cycle 0.46∗∗∗ 10.03
IMF 3 8.4 Juglar cycle 0.58∗∗∗ 5.53
IMF 4 14.0 Kuznets cycle 0.59∗∗∗ 12.44
RES ∞0.54∗∗∗ 10.28
Tertiary
industry
IMF 1 3.0 Kitchin cycle 0.72∗∗∗ 45.20
IMF 2 8.4 Juglar cycle 0.12 19.86
IMF 3 14.0 Kuznets cycle 0.58∗∗∗ 30.19
IMF 4 21.0 Kuznets cycle 0.39∗∗ 4.24
RES ∞0.08 0.52
Jilin
Primary
industry
IMF 1 3.2 Kitchin cycle 0.73∗∗∗ 59.68
IMF 2 7.0 Juglar cycle 0.37∗∗ 33.32
IMF 3 14.0 Kuznets cycle 0.17 3.16
IMF 4 21.0 Kuznets cycle 0.13 0.17
RES ∞0.17 3.67
Secondary
industry
IMF 1 4.7 Political economic
cycle 0.77∗∗∗ 69.20
IMF 2 8.4 Juglar cycle 0.55∗∗∗ 7.96
IMF 3 14.0 Kuznets cycle 0.56∗∗∗ 10.96
IMF 4 21.0 Kuznets cycle 0.59∗∗∗ 4.92
RES ∞0.38∗∗ 6.97
Tertiary
industry
IMF 1 3.0 Kitchin cycle 0.57∗∗∗ 29.83
IMF 2 10.5 Juglar cycle 0.55∗∗∗ 48.55
IMF 3 14.0 Kuznets cycle 0.26 13.04
IMF 4 21.0 Kuznets cycle 0.12 6.32
RES ∞0.21 2.27
6Complexity
variance contribution rate. erefore, trend terms were not
used as the main research objects. Below is a discussion on
the consistency and heterogeneity of the characteristics of
industrial fluctuation cycles by province.
e fluctuation components of each industry in Hei-
longjiang Province include Kitchin (high frequency), Juglar
(midfrequency), and Kuznets cycles (midfrequency).
Among the three industries, the tertiary industry contains
two Kuznets cycles of 14 and 21 years, respectively. at is to
say, the fluctuation of the tertiary industry was significantly
affected by two different construction factors simulta-
neously. In addition, both primary and secondary industries
include a 4.7-year political and economic cycle. at is, the
fluctuations in primary and secondary industries were af-
fected by political and economic factors like government
changes. In combination with correlation coefficients and
variance contribution rates, it can be seen that the Kitchin
cycle always had the strongest correlation with the corre-
sponding original sequence and the largest impact on the
original sequence among the periodic components of in-
dustry fluctuations. In other words, the cyclical fluctuations
of various industries in Heilongjiang Province were most
affected by the inventory investment cycle.
e fluctuation components of each industry in Jilin
Province include a Juglar cycle and two Kuznets cycles of 14
and 21 years, respectively. Besides, high-frequency com-
ponents are slightly different. e high-frequency periodic
component of primary and tertiary industries is the Kitchin
cycle, while that of the secondary industry is the political
economic cycle. at is, the short-term fluctuations of
primary and tertiary industries in Jilin Province were mainly
affected by the market while those of the secondary industry
were mainly affected by political and economic factors since
the implementation of the reform and opening-up policy.
From correlation coefficients and variance contribution
rates, it can be seen that Kitchin, political economic, and
Juglar cycles had a major impact on primary, secondary, and
tertiary industries, respectively. In addition, Juglar, Kuznets,
and Kitchin cycles played a secondary role in primary,
secondary, and tertiary industries, respectively, which in-
dicated that high and midfrequency components exerted a
great impact on original industry fluctuations. at is to say,
the periodic fluctuations of various industries in Jilin
Province were significantly influenced by inventory in-
vestment, political economic, and equipment investment.
e fluctuation components of each industry in Liaoning
Province include Kitchin, Juglar, and Kuznets cycles. Among
the three industries, the tertiary industry contains two
Kuznets cycles of 14 and 21 years, respectively, the primary
industry also includes the political economic cycle, and the
secondary industry covers the Kondratiev cycle (low fre-
quency) as well. From the perspective of correlation coef-
ficients, every component was significantly correlated with
the original sequence except Juglar and Kuznets cycles in the
primary industry. From the perspective of variance con-
tribution rate, high-frequency periodic components
(Kitchin and political economic cycles) had the main impact
on primary industry fluctuations with a total variance
contribution rate of 93.36%. High- and low-frequency
components had a major impact on secondary industry
fluctuations with the sum of variance contribution rates
being 75.48%. e trend term of the tertiary industry had the
greatest impact on industrial fluctuations with a variance
contribution rate of 40.35%, while other components in total
had an influence degree of 59.65%, of which 50.44% came
from Kitchin and Kuznets cycles, indicating that inventory
investment, political economic, and construction cycles were
the main factors affecting the fluctuations of various in-
dustries in Liaoning Province in addition to its own de-
velopment trends.
4. Correlation Analysis of Industrial and
Business Cycle Components
e previous section decomposed the economic and in-
dustrial fluctuation sequences of each province and
Table 2: Continued.
Province Industry Component Average period
(year) Type Person correlation
coefficient
Contribution rate of var.
(%)
Liaoning
Primary
industry
IMF 1 3.2 Kitchin cycle 0.73∗∗∗ 66.16
IMF 2 5.3 Political economic
cycle 0.42∗∗∗ 27.20
IMF 3 10.5 Juglar cycle 0.23 2.96
IMF 4 21.0 Kuznets cycle 0.14 1.24
RES ∞0.20 2.44
Secondary
industry
IMF 1 3.8 Kitchin cycle 0.57∗∗∗ 38.89
IMF 2 8.4 Juglar cycle 0.56∗∗∗ 14.95
IMF 3 21.0 Kuznets cycle 0.67∗∗∗ 36.59
IMF 4 41.0 Kondratiev cycle 0.44∗∗∗ 9.56
RES ∞0.08 0.01
Tertiary
industry
IMF 1 2.6 Kitchin cycle 0.45∗∗∗ 21.23
IMF 2 7.0 Juglar cycle 0.63∗∗∗ 9.21
IMF 3 14.0 Kuznets cycle 0.68∗∗∗ 14.89
IMF 4 21.0 Kuznets cycle 0.60∗∗∗ 14.32
RES ∞0.53∗∗∗ 40.35
Note. Asterisks mean that the figure is statistically significant; ∗,∗∗, and ∗∗∗ indicate P<0.1, P<0.05, and P<0.1, respectively.
Complexity 7
compared and analyzed decomposition results by province.
Based on the above analysis, a study was carried out on the
correlation between the cyclical components of economic
and industrial fluctuations. First, the table of correlation
coefficients between the industrial and economic fluctuation
components of each province was obtained by using
MATLAB. en, line charts were drawn using correlation
information to intuitively analyze the relationship between
economic and industrial fluctuation components.
Table 3 shows the correlation between the economic and
industrial fluctuation components of Heilongjiang Province.
It can be seen that the Kitchin cycle was significantly pos-
itively related to the Kitchin and political economic cycles of
the secondary industry and especially more significantly
relevant (|r|�0.6) to the political economic cycle in terms of
the economic fluctuation components of Heilongjiang
Province. Kuznets and Kondratiev cycles were positively
correlated with the Kuznets cycle of every industry and the
Juglar cycle of the secondary industry (|r|>0.5). To deeply
study the impact of industrial fluctuation components on
economic cycle components, a comparison was made be-
tween the line charts of the economic and industrial fluc-
tuation components of each province.
Figure 3 shows some line charts of economic and in-
dustrial fluctuation components that were significantly
correlated in Heilongjiang Province. Figure 3(a) shows the
Kitchin cycle of economic fluctuations, and the Kitchin and
political economic cycles of the secondary industry. It can be
found from this graph that the Kitchin cycle of economic
fluctuations was more similar to the political economic cycle
of the secondary industry before the 1990s and the Kitchin
cycle of the secondary industry after the 1990s, and industry
fluctuations were always ahead of economic ones. e
reason is that the economic system of China was officially
changed from a planned economy system to a socialist
market one in 1992. Government decisions played a decisive
role in economic development during the implementation of
the planned economy system, while the market mechanism
played a major role after 1992. As seen from Table 1, the
Kitchin cycle of economic fluctuations in Heilongjiang
Province had a variance contribution rate as high as 51.95%,
which exerted a major impact on economic fluctuations. It
can be seen from Table 2 that the variance contribution rates
of the Kitchin and political economic cycles of the secondary
industry in Heilongjiang Province accounted for more than
70% of the fluctuations of the secondary industry, indicating
that the economic fluctuations in Heilongjiang Province
were mainly caused by the fluctuations of the secondary
industry. Furthermore, the political economic cycle was
dominant before the 1990s, while the Kitchin cycle was
dominant after the 1990s.
Figure 3(b) shows the line charts of the Kuznets cycles of
economic and primary industry fluctuations, the Juglar and
Kuznets cycles of secondary industry fluctuations and the
Kuznets cycle of the tertiary industry. As seen from Figure 3
and the correlation coefficients in Table 3, the Kuznets cycle
of economic fluctuations was most relevant to the Juglar
cycle of the secondary industry, and industry fluctuations
were ahead of economic ones, indicating that the Kuznets
cycle of economic fluctuations in Heilongjiang Province was
mainly caused by the Juglar cycle of the secondary industry.
In addition, the Kuznets cycles of various industries had
some influence on it, whose effects were the most significant
in the early 21st century. e Kuznets cycle of economic
fluctuations showed a trend of steady growth at the be-
ginning of the 21st century due to the combined action of a
significant decline in the Juglar cycle of the secondary in-
dustry and the growth of the Kuznets cycle of each industry.
e Kondratiev cycle of economic fluctuations was the
same as the Kuznets cycle of economic fluctuations, which
was significantly related to the Kuznets cycle of primary
industry fluctuations, the Juglar and Kuznets cycle of sec-
ondary industry fluctuations as well as the Kuznets cycle of
tertiary industry fluctuations. Figure 3(c) is the line chart of
corresponding components. Related industrial fluctuation
components were the same, but two economic fluctuation
components and their relationship with industrial ones
showed different trends. e average growth rate of the
Kondratiev cycle of economic fluctuations was higher than
that of the Kuznets cycle, and the trend of the Kondratiev
cycle of economic fluctuations was more stable. In addition,
it was mainly affected by the Juglar and Kuznets cycles of the
secondary industry. at is to say, the Kondratiev cycle of
economic fluctuations in Heilongjiang Province was mainly
caused by the Juglar and Kuznets cycles of secondary in-
dustry fluctuations.
Table 4 shows the correlation between the economic and
industrial fluctuation components of Jilin Province. It can be
seen that the political economic cycle of economic fluctu-
ations in Jilin Province was strongly positively related to the
Kitchin cycle of primary industry fluctuations and that of the
secondary industry (|r|>0.5). e Juglar cycle of eco-
nomic fluctuations was positively related to those of various
industrial fluctuations and negatively related to the
Kuznets cycle of primary industry fluctuations but only
strongly correlated with those of secondary and tertiary
industries (|r|>0.5). e Kuznets cycle of economic fluc-
tuations was significantly positively associated with those of
the fluctuations of the three industries. Furthermore, the
strongest correlation came from the Kuznets cycle of sec-
ondary industry fluctuations (|r|>0.8), followed by those of
tertiary and finally primary industry ones (|r|<0.5).
Figure 4 shows the line charts of economic and industrial
fluctuation components that were significantly correlated in
Jilin Province. Figure 4(a) is the line charts of the political
economic cycles of economic and secondary industry
fluctuations and the Kitchin cycle of primary industry
fluctuations. It can be seen that the trend of the political
economic cycle of economic fluctuations was similar to that
of the secondary industry in Jilin Province, and the fluc-
tuations of industrial components were ahead of those of
economic components. e trend of the political economic
cycle of economic fluctuations was similar to the Kitchin
cycle of the primary industry, but the amplitudes of com-
ponents were quite different. at is to say, the political
economic cycle of economic fluctuations in Jilin Province
was mainly brought by that of the secondary industry, and
the fluctuation range was affected by the Kitchin cycle of the
8Complexity
primary industry, especially before the 21st century. e
reason is that economic development mainly relied on the
heavy industry as an important member of the old industrial
base in Northeast China at the beginning of the reform and
opening up and for a long time afterward. As a member of
the main grain-producing areas in China, the primary in-
dustry also had a great impact on economic development.
Figure 4(b) shows the line charts of the Juglar cycle of
economic and primary industry fluctuations and the
Kuznets and Juglar cycles of secondary industry fluctu-
ations as well as the Juglar cycle of tertiary industry
fluctuations. It can be seen from Table 4 that the Juglar
cycle of economic fluctuations in Jilin Province had the
strongest correlation with the Juglar cycles of secondary
and tertiary industries, and economic fluctuation com-
ponents were weakly correlated with other industries. In
addition, industrial fluctuations were ahead of economic
ones. at is to say, the Juglar cycle of economic fluc-
tuations in Jilin Province was mainly caused by those of
secondary and tertiary industries and slightly attributed to
that of the primary industry.
e Kuznets cycle of economic fluctuation was signifi-
cantly related to those (a total of six) of primary, secondary,
and tertiary industries. According to the correlation between
the components of each industry and the Kuznets cycle of
economic fluctuations in Table 4, it can be seen that the
Kuznets cycle of the secondary industry had the greatest
impact on economic fluctuations, followed by those of
tertiary and finally primary industries. Figure 4(c) shows the
line charts of components. It can be seen that the trends and
amplitudes of Kuznets cycle of economic fluctuations were
strongly consistent with the Kuznets cycle of the secondary
industry while the Kuznets cycle of the tertiary industry
gradually approached economic fluctuations after the 21st
century, suggesting that the Kuznets cycle of economic
fluctuations in Jilin Province was mainly affected by that of
the secondary industry between the implementation of the
reform and opening-up policy to the 21st century. e
tertiary industry played an increasing role in the develop-
ment of economy with the transformation of industrial
structure.
Table 5 shows the correlations between economic and
industrial fluctuation components in Liaoning Province. It
can be seen that the political economic cycle of economic
fluctuations was strongly positively related (|r|�0.9) to the
Kitchin cycle of the secondary industry. e Juglar cycle of
economic fluctuations was significantly positively correlated
with that of secondary industry fluctuations and related to
the Kitchin cycles of the three industries, the 14-year
Kuznets cycle of the tertiary industry, and the political
economic and Juglar cycles of the primary industry. Eco-
nomic and Juglar cycles were significantly related, but the
coefficient was slightly lower (|r|<0.5). e Kuznets cycle of
economic fluctuations was significantly positively correlated
with that of the three industries and the Kondratiev cycle of
the secondary industry and strongly related to those of
primary and secondary industries (|r|>0.9).
Figure 5 shows the line charts of economic and industrial
fluctuation components significantly correlated with eco-
nomic ones in Liaoning Province. Figure 5(a) is a line chart
of the political economic cycle of economic fluctuations and
the Kitchin cycle of secondary industry fluctuations. Table 5
shows that only the Kitchin cycle of the secondary industry
was significantly related to the political economic cycle of
economic fluctuations among all industrial fluctuation
components, and the correlation coefficient was as high as
0.9. It can be seen from line charts that the changes of
industrial fluctuation components were ahead of those of
economic ones, indicating that the political economic cycle
of economic fluctuations was mainly caused by the sec-
ondary industry in Liaoning Province.
Table 5 shows that industrial fluctuation components
related to the Juglar cycle of economic fluctuations include
Kitchin and political economic cycles, the Juglar cycle of
primary industry fluctuations, the Kitchin, and Juglar cycles
of secondary industry fluctuations as well as the Kitchin and
Kuznets cycles of tertiary industry fluctuations. To show key
information clearly, Figure 5(b) only shows the political
economic cycle of primary industry fluctuations, the Juglar
cycle, and the Juglar cycles of economic and secondary
industry fluctuations. e political economic and Juglar
cycles of the primary industry were negatively related to the
Table 3: Correlation between industrial and the economic cycle components of Heilongjiang Province.
Industrial Industrial component Economic component
Kitchin cycle Juglar cycle Kuznets cycle Kondratiev cycle
Primary industry
Kitchin cycle 0.19 0.08 0.04 0.03
Political economic cycle −0.21 0.20 0.09 0.04
Juglar cycle −0.09 0.15 0.08 −0.04
Kuznets cycle −0.02 0.02 0.61∗∗∗ 0.68∗∗∗
Secondary industry
Kitchin cycle 0.37∗∗ 0.12 0.01 −0.03
Political economic cycle 0.60∗∗∗ 0.34∗∗ 0.09 0.03
Juglar cycle 0.09 0.22 0.82∗∗∗ 0.70∗∗∗
Kuznets cycle −0.02 0.06 0.75∗∗∗ 0.86∗∗∗
Tertiary industry
Kitchin cycle 0.14 −0.04 0.01 0.02
Juglar cycle 0.07 −0.40∗∗∗ −0.08 0.02
Kuznets cycle 0.05 0.24 0.03 −0.10
Kuznets cycle −0.02 0.20 0.65∗∗∗ 0.31∗
Note. Asterisks mean that the figure is statistically significant; ∗,∗∗, and ∗∗∗ indicate P<0.1, P<0.05, and P<0.1, respectively.
Complexity 9
political economic cycle of economic fluctuations. As seen
from the figure, the reason for this negative correlation is
that industrial fluctuations lagged behind economic ones,
indicating that economic fluctuations brought about the
fluctuations of components in the primary industry of
Liaoning Province and the peaks (troughs) of the industrial
fluctuation cycle with the last troughs (peaks) of the eco-
nomic fluctuation cycle being in the same period. However,
the fluctuation trends of components were similar, but the
correlation between sequences was still low due to the
differences in the frequency of fluctuations between the
Kitchin and Kuznets cycles of each industry and economic
fluctuation components. Only the Juglar cycle of the sec-
ondary industry was strongly related to that of economic
fluctuations, indicating that the Juglar cycle of economic
fluctuations was mainly caused by that of the secondary
industry.
Figure 5(c) is a line chart of the Kuznets cycle of
economic fluctuations, the Kuznets cycles of the fluctu-
ations of various industries, and the Kondratiev cycle of
secondary industry fluctuations. As seen from the graph
and Table 5, the Kuznets cycles of primary and secondary
industries had the strongest connection with the Kuznets
cycle of economic fluctuations, indicating that the
Year
–10
–6
–2
2
6
10
Kitchin cycle of economy
(T = 2.2)
Kitchin cycle of secondary
industry (T = 2.2)
Political economic cycle of
secondary industry (T = 4.7)
1978 1983 1988 1993 1998 2003 2008 2013 2018
(a)
–3
–2
–1
0
1
2
3
Kuznets cycle of economy
(T = 21.0)
Kuznets cycle of primary
industry (T = 21.0)
Juglar cycle of secondary
industry (T = 8.4)
Kuznets cycle of secondary
industry (T = 14.0)
Kuznets cycle of tertiary
industry (T = 21.0)
Year
1978 1983 1988 1993 1998 2003 2008 2013 2018
(b)
Kondratiev cycle of
economy (T = 41.0)
Kuznets cycle of primary
industry (T = 21.0)
Juglar cycle of secondary
industry (T = 8.4)
Kuznets cycle of secondary
industry (T = 14.0)
Kuznets cycle of tertiary
industry (T = 21.0)
–3
–2
–1
0
1
2
3
Year
1978 1983 1988 1993 1998 2003 2008 2013 2018
(c)
Figure 3: Line charts of the economic and industrial components in Heilongjiang Province. (a) Kitchin cycle of economy and related
industry components. (b) Kuznets cycle of economy and related industry components. (c) Kondratiev cycle of economy and related industry
components.
10 Complexity
Kuznets cycle of economic fluctuations in Liaoning
Province was mainly determined by that of primary and
secondary industrial fluctuations. e amplitudes of the
Kuznets cycle of the tertiary industry and the trend term
of the Kuznets cycle of economic fluctuations were dif-
ferent before the 21st century but gradually converged
after the 21st century, which is because the government
put forward the strategy of “Revitalizing the Old Indus-
trial Base in the Northeast” to solve the “Northeast
phenomenon” at the beginning of the 21st century. e
northeast region began to attach importance to devel-
oping the tertiary industry, especially the field of pro-
duction service. erefore, the components of tertiary
industry fluctuations gradually approached the fluctua-
tions of economic components after the 21st century.
Below is an analysis of the similarities and differences
between economic and industrial fluctuations across the
three provinces, each of which has three types of economic
fluctuation components, namely, high-, mid-, and low-
frequency components. Firstly, the high-frequency
components (short-term business cycles) of economic
fluctuations in the three provinces were mainly affected by
the secondary industry. e difference is that the short-
term business cycle of Heilongjiang Province was affected
by the Kitchin and political economic cycles of the sec-
ondary industry simultaneously, and the short-term
business cycles of Jilin and Liaoning Provinces were only
affected by the political economic and Kitchin cycles of the
secondary industry respectively, suggesting that the short-
term business cycle of Heilongjiang Province was affected
by the combined effect of the market and government, and
the market played a more obvious role from the late 1990s;
the short-term business cycle of Jilin Province was mainly
affected by government behavior; the short-term business
cycle of Liaoning Province was mainly brought about by
the factor of the market. e fluctuation ranges of short-
term business cycles in Heilongjiang, Liaoning, and Jilin
provinces increased sequentially, indicating that the
participation of both the market and government led to
the smallest fluctuation range of short-term business cycle
and contributed most to the economy.
Secondly, the midfrequency components (midterm
business cycles) of economic fluctuations in the three
northeastern provinces of China are all Juglar cycles. As
mentioned above, the midterm business cycle of Hei-
longjiang Province played a negligible role in economic
fluctuations, which was thus not considered here. e
midterm business cycle of Jilin Province was mainly brought
about by the Juglar cycles of secondary and tertiary in-
dustries, and the midterm business cycle of Liaoning
Province was mainly affected by the Juglar cycle component
of the secondary industry, indicating that the investment
change of equipment in secondary and tertiary industries
brought about the midterm business cycle in Jilin Province,
the investment change of equipment in the secondary in-
dustry gave rise to the midterm business cycle in Liaoning
Province, and the investment change of equipment in the
primary industry had no effect on the value of output
without resulting in economic fluctuations. As a matter of
fact, investment in the equipment of the primary industry
increased labor productivity but had no impact on the value
of output.
Finally, the low-frequency components (long-term
business cycles) of economic fluctuations in the three
northeastern provinces of China are mainly Kuznets cycles.
e same point of the three provinces is that the Kuznets
cycle of the tertiary industry played an increasingly im-
portant role in their long-term business cycles after the 21st
century, suggesting that the transformation of industrial
structure effectively shifted economic development from
“industrial dominance” to “multipoint support” and im-
proved the stability of economic development. e differ-
ence is that the long-term business cycle of Heilongjiang
Province was most affected by the Juglar and Kuznets cycles
of the secondary industry, demonstrating that continuous
investment in the equipment and construction of the sec-
ondary industry was the key to improving the stability of the
long-term business cycle in Heilongjiang Province. e
long-term business cycles of Jilin and Liaoning Provinces
were mainly affected by the Kuznets cycle of the secondary
industry (T�14). at is to say, stable investment in the
construction of secondary and tertiary industries kept the
Table 4: Correlation between industrial and the economic cycle components of Jilin Province.
Industrial Industrial component Economic component
Political economic cycle Juglar cycle Kuznets cycle
Primary industry
Kitchin cycle 0.64∗∗∗ −0.05 −0.03
Juglar cycle −0.13 0.40∗∗ −0.02
Kuznets cycle 0.00 −0.41∗∗∗ 0.37∗∗
Kuznets cycle −0.09 −0.14 0.45∗∗∗
Secondary industry
Political economic cycle 0.59∗∗∗ 0.20 0.09
Juglar cycle −0.04 0.75∗∗∗ 0.21
Kuznets cycle 0.01 0.08 0.88∗∗∗
Kuznets cycle 0.03 0.05 0.89∗∗∗
Tertiary industry
Kitchin cycle 0.21 −0.05 −0.11
Juglar cycle −0.05 0.83∗∗∗ −0.08
Kuznets cycle −0.05 0.01 0.71∗∗∗
Kuznets cycle −0.08 −0.15 0.47∗∗∗
Note. Asterisks mean that the figure is statistically significant; ∗,∗∗, and ∗∗∗ indicate P<0.1, P<0.05, and P<0.1, respectively.
Complexity 11
long-term business cycle more stable in Jilin and Liaoning
Provinces.
5. Discussion
Complex and changeable economic systems are character-
ized by nonstationarity and nonlinearity. As a result, re-
searchers always deal with the data in a linear system when
analyzing the economic sequence in an economic system in
order to facilitate quantification and often transform non-
stationary time series into stationary ones before adopting
existing time series methods. e difficulty of the analysis
process is reduced, but the effectiveness of analysis depends
too much on the stationarity and linear assumption of data,
which often leads to the loss of some information and
changes the economic meaning of data. In contrast, the
CEEMDAN algorithm puts forward no requirements for the
stability and linearity of data, loses no information, and
obtains more objective results, effectively avoiding the
shortcomings of the nonlinear cointegration theory that is
too complicated with weak economic significance [38].
e CEEMDAN algorithm was selected to deal with the
time series of economic and industrial fluctuations since the
reform and opening up, and a detailed analysis was carried
Political economic cycle of economy (T = 4.2)
Kitchin cycle of primary industry (T = 3.2)
Political economic cycle of secondary industry (T = 4.7)
1983 1988 1993 1998 2003 2008 2013 20181978
Year
–24
–16
–8
0
8
16
24
(a)
8
Juglar cycle of economy
(T = 10.5)
Juglar cycle of primary
industry (T = 7.0)
Kuznets cycle of primary
industry (T = 14.0)
Juglar cycle of secondary
industry (T = 8.4)
Juglar cycle of tertiary
industry (T = 10.5)
–24
–16
–8
0
16
1983 1988 1993 1998 2003 2008 2013 20181978
Year
(b)
Kuznets cycle of economy
(T = 14.0)
Kuznets cycle of primary
industry (T = 14.0)
Kuznets cycle of primary
industry (T = 21.0)
Kuznets cycle of secondary
industry (T = 14.0)
Kuznets cycle of secondary
industry (T =21.0)
Kuznets cycle of tertiary
industry (T = 14.0)
Kuznets cycle of tertiary
industry (T = 21.0)
1983 1988 1993 1998 2003 2008 2013 20181978
Year
–6
–4
–2
0
2
4
6
(c)
Figure 4: Line charts of the economic and industrial components in Jilin Province. (a) Political and economic cycle of economy and related
industry components. (b) Juglar cycle of economy and related industry components. (c) Kuznets cycle of economy and related industry
components.
12 Complexity
Table 5: Correlation between industrial and economic cycle components of Liaoning Province.
Industrial Industrial component Economic component
Political economic cycle Juglar cycle Kuznets cycle
Primary industry
Kitchin cycle 0.16 0.27∗−0.04
Political economic cycle 0.12 −0.50∗∗∗ 0.03
Juglar cycle −0.02 −0.27∗0.14
Kuznets cycle −0.13 0.01 0.91∗∗∗
Secondary industry
Kitchin cycle 0.90∗∗∗ 0.26∗−0.07
Juglar cycle 0.13 0.98∗∗∗ 0.18
Kuznets cycle −0.08 0.11 0.96∗∗∗
Kondratiev cycle −0.11 0.04 0.48∗∗∗
Tertiary industry
Kitchin cycle 0.13 0.28∗0.00
Juglar cycle 0.11 0.12 0.24
Kuznets cycle 0.12 0.38∗∗ 0.35∗∗
Kuznets cycle 0.01 0.10 0.59∗∗∗
Note. Asterisks mean that the figure is statistically significant; ∗,∗∗, and ∗∗∗ indicate P<0.1, P<0.05, and P<0.1, respectively.
–8
–4
0
4
8
1983 1988 1993 1998 2003 2008 2013 20181978
Year
Political economic cycle of economy (T = 5.3)
Kitchin cycle of secondary industry (T = 3.8)
(a)
–12
–8
–4
0
4
8
12
1983 1988 1993 1998 2003 2008 2013 20181978
Year
Juglar cycle of economy (T = 8.4)
Political economic cycle of primary industry (T = 5.3)
Juglar cycle of primary industry (T = 10.5)
Juglar cycle of secondary industry (T = 8.4)
(b)
Kuznets cycle of economy
(T = 21.0)
Kuznets cycle of primary
industry (T = 21.0)
Kuznets cycle of secondary
industry (T = 21.0)
Kuznets cycle of tertiary
industry (T = 14.0)
Kuznets cycle of tertiary
industry (T = 21.0)
–9
–5
–1
3
7
1983 1988 1993 1998 2003 2008 2013 20181978
Year
(c)
Figure 5: Line charts of economic and industrial components in Liaoning Province. (a) Political and economic cycle of economy and related
industry components. (b) Juglar cycle of economy and related industry components. (c) Kuznets cycle of economy and related industry
components.
Complexity 13
out on the characteristics of these fluctuations in the three
northeastern provinces of China. With regard to economic
fluctuations, all provinces include high-, mid-, and low-
frequency components, indicating the existence of three
types of cycle components, namely, short-, mid-, and long-
term business cycles. Both Heilongjiang and Jilin Provinces
took the short-term business cycles as the main component,
which had around 50% of the impact on economic fluctu-
ations. Economic fluctuations in Liaoning Province took a
long-term business cycle as the main component, which had
nearly 50% of the impact on economic fluctuations. Re-
garding industrial fluctuations, the med- and low-frequency
components of industrial fluctuations in the three provinces
are common but still show some differences. Table 6
summarizes the main fluctuation components of all in-
dustries in the three northeastern provinces of China. e
main component of the primary industry is the Kitchin cycle
in each province, exhibiting that the fluctuation of the
primary industry was mainly affected by the investment of
inventory. e main component of the secondary industry is
the Kitchin cycle for Heilongjiang Province, the political
economic cycle for Jilin Province, and Kitchin and Kuznets
cycles for Liaoning Province, showing that the main factors
for the fluctuation of the secondary industry in Heilongjiang,
Jilin, and Liaoning Provinces were the inventory investment
cycle and political and economic factors as well as the in-
vestment of inventory and construction, respectively. e
main component of the tertiary industry is the Kitchin cycle
for Heilongjiang Province, the Juglar cycle for Jilin Province,
and the Kitchin cycle for Liaoning Province. However, the
Kitchin cycle only brought about 20% of the impact on
fluctuations more affected by the trend item in Liaoning
Province, indicating that the main component of the tertiary
industry was affected by inventory investment in Hei-
longjiang Province, equipment investment in Jilin Province,
and the trend item in Liaoning Province.
It can be seen that certain differences exist in the business
cycle and industrial fluctuation components of Heilongjiang,
Jilin, and Liaoning Provinces. erefore, accurate results
may provide a reference for the healthy and orderly de-
velopment of the regional economy. However, the research
results are less precise and not specific due to the annual data
and simple division of industries. Next, more specific results
were obtained by starting with higher-frequency data
(quarterly or monthly data) and segmented industries.
6. Conclusion
e components of economic and industrial fluctuations
were measured by taking the three provinces of Northeast
China as research areas, taking CEEMDAN as a research
method, and using the time series data of GDP and in-
dustrial growth rates from 1978 to 2018. Besides, the rela-
tionship between industrial and economic fluctuations was
analyzed. is paper aimed to understand economic and
industrial fluctuations and their relationship so as to provide
a valuable reference for the sustainable economic develop-
ment of the three northeastern provinces. e main con-
clusions of this research are as follows:
(1) Since the implementation of the reform and open-
ing-up policy, the economic fluctuations in the three
northeastern provinces of China have almost been
free from the influence of the primary industry and
most affected by the development of the secondary
industry. With the transformation of industrial
structure, the tertiary industry gradually had an
influence on economic fluctuations after the 21st
century.
(2) e short-term business cycles of the three north-
eastern provinces were all affected by the develop-
ment of the secondary industry. To be specific, the
short-term business cycle of Heilongjiang Province
was affected by the combined effect of the market
and government on the secondary industry, with the
market playing a more obvious role after the 1990s;
the short-term business cycle of Jilin Province was
mainly affected by the government activities of the
secondary industry; the short-term business cycle of
Liaoning Province was mainly driven by the market
factors of the secondary industry. And the fluctua-
tion of the short-term business cycle was the smallest
and economy was the most stable for the three
northeastern provinces when the market and gov-
ernment participated in developing the secondary
industry simultaneously.
(3) e midterm business cycles of Jilin and Liaoning
Provinces are both Juglar cycles (the midterm
business cycle of Heilongjiang Province played a
negligible role in economic fluctuations, which was
thus not considered). In addition, the midterm
Table 6: Main fluctuation components of each industry in the three northeastern provinces (except trend items).
Province Industry
Primary industry Secondary industry Tertiary industry
Heilongjiang Kitchin cycle Kitchin cycle Kitchin cycle
Jilin Kitchin cycle Political economic cycle Juglar cycle
Liaoning Kitchin cycle Kitchin cycle and Kuznets cycle Kitchin cycle
14 Complexity
business cycle of Jilin Province was affected by the
investment of equipment in secondary and tertiary
industries, and that of Liaoning Province was af-
fected by the investment of equipment in the sec-
ondary industry.
(4) e long-term business cycles of the three north-
eastern provinces are dominated by the Kuznets
cycle. e difference is that the long-term business
cycle of Heilongjiang Province was affected by the
Juglar and Kuznets cycles of the secondary industry,
while those of Jilin and Liaoning Provinces were
mainly affected by the Kuznets cycle of the secondary
industry (T�14), showing that the key to main-
taining the stability of long-term business cycle in
Heilongjiang Province was the equipment invest-
ment of the secondary industry and the equipment
and construction investment of the tertiary industry,
and the key to maintaining the stability of long-term
business cycles in Jilin and Liaoning Provinces was
the construction investment of secondary and ter-
tiary industries.
Data Availability
e data used to support the findings of this study are
available from the corresponding author upon request.
Conflicts of Interest
All authors declare that they have no conflicts of interest.
Acknowledgments
is research was funded by Chinese National Funding of
Social Sciences (16BJL032).
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