“Time series analysis” is one of the main tools to predict the value of economic variable with the appropriate model to describe the time variation of historical data. “Scenario planning” is a kind of special research method which is used to analyze the macro environment of a subject. In the prediction of the growth trend of economic entities, the two methods can be used to a certain extent to avoid the prediction errors caused by environmental changes. The results showed that the economic growth of Shenzhen during “the 13th Five-Year Plan” would appear a slowing trend.
Since Adam Smith, the economic growth has been a hot topic in economics circles, and an important constituent of main-stream economics. Kuznets, Nobel economics prize winner, measured economical growth with GDP growth rate and defined economic growth as an increase of GDP [
For GDP prediction, the common ways are as follows: regression model [
After quick development in the past three decades, information of this rich growth journey must be included in GDP, Shenzhen economic gross statistical index, and certain periodicity and increase rule are exhibited. This rule will certainly be more obvious for the mid and long-term development potential of “13th five-year planning”. Thus according to Shenzhen GDP data for time series analysis, we use data-driven modeling method to concentrate all the factors influencing GDP growth into increase history of GDP itself, so as to fit increase history and predict development and change of GDP during the “13th five-year planning”. In the idea of data-driven modeling, all the factors influencing one variable are concentrated to this variable’s historic records.
The changes of time series can be roughly divided into superposition or combination of trend change, periodic change, cyclical fluctuation as well as random fluctuation. As a common random time series model, ARIMA model was established by Box and Jenkins, called Box-Jenkins method or B-J method jointed [
Consider series
The ARMA (p, q) model after d order differencing is called ARIMA (p, d, q) model (Auto-regressive Integrated Moving Average Model), in which p and q are order numbers of moving average, while εt is a white noise process.
Local GDP is used to represent economic growth level of Shenzhen in this paper. Local GDP in Shenzhen is a monetary value index reflecting ultimate achievement of production activities of all resident units in the city in the whole year, able to deliver a comprehensive picture of total scale of economic activities in the whole society, and is an important comprehensive index to measure economic strength and evaluate economic situation in Shenzhen. For data of nominal GDP gross fails to eliminate effect of inflation, the paper takes the nominal data issued by Shenzhen Statistics Department and local GDP index number as reference to correct nominal GDP data series, and converts it to actual GDP series calculated by the price in 1979, and makes analysis according to this data series.
The growth rate of local GDP in Shenzhen refers to annual increase rate of GDP, and should be calculated by using Shenzhen total GDP calculated in comparable prices. The computing formula of actual GDP growth rate is as follows:
We obtain total local GDP in Shenzhen from 1979 to 2014 from “Shenzhen statistical yearbook”, and use time series yt to express it. GDP growth trend diagram is made according to these data (
Then we define lagging order number p and q, ARMA (p, q) model’s order number can be judged using cutoff property of the model sample’s autocorrelation function and sample’s partial autocorrelation function. The
judgment rule is as follows: if partial correlation function of stationary time series is cutoff, while autocorrelation function is trailing, then this series is suitable for AR model; if the partial correlation function of stationary time series is trailing, while autocorrelation function is cutoff, then this series is suitable for MA model; if the partial correlation function and autocorrelation function of stationary time series are all trailing, then this series is suitable for ARMA model.
The results of autocorrelation function test to sequence D3yt, which can be seen in
The output result of ARIMA (5, 3, 7) model (
ADF | Significant level | critical value |
---|---|---|
1.638473 | 1% | −2.664853 |
5% | −1.955681 | |
10% | −1.608793 |
ADF | Significant level | critical value |
---|---|---|
−3.748875 | 1% | −2.664853 |
5% | −1.955681 | |
10% | −1.608793 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|
C | 0.693012 | 0.084694 | 8.182580 | 0.0000 |
AR(1) | 0.041558 | 0.131407 | 0.316255 | 0.0057 |
AR(2) | 0.271372 | 0.136115 | 1.993696 | 0.0625 |
AR(3) | 0.693298 | 0.120527 | 5.752216 | 0.0000 |
AR(5) | −1.333350 | 0.204919 | −6.506710 | 0.0000 |
MA(1) | −1.759018 | 0.074929 | −23.47577 | 0.0000 |
MA(3) | 0.564195 | 0.124849 | 4.519027 | 0.0003 |
MA(4) | 0.752938 | 0.098721 | 7.626926 | 0.0000 |
MA(5) | 0.348454 | 0.079011 | 4.410204 | 0.0004 |
MA(6) | −1.595127 | 0.197661 | −8.069998 | 0.0000 |
MA(7) | 0.691729 | 0.120259 | 5.752013 | 0.0000 |
R-squared | 0.900758 | Mean dependent var | −0.574836 | |
Adjusted -squared | 0.842381 | S.D. dependent var | 25.29851 | |
S.E. of regression | 10.04383 | Akaike info criterion | 7.738518 | |
Sum squared resid | 1714.936 | Schwarz criterion | 8.261884 | |
Log likelihood | −97.33925 | Hannan-Quinn criter. | 7.898516 | |
F-statistic | 15.42989 | Durbin-Watson stat | 1.942597 | |
Prob (F-statistic) | 0.000001 |
Based on the ARIMA model and the equation
Prediction over local total GDP of Shenzhen in 1987-2013 is made using ARIMA (5, 3, 7) model, and actual numerical values are used to calculate errors (
It is undeniable that any prediction of the future will have a certain error. To ensure the accuracy of the forecast as much as possible, the predicted results were limited in a certain range. According to the results and the mean prediction error from 1987 to 2014, this article takes the mean absolute error as 2% for convenient calculation, assume that the probability of the occurrence of a positive error or a native error is 1% and that the error increase 1% per year because of the dynamic prediction. The nominal GDP of Shenzhen will reach 3111 billion RMB in 2020 in the positive error and 2703.9 billion RMB in the native error. Therefore, in the benchmark scenario, the GDP of Shenzhen in 2020 should be in 2703.9 to 3111 billion RMB, with 2703.9 billion as the upper limit of the pessimistic scenario, and 3111 billion RMB as the lower limit of the optimistic scenario that year. Compared with that of the last five-year plan and the five-year plan before the last, the economic growth rate showed a falling tendency, with an average growth rate around 9.5% in the thirteenth five-year plan. And the average growth rate will be 9.5% in the thirteenth five-year plan in the positive error, 8.3% in the native error.
H. Kahn et al. borrowed the term of scenario in artistic creation and stage management to describe the “probable” development scenes of future. He believes that future is diversiform, and diversified potential results may be realized, and the ways are not same. Scenes are built by descriptions of paths to the possible future. From the perspective of Economics, Yue Zhen et al. (2006) thought that “scenario planning” is to conceive of possible future schemes through detailed and strict reasoning of future based on key hypotheses for major economic, in-
Year | actual values | Predicted value | Error rate | Year | actual values | Predicted value | Error rate |
---|---|---|---|---|---|---|---|
1987 | 31.59754 | 32.37 | 2.5% | 2001 | 621.745 | 659.84 | 6.1% |
1988 | 42.94045 | 46.54 | 8.4% | 2002 | 720.149 | 761.13 | 5.7% |
1989 | 50.97043 | 58.51 | 14.8% | 2003 | 858.3161 | 879.29 | 2.4% |
1990 | 67.53705 | 76.82 | 13.7% | 2004 | 1006.734 | 1024.04 | 1.7% |
1991 | 91.84889 | 100.21 | 9.1% | 2005 | 1158.483 | 1165.73 | 0.6% |
1992 | 122.3428 | 128.05 | 4.7% | 2006 | 1350.387 | 1323.97 | −2.0% |
1993 | 160.1577 | 156.52 | −2.3% | 2007 | 1550.732 | 1513.94 | −2.4% |
1994 | 209.6906 | 197.60 | −5.8% | 2008 | 1738.601 | 1702.90 | −2.1% |
1995 | 259.6242 | 239.83 | −7.6% | 2009 | 1923.792 | 1895.16 | −1.5% |
1996 | 304.2476 | 285.93 | −6.0% | 2010 | 2157.829 | 2132.99 | −1.2% |
1997 | 355.5165 | 343.00 | −3.5% | 2011 | 2374.67 | 2371.38 | −0.1% |
1998 | 409.6683 | 410.13 | 0.1% | 2012 | 2611.22 | 2603.62 | −0.3% |
1999 | 470.0571 | 476.05 | 1.3% | 2013 | 2885.398 | 2891.08 | 0.2% |
2000 | 543.7369 | 561.13 | 3.2% | 2014 | 3139.31 | 3198.46 | 1.9% |
Year | Real GDP | Nominal GDP | GDP growth rate (%) |
---|---|---|---|
2015 | 3480.86 | 17,773.65 | 8.8% |
2016 | 3830.31 | 19,714.46 | 10.0% |
2017 | 4223.85 | 21,913.91 | 10.3% |
2018 | 4573.45 | 23,917.51 | 8.3% |
2019 | 4981.03 | 26,257.40 | 8.9% |
2020 | 5471.79 | 29,075.19 | 9.9% |
dustrial or technical evolution. The basis point of “scenario planning” is that future is full of uncertainty, while its gist is to use diversified hypotheses to replace one hypothesis. Thus the predicted result will be multidimensional (
In an effort to comprehensively reflect GDP change in Shenzhen, the paper introduces scenario planning to analyze the macro environment of the development of Shenzhen economy, identify the major external and internal factors influencing its development, and make model analysis on the prospect of economic growth in Shenzhen during the “13th five year planning” period, and presents the results of economic growth in Shenzhen in reference growth scene, optimistic growth scene, pessimistic (risk) growth scene, as shown in
Based on combination of scene hypotheses about recent international, domestic and local economic development situation, we can identify several prediction schemes as shown in
The results shown in
policy changes or other special factors were not taken into consideration, and some main factors that influence the economic development of Shenzhen (such as the population and the labor force, natural resources, exports, technological progress, industrial structure, demand structure, total factor productivity and so on) change along the trend of the past in the analysis above. We use this as a benchmark of scenario analysis, providing comparable frame of reference to other situations. The predicted GDP for optimistic and pessimistic situations were calculated by increasing or decreasing the reference value by 2% respectively, and divided by the relevant implicit price deflator (
Scene hypotheses | Bad news/pessimistic | Good news/optimistic |
---|---|---|
International environment | Explosion of new-round global economy or financial crisis, slack international market demand, further enlarged debt crisis related to national sovereignty, tightened credit market, severe insufficient capital in cash, international economic stalemate sparked by new cold war and regional hot war, escaping of capital, especially foreign capital, disorder of international energy markets | Major innovation and breakthrough in human society, new-round of technological revolution, significant improvement of international trade environment, lowering of international tension, global economic resurgence, deepened globalization, loose monetary policy continued by related developed countries, still strong increase tendency of most of rising economies |
Domestic and surrounding environment | Slow progress of structural transformation, slow technology innovation and efficiency improvement, further shortage of resources and deterioration of environment, domestic financial concussion sparked by local debt crisis, local undersupply aggravated by countercurrent flow of work force, climbing cost caused by price of resources such as manpower, aggravated insufficient domestic demand and over capacity | Quantum jump of reform and innovation, new demand and new market caused by technological revolution, increased domestic demand caused by urbanization-accelerated citizens’ income and improvement of consumption level, increased export driven by foreign economic resurgence, significant improvement of interaction between regional economy integration and inter-area economic harmony, continuous economic prosperity in neighboring areas, including Hong Kong and Macau |
Year | Pessimistic scene | Reference scene | Optimistic scene | |||
---|---|---|---|---|---|---|
Gross | Increase rate | Gross | Increase rate | Gross | Increase rate | |
2015 | 17,062.71 | 7.5% | 17,773.65 | 9.7% | 18,484.60 | 11.9% |
2016 | 18,531.59 | 8.6% | 19,714.46 | 10.9% | 20,897.33 | 13.1% |
2017 | 20,160.80 | 8.8% | 21,913.91 | 11.2% | 23,667.02 | 13.3% |
2018 | 21,525.76 | 6.8% | 23,917.51 | 9.1% | 26,309.27 | 11.2% |
2019 | 23,106.51 | 7.3% | 26,257.40 | 9.8% | 29,408.29 | 11.8% |
2020 | 25,004.66 | 8.2% | 29,075.19 | 10.7% | 33,145.71 | 12.7% |
Year | Pessimistic scene | Reference scene | Optimistic scene | |
---|---|---|---|---|
2015 | Gross | 16,728.42 ~ 17,418.18 | 17,418.18 ~ 18,129.13 | 18,129.13 ~ 18,861.54 |
rate | 6.4% ~ 8.6% | 8.6% ~ 10.8% | 10.8% ~ 13.0% | |
2016 | Gross | 17,992.86 ~ 19,123.03 | 19,123.03 ~ 20,305.90 | 20,305.90 ~ 21,542.52 |
rate | 7.6% ~ 9.8% | 9.8% ~ 12.0% | 12.0% ~ 14.2% | |
2017 | Gross | 19,388.03 ~ 21,037.36 | 21,037.36 ~ 22,790.47 | 22,790.47 ~ 24,650.17 |
rate | 7.8% ~ 10.0% | 10.0% ~ 12.2% | 12.2% ~ 14.4% | |
2018 | Gross | 20,506.28 ~ 22,721.64 | 22,721.64 ~ 25,113.39 | 25,113.39 ~ 27,687.51 |
rate | 5.8% ~ 8.0% | 8.0% ~ 10.2% | 10.2% ~ 12.3% | |
2019 | Gross | 21,808.98 ~ 24,681.96 | 24,681.96 ~ 27,832.85 | 27,832.85 ~ 31,272.99 |
rate | 6.4% ~ 8.6% | 8.6% ~ 10.8% | 10.8% ~ 12.9% | |
2020 | Gross | 23,386.83 ~ 27,039.92 | 27,039.92 ~ 31,110.45 | 31,110.45 ~ 35,618.35 |
rate | 7.2% ~ 9.6% | 9.6% ~ 11.8% | 11.8% ~ 13.9% |
After combination of previous conclusion about status quo analysis, the probability of “mesoscopic” scene is the highest, so it is taken as “reference scene” (
The above analysis on Shenzhen GDP time series and established model shows that ARIMA model established by using B-J method has better predictive validity when modeling analysis is made for nonstationary time series. The ARIMA (3, 3, 5) model established in this paper better reflects law of economic development, and can be used for mid- and long-term predication over Shenzhen GDP. Besides, the introduction of scenario analysis method can give more scientificalness and accuracy to the prediction result and provide decision reference for Shenzhen to establish economic development objectives. The prediction result is as shown in
TaoWang, (2016) Forecast of Economic Growth by Time Series and Scenario Planning Method—A Case Study of Shenzhen. Modern Economy,07,212-222. doi: 10.4236/me.2016.72023