Open access peer-reviewed chapter

# Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development

Written By

Yanyan Zheng, Tong Shu, Shou Chen and Kin Keung Lai

Submitted: June 8th, 2018 Reviewed: December 23rd, 2018 Published: October 23rd, 2019

DOI: 10.5772/intechopen.83801

From the Edited Volume

## Time Series Analysis

Edited by Chun-Kit Ngan

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## Abstract

Foreign direct investment (FDI) is one of the important factors affecting China’s economic development, the prediction of which is the basis of its development and decision-making. Based on elaborating the significant role in China’s economic growth and the status quo of utilizing foreign investment over the period between 2000 and 2016, this chapter attempts to construct Gray-Markov model (GMM) and time series model (TSM) to forecast the trend of China’s utilization of FDI and then compares the precision of two different prediction models to obtain a better one. Results indicate that although it is qualified, traditional Gray model needs to be optimized; GMM is built to help modify the result, improve Gray-related degrees, and narrow the gap with real value. Comparing the accuracy of GMM with that of TSM, we can conclude that the fitting effect of GMM is better. To increase the credibility of these results, this chapter is based on the data of Beijing and Chongqing from 1990 till 2016, also verifying that the fitting effect of GMM is superior to that of the TSM. Then, we can safely draw a conclusion that the prediction model of GMM is more credible, which has a certain referencing value for the utilization of FDI.

### Keywords

• foreign direct investment (FDI)
• Gray-Markov model (GMM)
• time series model (TSM)

## 1. Introduction

In the light of the definition of the International Monetary Fund (IMF) and the Organization for Economic Cooperation and Development, foreign direct investment (FDI) is an investment in the form of a controlling ownership in a business in one country by an entity based in another country. The primary purpose of the host country in attracting FDI is to promote the country’s economic development and industrial upgrading. This will facilitate domestic enterprises to improve their technology and quality, gradually supporting the development of foreign enterprises to enter the global value chain [1]. Influencing the supply chain system, FDI has significantly promoted the sound and rapid development of the national economy. Therefore, it is necessary to focus on the future tendency of FDI in the supply chain system when we investigate the transformation and innovation of Chinese economy.

Since the late 1970s, FDI attracted by China has been steadily increasing, regardless of the changes and fluctuation of the international economic environment and the total flow of FDI globally. Statistically, over the period from 1979 to 2010, China’s actual use of FDI amounted to $1048.31 billion [2], and FDI keeps a rapid growth. According to the data of Ministry of Commerce of the People’s Republic of China (PRC) (Figure 1), the FDI in China presented a rising trend over the period from 1990 to 2016. The vital roles in the economic development of China are as follows. Firstly, the proportion of basic industries in China declines generally, and the proportion of agricultural output drops by 18% over the period between 1978 and 2011 [3]. Secondly, for a long time, FDI mainly concentrates in secondary and tertiary industries, accelerating the restructuring and upgrading of China’s industries [4]. Finally, FDI provides investment capital and promotes the rapid development of China’s import and export trade, improving China’s status in international trade. Due to the remarkable role of FDI, a multitude of scholars began to track and study the FDI in developing countries, build analytical framework, and launch a new field of research of FDI in developing countries. The statistics shows that China has become an emerging market for FDI. Dees indicates that FDI has positive effects on the GDP, technological progress, and the improvement of management system [5]. Nourzad considers that FDI promotes economy development through technology transfer [6], while Mah argues that the latter one promotes the former one [7]. Taking the reform policy (implemented in July 2005) as the boundary, Pan and Song explore the impact of the effective exchange rate of RMB on FDI [8]. Research shows that they are in a long-term equilibrium relationship before implementing reform policy. After the policy, the exchange rate of RMB has the Granger causality for FDI, and the appreciation of RMB can promote the flow of FDI. Additionally, De Mello shows that FDI can increase the added value associated with it [9]. Based on the data from 1971 to 2012, Dreher et al. conclude that the membership in international organizations is an essential and decisive factor of FDI liquidity and has a promoting effect on FDI mobility [10]. Badr and Ayed do a quantitative study of the relationship between FDI and economic development in South American countries, and they find that FDI can be determined by some economic factors, having no important effect on economic development [11]. Kathuria et al. apply panel data to examining the effectiveness of public policy in attracting FDI [12]. Lin et al. divide the FDI company into five strategies [13]; Brülhat and Schmidheiny estimate the rivalness of state-level inward FDI [14]. The trend of FDI in the future is an important reference for China’s economic development. However, much literature focuses on the development of FDI itself and its influencing factors, and there is little research on the future development. This is what we do in this chapter. Currently, the predictive analysis model for economic and trade development can be divided into linear prediction method and nonlinear prediction one. The linear prediction method mainly includes historical average level prediction method, time series prediction method, and Kalman filter prediction method, to name just a few. The nonlinear prediction methods concern Gray theory, Markov chain, support vector machine, and boom prediction method. The historical average prediction algorithm is simple and easy to understand and the parameters can be estimated by using the least squares method. However, it is too simple to accurately reflect the randomness and nonlinearity, and therefore it cannot be applied to unexpected events. The Kalman filter uses the flexible recursive state space model, with the advantages of linear, unbiased, and minimum mean variance. Nevertheless, because the Kalman filter prediction model belongs to the linear model, its performance becomes worse in the nonlinearity and uncertainty [15]. The time series model is simple in modeling, with high prediction accuracy in the case of full historical data. The Gray model can be modeled with less information, handling data easily and having higher accuracy, which can be extensively used in several fields [15, 16, 17, 18]. However, Gray model becomes less attractive for time series with large stochastic fluctuation. Markov stochastic process predicts the development and changes of dynamic system according to the transfer probability of different states, and the transfer probability reflects the influence degrees of various stochastic factors and the internal law of the transition states. Therefore, it is more suitable to predict the problems with large stochastic fluctuation. What cannot be ignored is that Markov model requires data to meet the characteristics of no effect. Consequently, when using a simple model, it is very difficult to obtain a better prediction result, and the combination method becomes a popular method. Through the vector autoregressive moving average (VARMA), Bhattacharya et al. compare and analyze the consumer price index sequence (CPI) and improve the forecasting accuracy [16]. The Gray model (proposed by a Chinese scholar, Professor Deng) and the Markov model (proposed by a Russian mathematician, Markov) have been combined very early, which is called Gray-Markov model (GMM). Based on the Gray prediction model, GMM is used to solve the inaccurate problems resulting from the large random fluctuation of the data and widely promoted in the fields of financial economy, agricultural economy, and resource and energy [17, 18, 19, 20]. On the basis of GM(1,1), Li et al. propose an improved GM(2,1) model [21]. Based on the model of GM(1,1) and Markov stochastic process and combining Taylor formula approximation method, Li et al. construct a model of T-MC-RGM(1,1) and verify its validity by the example of thermal power station in Japan [22]. The level of FDI in China is influenced by many factors such as fixed investment, laws and regulations, corporate culture, innovation ability, and financial market stability, among others. To clearly recognize and describe the role of FDI, the foreign investment system is abstracted as a Gray system with no physical prototype and incomplete information, which can be predicted with GM(1,1) model. Meanwhile, the FDI level in the previous year has no direct influence on that in the next year, in line with the no-effect characteristic of Markov stochastic process. On the basis of the previous study of Gray-Markov model, it is used to predict the tendency of FDI in China, addressing the shortcomings of the Gray model for the low precision of the data sample with large fluctuation and compensating for the limitation that the Markov model requires the data to have a smooth process. As a comparison, the time series prediction model is introduced to evaluate FDI. Then, the fitting results are compared to decide the optimal prediction model. Advertisement ## 2. Gray-Markov model Gray-Markov model is a forecasting method integrating the Gray theory with the Markov theory [17, 18, 19, 20, 21, 22, 23, 24, 25]. Firstly, GM(1,1) is constructed to obtain the predicted residual value. Then, the error state can be divided according to the residual values, and the error state can be obtained in light of the Markov prediction model. Then, based on the error state and transition matrix, the predicted sequence from GM(1,1) can be adjusted to obtain more precise predicting internals. The traditional GM(1,1) has its advantage in short-term prediction, while it has a poor fitting effect in forecasting the long-range and fluctuating data series. And the benefit of Markov stochastic process is the prediction of the large data series with random volatility. GMM has been proposed by He to predict the yield of cocoon and oil tea in Zhejiang Province. Subsequently, this model is widely used in the prediction of transportation, air accidents, and rainfall. Accordingly, we use GMM to predict FDI of China [26, 27, 28]. ### 2.1 Gray model The Gray system theory, founded and developed by Chinese scholar Deng, extends the viewpoints and methods of general system theory, information theory, and cybernetics to the abstract system of society, economy, and ecology, incorporating the development of mathematical methods to develop the theory and method of Gray system. The modeling process is as follows. (1) Raw series are X 0 = x 0 1 x 0 2 x 0 m E1 (2) To weaken the randomness of the original data, the accumulated generating series is derived: X 1 k = i = 1 k x 0 i . E2 (3) Based on the sequence of X t 1 , a new sequence Z t 1 is derived as follows: Z 1 k = 1 2 x 1 k + 1 2 x 1 k 1 E3 (4) Then, whitened differential equation is obtained: x 0 k + a Z 1 k = b E4 In Eq. (4) a is development coefficient, b is the parameter of Gray action, and Φ is identification parameter vector. Then, the least squares estimation of parameters satisfies the following equation: Φ ̂ = a ̂ b ̂ = B T B 1 B T Y E5 and B = Z 1 2 1 Z 1 3 1 Z 1 m 1 , Y = x 0 2 x 0 3 x 0 m E6 By differentiating x 1 k , a whitened differential equation can be written as d x 1 dt + a x 1 k = b (5) The whitened time response is as follows: x ̂ 1 k + 1 = x 1 1 b ̂ a ̂ e a ̂ k + b ̂ a ̂ E7 Reducing the sequence of x ̂ 1 k + 1 k = 1 2 m 1 , the following sequence is obtained: X ̂ 0 = x ̂ 0 1 x ̂ 0 2 x ̂ 0 m E8 (6) Model testing Model test is divided into residual test and Gray-relating test. Residual test is to obtain the difference between predicting value and the actual value. Firstly, the absolute residuals and relative residuals about X 0 and X ̂ 0 are calculated: 0 i = x ̂ 0 i x 0 i ( i = 1 , 2 , , n ) E9 ϕ i = 0 i x ̂ 0 i ( i = 1 , 2 , , n ) E10 Then, below is the average value of relative residuals: Φ = 1 n i = 1 n ϕ i E11 Given the value of α , it is called residual qualification model when Φ < α . The value of α can be 0.01, 0.05, or 0.10, and the corresponding model is perfect, qualified, and barely qualified. As shown in Eq. (12), Gray correlation degree measures the correlating coefficient between the original sequence and the reference sequence: ε i k = min i min k x k x i k + ρ max i max k x k x i k x k x i k + ρ max i max k x k x i k E12 i denotes the i th group of fitting data, and k denotes the k th one in a certain group. ρ denotes the distinguish coefficient varying from 0 to 1, which is always set as 0.5. However, the correlation coefficient varies with moments, which results in disperse information. Combining the correlation coefficient in different moments together, we can obtain the correlation degree between the original curve and the fitting curve: r i = 1 n k = 1 n ε i k E13 ### 2.2 Markov model Markov chain is proposed by Andrey Markov (1856–1922), and it is a discrete time stochastic process with Markov property in mathematics. Given the current knowledge and information, historical information has no impact on the future. To improve prediction accuracy, Markov model is used to handle the data obtained by GM(1,1). It is critical to divide state and build transition matrix. #### 2.2.1 Dividing states To divide states, four rules are suggested to follow. Firstly, the partition state must have at least one true value in each state. Secondly, elements in a one-step transition matrix cannot be the same. Thirdly, the actual values must fall into one state. Finally, the state must pass Markov test. The numbers vary according to the original data. In this chapter, the overall level of FDI in China is on the rise while fluctuating in detail. Therefore, the level of FDI is a non-stable stochastic process. Taking the curve of Y ̂ k = x ̂ 0 k + 1 as reference, the sequence can be divided into n states. The intervals can be denoted as Q i = Q 1 i Q 2 i and i = 1 , 2 , , n , in which Q 1 i = Y ̂ k + E 1 i and Q 2 i = Y ̂ k + E 2 i . #### 2.2.2 Transition matrix Assuming that there are n states denoting as E 1 , E 2 , , E n , the transition probability amounts to frequency approximately in general, namely, P ij = M ij l M i . Then, we can get the l th step transition matrix P l = P ij l n × n . M ij l is the data of raw series transferring l step from the state Q i to the state Q j . #### 2.2.3 The forecasting value The eventual forecast is in the center of the Gray zone, which is denoted as Y k = 1 2 Q 1 i + Q 2 i = Y ̂ k = 1 2 E 1 i + E 2 i . Eventually, the forecasting sequence is obtained as Y k = Y 1 Y 2 Y m . Advertisement ## 3. Time series model (TSM) Burg suggests that recursive algorithm estimated by the AR(P) model is the most practical one [29], while Hannan proposes time series with multidimensional linear stationary RMA p q . The times series model mainly includes the autoregressive model and the moving average model [30, 31, 32], and generally the modeling steps are as follows. ### 3.1 Preliminary analysis of data and modeling identification Time series prediction is a statistical method processing dynamic data, which is a random sequence arranged in chronological order or a set of ordered random variables defined in probabilistic space { X t , t = 1, 2, …, n}, in which the parameter t represents time. In the TSM, if the samples’ autocorrelation function ρ ̂ k decreases to zero based on the negative exponential function, then it can be preliminarily judged that this sequence is a stationary autoregressive moving average model (ARMA). If the absolute value of the sample autocorrelation function in the q -step delay ρ ̂ k k q is greater than twice of the standard deviation and the value of ρ ̂ k k > q is less than twice of the standard deviation, then the sequence is q -step moving average model (MA(q)). In a similar vein, we can judge p -step autoregressive moving average model (AR(p)) according to the truncation situation of partial autocorrelation function φ ̂ kk . ### 3.2 Parameter estimation In order to fit the TSM, we need to estimate the autoregressive coefficient φ i , the moving average coefficient θ i , the mean μ , and the variance σ ε 2 of the white noise sequence in the ARMA model. ### 3.3 Diagnostic test The purpose of diagnostic test is to check and test the rationality of the model, including residual test, autocorrelation function of residual error and partial autocorrelation function test, and the significance test of parameters in the model. ### 3.4 Optimal model selection Model recognition is only a preliminary selection of TSM. Considering the actual observed errors and statistical errors, several models are taken as candidate models. And the most common methods of selecting optimal models include F-test method, criterion function method (AIC criterion, BIC criterion, SBC criterion). Advertisement ## 4. Comparison of GMM and TSM ### 4.1 GMM predicting FDI of China Take the FDI value of China over the period from 1990 to 2016 as the original data (unit,$100 million; data source, Ministry of Commerce of the PRC):

X 0 = { 34.87, 43.66, 110.08, …, 1260 }

Based on Eq. (5) and using the software MATLAB, the least squares estimation (LSE) of FDI is as follows:

Φ = a ̂ b ̂ = 0.0697 243.795

Based on Eq. (7), time-response function can be written as x ̂ k + 1 = 3530.59 e 0.0697 k 3495.72 . Residual values can be obtained according to relative error based on the prediction value of GM(1,1) model. To improve the predicting accuracy, the relative error can be divided into five states (E1, E2, E3, E4, E5) between 1990 and 2010. The relative error status can be seen in Tables 2.

Residual State E1 E2 E3 E4 E5
Meaning Extremely underestimated Underestimated Reasonable Overestimated Extremely overestimated
Range [−0.17, −0.10] [−0.10, 0.02] [0.02, 0.07] [0.07, 0.12] [0.12, 0.83]

### Table 1.

Relative error status of FDI level in China.

Year Original Relative error of GM State Year Original Relative error of GM State
1990 34.87 0 E3 2001 468.78 0.0848 E4
1991 43.66 0.8288 E5 2002 527.43 0.0397 E3
1992 110.08 0.5974 E5 2003 535.05 0.0914 E4
1993 275.15 0.0615 E3 2004 606.30 0.0398 E3
1994 337.67 −0.0741 E2 2005 603.25 0.109 E4
1995 375.21 −0.1131 E1 2006 630.21 0.1318 E4
1996 417.26 −0.1545 E1 2007 747.68 0.0394 E3
1997 452.57 −0.1679 E1 2008 923.95 −0.1071 E1
1998 454.63 −0.0941 E1 2009 900.33 −0.0061 E2
1999 403.19 0.095 E4 2010 1057.35 −0.102 E1
2000 407.15 0.1477 E4

### Table 2.

Comparison of GM(1,1) prediction value and original value of FDI of China.

Data source: China Statistical Yearbook over the period from 2000 to 2006, Ministry of Commerce of the PRC.

According to the original FDI value over a period from 1990 to 2010 and the relative error of prediction value in GM(1,1), the transition matrixes of different steps P 1 i i = 1 2 3 4 5 are shown as follows:

P 1 1 = 3 5 1 5 0 1 5 0 1 0 0 0 0 1 4 1 4 0 1 2 0 0 0 1 2 1 2 0 0 0 1 2 0 1 2 , P 1 2 = 3 5 0 0 2 5 0 1 0 0 0 0 1 4 1 4 1 4 1 4 0 1 6 0 1 3 1 2 0 0 1 2 1 2 0 0 , P 1 3 = 1 4 0 0 3 4 0 1 0 0 0 0 1 2 0 1 4 1 4 0 1 6 1 6 1 3 1 3 0 1 2 1 2 0 0 0 ,
P 1 4 = 0 0 1 4 3 4 0 1 0 0 0 0 2 3 0 0 1 3 0 1 6 1 6 1 3 1 3 0 1 0 0 0 0 , P 1 5 = 0 0 1 4 3 4 0 0 0 0 1 0 1 3 1 3 1 3 0 0 2 5 0 1 5 2 5 0 1 0 0 0 0

Based on the transition matrix, we can obtain the error state over a period from 2011 to 2016 (see Table 3). Taking the middle value of the error state to modify the prediction value of GM(1,1) model, then the modified value can be seen in Table 3. And x 0 k , x ̂ 0 k , and ϕ i represent the original value, predicting value and relative error of GM(1,1). x ̂ 0 k and ϕ i represent the modified value and relative error of GMM.

Year x 0 k x ̂ 0 k ϕ i x ̂ 0 k ϕ i
1990 34.87 0.0349 0 33.4752 −0.0471
1991 43.66 0.2550 0.8288 127.5082 0.6739
1992 110.08 0.2734 0.5974 136.7182 0.2332
1993 275.15 0.2932 0.0615 281.4594 0.0173
1994 337.67 0.3144 −0.0741 326.9385 −0.0328
1995 375.21 0.3371 −0.1131 379.20437 0.0193
1996 417.26 0.3614 −0.1545 406.5945 −0.0172
1997 452.57 0.3875 −0.1679 435.9630 −0.0289
1998 454.63 0.4155 −0.0941 467.4528 0.0360
1999 403.19 0.4455 0.0950 392.0632 0.0000
2000 407.15 0.4777 0.1477 420.3821 0.0582
2001 468.78 0.5122 0.0848 450.7465 −0.0113
2002 527.43 0.5492 0.0397 527.2409 −0.0056
2003 535.05 0.5889 0.0914 518.2134 −0.0040
2004 606.30 0.6314 0.0398 606.1574 −0.0055
2005 603.25 0.6770 0.1090 595.7787 0.0154
2006 630.21 0.7259 0.1318 638.8121 0.0407
2007 747.68 0.7784 0.0394 747.2223 −0.0059
2008 923.95 0.8346 −0.1071 938.8998 0.0246
2009 900.33 0.8949 −0.0061 930.6540 0.0326
2010 1057.35 0.9595 −0.1020 1079.4327 0.0291
2011 1160.11 1.0288 −0.1276 1157.4006 0.0065
2012 1117.20 1.1031 −0.0128 1241.0002 0.1077
2013 1175.90 1.1828 0.0058 1330.6383 0.1241
2014 1195.60 1.2682 0.0573 1116.0363 −0.0417
2015 1262.70 1.3598 0.0714 1196.648 −0.0260
2016 1260.00 1.4580 0.1358 1283.0826 0.0451

### Table 3.

Residual checklist of Markov model and GM(1,1).

Annotations: The unit of x 0 k and x ̂ 0 k is 1 billion dollars. The unit of x ̂ 0 k is 103 billion dollars. Data source: China Statistical Yearbook.

In the light of Eqs. (9)(11), the relative residual error of GM(1,1) and GMM is 0.0584 < α = 0.1 and 0.0458 < α = 0.05 , respectively. Therefore, the GM(1,1) model is barely qualified, and the modified GMM model is qualified. Gray correlation degrees of the two models are 67 and 79.9%, respectively. In summary, the prediction accuracy of GMM has been improved, and its fitting effect exceeds the model of GM(1,1).

### 4.2 TSM predicting FDI of China

Now we will build a TSM based on the FDI value of China over the period from 1990 to 2016, obtain the predicting data, compare the difference between the predicted data and the original date, and evaluate the accuracy of this model.

Figure 1 shows the changing tendency of FDI in China over the period between 1990 and 2016. The raw data series show the seasonal change and overall growth, but the data series are not stable. Through the seasonal difference method to process the data, the seasonal difference order of three was selected. After the differential processing, the data sequence has been stabilized, eliminating the growing trend (Figure 2).

We determine the order of TSM based on sample autocorrelation function and partial autocorrelation function. After the one-step delay, the sample autocorrelation function falls to a standard error of twice times and has the property of truncation. After the two-step delay, the sample partial autocorrelation function falls to a standard error of twice times and has the property of truncation.

In the light of the calculation of SAS software, now we compare the model of ARMA(2,1), AR(2), and MA(1) (see Tables 4 and 5).

Model Parameter Estimate P-value AIC SBC
AR(2) MU 2.0711 <0.0001*** 1.0603 4.5944
AR1,1 1.5357 <0.0001***
AR1,2 −0.5392 0.0102**
MA(1) MU 0.4676 0.0038*** 28.7297 31.08558
MA1,1 −0.7099 0.0001***
ARMA(2,1) MU 1.9380 <0.0001*** 0.7062 5.4184
MA1,1 −0.4940 0.1192
AR1,1 1.2352 0.0015***
AR1,2 −0.2358 0.5028

### Table 4.

Prediction results of TSM.

Annotations: ***, **, and * indicate a significant level of 0.01, 0.05, and 0.1, respectively.

To lag 6 12 18
Chi-square 3.91 5.44 8.02
Pr > ChiSq 0.4187 0.8599 0.9481

### Table 5.

Self-correlation test of AR(2) model.

Comparing the AIC and SBC values for ARMA(2,1), AR(2), and MA(1) models (see Table 4), we find the model MA(1) to be the most inferior. Considering the AIC and SBC criterion values of ARMA(2,1) and AR(2) and the significance of parameters, it is found that fitting effect of the AR(2) model is the best.

As shown in Table 5, the P-value (Pr > ChiSq) for self-correlation test of the residual sequence with the 6-step delay, the 12-step delay and the 18-step delay are greater than that of the significant level α = 0.1. Therefore, we cannot reject the hypothesis that residuals are non-autocorrelated. That is to say, the residual is regarded as a white noise sequence. This illustrates that the AR(2) model has extracted sufficient information from the raw series and it is a rational model:

( 1−1.53571 B + 0.53921 B 2 ) ( 1 B 3 ) X t = ε t

where X = num 2.0711 , t = year , and num represents the FDI value of the corresponding year.

### 4.3 Comparison of prediction results of two models

#### 4.3.1 Accuracy assessment

Regarding how to select the appropriate accuracy evaluation criteria, Yokuma and Armstrong [33] have done a survey of expert opinions. They think that accuracy, clear physical meaning, and being easy to implement can be the critical evaluation criteria [33]. Accordingly, three criteria are used to evaluate the accuracy of the prediction model.

#### 4.3.2 Comparing predicted values with actual values

As shown in Table 6, the prediction accuracy of GMM has been improved manifestly compared with that in GM(1,1) model. Therefore, the forecasting value in GMM is closer to the actual level of China’s FDI. Then, from Figures 3 and 4, we can clearly see that GMM model has a better fitting effect than that in TSM.

Criterion Mean squared error Mean absolute error Mean absolute percentage error
Index MSE = 1 n i = 1 n x i x ̂ i 2 MAE = 1 n i = 1 n x i x ̂ i MAPE = 1 n i = 1 n x i x ̂ i x i
GM(1,1) 7.3991e+03 66.0812 0.3117
GMM 2.4731e+03 29.5558 0.1181
Time series 1.6644e+04 85.6101 0.1448

### Table 6.

Three criteria to evaluate the accuracy of models.

Annotations: x ̂ i is the predicting value, x i is the original value, and n is the predicting number.

## 5. Empirical analysis of FDI in Chongqing and Beijing

From discussions above, it is found that GMM has higher prediction accuracy and better fitting effects than those of TSM of Chinese FDI level. To further verify the credibility of this result, we construct GMM and TSM based on the FDI level of Beijing (1990–2016) and Chongqing (1990–2015). The divided states involved in the GMM are shown in Table 7, and the transition matrixes of GMM associated with Beijing and Chongqing are denoted as P 2 and P 3 . For simplification, we only list the form of transition matrix P 2 . The comparison of GM(1,1) and GMM can be seen in Tables 8 and 9. The average relative errors of GM(1,1) and GMM of Beijing (Chongqing) are 0.0312 (0.5285) and −0.0029 (−0.1051), respectively. The Gray relational degrees of GM(1,1) and GMM of Beijing (Chongqing) are 64.62% (75.26%) and 79.39% (86.82%), respectively. Therefore, the errors of GM(1,1) and GMM are barely qualified or qualified, and hence GMM is superior to GM(1,1):

P 1 1 = 2 4 4 4 0 0 0 1 5 2 5 1 5 1 5 0 0 1 3 1 3 0 1 3 1 5 0 1 5 2 5 1 5 0 0 0 2 3 1 3 , P 1 2 = 2 4 1 4 0 1 4 0 0 2 5 2 5 1 5 0 0 1 2 0 0 1 2 1 5 1 5 1 5 1 5 1 5 1 3 0 0 2 3 0 , P 1 3 = 1 4 2 4 0 1 4 0 0 0 2 4 1 4 1 4 0 1 2 0 1 2 0 1 5 2 5 0 2 5 0 2 3 0 1 3 0 0 ,
P 1 4 = 1 4 1 4 1 4 0 1 4 0 0 1 3 2 3 0 1 2 0 1 2 0 0 0 3 5 1 5 1 5 0 2 3 1 3 0 0 0 , P 1 5 = 0 1 4 0 3 4 0 0 0 0 1 2 1 2 1 2 0 1 2 0 0 1 5 2 5 2 5 0 0 1 3 2 3 0 0 0
Area Error state E1 E2 E3 E4 E5
Beijing Range [−0.47, −0.2] [−0.2, −0.1] [−0.1, 0.1] [0.1, 0.28] [0.28, 0.65]
Chongqing Range [0, 0.28] [0.28, 0.55] [0.55, 0.75] [0.75, 0.81] [0.81,0.97]

### Table 7.

Residual states of FDI in Beijing and Chongqing.

Year Original value GM(1,1) GMM TSM To state GR MR
1990 27,696 0.0277 0.0277 0.0277 E3 0 0
1991 24,482 0.0693 0.0371 0.0245 E5 0.6466 0.3395
1992 34,984 0.0780 0.0417 0.0364 E5 0.5514 0.1614
1993 66,693 0.0878 0.0711 0.0311 E4 0.2401 0.0618
1994 144,460 0.0988 0.1121 0.0863 E1 −0.4625 −0.2885
1995 140,277 0.1112 0.1262 0.1342 E1 −0.2617 −0.1117
1996 155,290 0.1251 0.1420 0.1969 E1 −0.2410 −0.0934
1997 159,286 0.1408 0.1620 0.1514 E2 −0.1310 0.0165
1998 216,800 0.1585 0.1799 0.2127 E1 −0.3677 −0.2050
1999 197,525 0.1784 0.2052 0.2126 E2 −0.1071 0.0373
2000 168,368 0.2008 0.1626 0.2680 E4 0.1615 −0.0352
2001 176,818 0.2260 0.1831 0.1767 E4 0.2176 0.0341
2002 172,464 0.2544 0.1361 0.2213 E5 0.3220 −0.2673
2003 219,126 0.2863 0.2319 0.1890 E4 0.2346 0.0551
2004 255,974 0.3222 0.2610 0.2560 E4 0.2056 0.0193
2005 352,834 0.3627 0.3627 0.2878 E3 0.0271 0.0271
2006 455,191 0.4082 0.4694 0.3979 E2 −0.1151 0.0303
2007 506,572 0.4594 0.5283 0.5179 E2 −0.1026 0.0412
2008 608,172 0.5171 0.5947 0.5870 E2 −0.1761 −0.0227
2009 612,094 0.5820 0.5820 0.6851 E3 −0.0517 −0.0517
2010 636,358 0.6550 0.6550 0.7277 E3 0.0285 0.0285
2011 705,447 0.7373 0.8368 0.7195 E5 0.0431 0.1570
2012 804,160 0.8298 0.6721 0.8222 E4 0.0309 −0.1964
2013 852,418 0.9339 0.7565 0.9097 E4 0.0873 −0.1268
2014 904,085 1.0512 1.0512 1.0009 E3 0.1399 0.1399
2015 1,299,635 1.1831 1.3428 1.0292 E1 −0.0985 0.0322
2016 1,302,858 1.3316 1.5114 1.4400 E1 0.0216 0.1380

### Table 8.

Comparison of predicted errors of GMM and GM(1,1) of Beijing FDI level.

Annotations: In Table 8, the unit of original value is 1 million dollars. GM, GMM, and TSM represent the predicted value of Gray model, Gray-Markov model, and time series model, and their unit is 106 million dollars. GR and MR represent the residuals of Gray model and Gray-Markov model. Data source: Beijing Statistical Yearbook (1990–2017), Beijing Municipal Bureau of Statistics.

Year Original value GM(1,1) GMM TSM To state GR MR
1990 332 0.0033 0.0029 0.0003 E1 0 −0.1628
1991 977 0.3159 0.0347 0.0010 E5 0.9691 0.7188
1992 10,247 0.3647 0.1276 0.0029 E3 0.7190 0.1972
1993 25,915 0.4210 0.2463 0.0846 E2 0.3844 −0.0523
1994 44,953 0.4860 0.4180 0.0686 E1 0.0751 −0.0755
1995 37,926 0.5611 0.3282 0.0842 E2 0.3241 −0.1554
1996 21,878 0.6478 0.2267 0.0406 E3 0.6623 0.0350
1997 38,466 0.7478 0.4375 0.0165 E2 0.4856 0.1207
1998 43,107 0.8633 0.5050 0.0755 E2 0.5007 0.1465
1999 23,893 0.9967 0.2193 0.0563 E4 0.7603 −0.0897
2000 24,436 1.1506 0.2531 0.0181 E4 0.7876 0.0347
2001 25,649 1.3284 0.2922 0.0296 E4 0.8069 0.1223
2002 28,089 1.5335 0.1687 0.0329 E5 0.8168 −0.6651
2003 31,112 1.7704 0.1947 0.0360 E5 0.8243 −0.5976
2004 40,508 2.0439 0.4497 0.0417 E4 0.8018 0.0991
2005 51,575 2.3596 0.5191 0.0599 E4 0.7814 0.0065
2006 69,595 2.7241 0.9534 0.0776 E3 0.7445 0.2700
2007 108,534 3.1448 1.1007 0.1059 E3 0.6549 0.0139
2008 272,913 3.6306 3.1223 0.1930 E1 0.2483 0.1259
2009 401,643 4.1914 3.6046 0.6941 E1 0.0417 −0.1143
2010 304,264 4.8388 2.8307 0.6809 E2 0.3712 −0.0749
2011 582,575 5.5862 3.2679 0.2878 E2 −0.0429 −0.7827
2012 352,418 6.4490 3.7727 1.2269 E2 0.4535 0.0659
2013 414,353 7.4452 4.3554 0.2769 E2 0.4435 0.0487
2014 423,348 8.5952 1.8909 0.5837 E4 0.5075 −1.2388
2015 377,183 9.9228 2.1830 0.5124 E4 0.6199 −0.7278
2016 332 0.0033 0.0029 0.0003 E1 0 −0.1628

### Table 9.

Comparison of predicted errors of GMM and GM(1,1) of Chongqing FDI level.

Annotations: In Table 9, the unit of original value is 1 million dollars. GM, GMM, and TSM represent the predicted value of Gray model, Gray-Markov model, and time series model, and their unit is 105 million dollars. GR and MR represent the residuals of Gray Model and Gray-Markov model. Data source: Chongqing Statistical Yearbook (1990–2016), Beijing Municipal Bureau of Statistics.

Similar to Section 4.2, TSM of Beijing FDI can be modeled as MA (1):

1 + 0.82673 B 1 B 2 X t = ε t

where X = num 0.27264 and t = year .

TSM of Chongqing FDI can be modeled as ARMA(1,2,1):

1 0.82442 B 1 + B 1 B 2 X t = ε t

where X = num 2.178452 and t = year .

Figure 5 (Figure 6) shows the difference between the original value and the predicting value in Gray-Markov model (time series model) of foreign direct investment in Beijing. It is apparent that the fitting effect of GMM is better than that of TSM. The similar conclusion can be drawn from Figures 7 and 8. Tables 9 and 10 show the predicting effect of GMM is better than that of TSM from the point of predicting errors and accuracy. There is no doubt that it is a good thing to predict accurately the foreign direct investment of the forthcoming 5 or 10 years for the domain specialists. Because if the predicting results is lower or higher than they expected, they could pay attention to seeking the critical factors and policy which have impacts on FDI and adjust them in advance.

Area Index MSE = 1 n i = 1 n x i x ̂ i 2 MAE = 1 n i = 1 n x i x ̂ i MAPE = 1 n i = 1 n x i x ̂ i x i
Beijing GM(1,1) 3.3244e+09 4.7473e+04 0.2587
GMM 4.3599e+09 3.9369e+04 0.1032
TSM 5.4378e+09 4.6731e+04 0.1335
Chongqing GM(1,1) 3.9173e+10 1.3478e+05 2.9584
GMM 5.8230e+09 3.4080e+04 0.2663
TSM 4.4524e+10 1.0126e+05 0.6018

### Table 10.

Comparison of predicting accuracy of two models.

Annotations: MSE, MAE, and MAPE denote mean squared error, mean absolute error, and mean absolute percentage error.

## 6. Conclusions and future work

Our contributions are threefold. Firstly, comparing the predicting results of the Gray-Markov model and the time series model and the original value, respectively, we can find that the fitting effect of the former (GMM) is better than the latter (TSM) and so does its scientific and practical importance. Secondly, the predicting results of GMM show that the level of foreign investment in China has been increasing by years. Thirdly, in order to further enhance Chinese international status and attract more foreign investment, the government should play a role at a macro level to reduce excessive market administrative intervention, establish a service-oriented government, and reduce the relevant approval procedures for international investment.

In the future work, the Gray-Markov model and time series model can be combined with other predicting model (e.g., support vector machine and dynamic Bayesian) to improve the accuracy. Also these models have the potential to be applied in the other areas such as finance (e.g., stocks, funds, and security), risk (e.g., financial risk and operational risk), and business (e.g., consumer price index and incomes).

## Acknowledgments

This chapter is financially supported by the National Natural Science Foundation of China under grant nos. 71771080, 71172194, 71521061, 71790593, 71642006, 71473155, 71390335, and 71571065.

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Written By

Yanyan Zheng, Tong Shu, Shou Chen and Kin Keung Lai

Submitted: June 8th, 2018 Reviewed: December 23rd, 2018 Published: October 23rd, 2019