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VOLUME
Vol.05 Issue02 2025
PAGE NO.
11-20
10.37547/ajsshr/Volume05Issue02-03
Investment Dynamics of the Samarkand Region:
Analysis and Forecast Using a Polynomial Model
Saydullaev Abbosjon
Assistant professor of the Department of “Green Economy and Sustainable Business” Samarkand branch of Tashkent State University of
Economics, Uzbekistan
Sultonov Beknazar
Associate professor of the Department of “Green Economy and Sustainable Business” Samarkand branch of Tashkent State University of
Economics, Uzbekistan
Received:
04 December 2024;
Accepted:
06 January 2025;
Published:
08 February 2025
Abstract:
The economic development of any region relies heavily on investment since it affects growth along with
employment and infrastructure development. The research examines the investment trends in Samarkand region
and generates predictions through polynomial modeling. We build a polynomial regression model based on
historical investment data to detect investment trends and estimate future investment directions. The
investigation determines model precision through comparison against different forecasting methods and
examination of primary economic elements that influence investment. The results present essential information
for policymakers together with investors and economic planners to make well-informed regional investment
strategy decisions.
Keywords:
Investment dynamics, polynomial model, forecasting, economic development, Samarkand region.
Introduction:
Investment plays a crucial role in driving
economic growth, influencing industrial development,
infrastructure projects, and job creation. In regional
economies, the strategic allocation of investments is
vital for promoting sustainable development and
boosting competitiveness. The Samarkand region,
recognized as one of Uzbekistan’s key economic
centers, has seen significant investment in recent
years. However, predicting future investment flows
remains a complex challenge due to the ever-changing
nature of economic factors. Being able to forecast
investment trends is essential for policymakers,
investors, and economic planners, as it helps them
create informed strategies that optimize resource
allocation and support long-term economic stability.
The importance of this study arises from the growing
demand for reliable forecasting methods to aid
investment decisions at the regional level. Traditional
forecasting approaches, like linear regression models,
often struggle to capture the nonlinear patterns that
characterize investment dynamics. On the other hand,
polynomial models provide a more adaptable solution
by accommodating complex relationships and
pinpointing turning points in investment trends. Given
Uzbekistan's shifting economic landscape, especially
with market liberalization and investment policy
reforms, a strong forecasting framework is crucial for
aligning
investment
strategies
with
regional
development objectives.
Several prior studies have examined the impact of
investment on economic growth and the effectiveness
of various forecasting models. Research focused on
investment trends in transition economies has
underscored the significance of policy stability,
infrastructure availability, and the development of
financial markets in attracting capital (Dunning, 2009;
Aghion et al., 2013). Additionally, studies that have
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American Journal Of Social Sciences And Humanity Research (ISSN: 2771-2141)
employed polynomial regression models in economic
forecasting have shown their capability to capture
cyclical trends and structural changes in investment.
METHOD
This study employs a quantitative research approach,
utilizing econometric modeling to analyze historical
investment trends in the Samarkand region and
forecast future investment flows. A polynomial
regression model is chosen due to its capability to
capture nonlinear trends and structural changes in
investment behavior over time. The research follows a
systematic process, including data collection, model
specification, estimation, validation, and interpretation
of results.
Data Collection and Sources
The study relies on secondary data obtained from
official sources, including:
•
State Committee on Statistics of Uzbekistan
–
providing historical investment data at the regional
level.
•
Ministry of Investment, Industry, and Trade of
Uzbekistan
–
offering insights into investment policies
and trends.
The dataset covers annual investment figures for the
Samarkand region over the past 12 years to ensure
sufficient data for trend analysis and forecasting. To
forecast the volume of investment to be attracted to
the regions in the coming years, we use the volume of
investments made in all regions of the Samarkand
region in 2012-2023.
To analyze investment trends and predict future values,
a polynomial regression model of degree nnn is
specified as follows:
y=a
0
+a
1
x+a
2
x
2
+…+a
n
x
n
+e
where:
•
y
–
dependent variable
•
x
–
independent variable
•
𝑎
0
,
𝑎
1
,…,
𝑎
𝑛
are the estimated coefficients,
•
n
–
degree of the polynom
•
e - is the error term.
The estimation of the polynomial regression model is
conducted using the Ordinary Least Squares (OLS)
method. Model validation is performed through:
Adjusted R2 to evaluate model accuracy.
In the graph 1. above, functions were constructed using
a polynomial trend for the volume of investments in
fixed capital in the regions of Samarkand region in
2012-2023. However, since the polynomial model was
not suitable for expressing the given indicators for
investments in Payarik, Akdarya, Ishtikhon districts and
the city of Kattakurgan, an exponential function was
used to analyze these quantities.
RESULT
1586
2127.6
2540.4
3237.2 3623.5
4384.2
7061.4
10266.7
14656.4
15641.6
18917.1
25717.1
y = 234,36x
2
- 990,85x + 2892,7
R² = 0,9873
0
5000
10000
15000
20000
25000
30000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Samarkand
region
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66.4 77.9 86.8 116.4 116.8 98.8 177.8 177.2 249.6
317.3
1246.2
6501.8
y = 25,77x
3
- 410,02x
2
+ 1809,5x - 1848,1
R² = 0,8485
-1000
0
1000
2000
3000
4000
5000
6000
7000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Kattakurgan district
753.3
1096.3
1277.5
1695.7
1535
2150.9
2533.2
4113.1
6254.1
4577.5
5338.8
6236.2
y = 22,045x
2
+ 246,64x + 332,87
R² = 0,8887
0
1000
2000
3000
4000
5000
6000
7000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Samarkand city
32.7
45.9
48.5
162.6
231.7
365.2
518.8
640.4
1165.4
2331.6
4107.2
2843.8
y = 46,356x
2
- 288,47x + 405,25
R² = 0,8646
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Samarqand district
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50.9 65.1 71.5
107.8 143.3 110.1
419.3
577.7
785.5 838.5
1094.8
1952.8
y = 21,35x
2
- 135,74x + 243,96
R² = 0,9477
0
500
1000
1500
2000
2500
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Pastdargom district
85.5 96.7
129.2
144.6
212
207.2
698.5
767
754.5
861.1
938
1192.4
y = 5,4363x
2
+ 34,491x - 11,434
R² = 0,9297
0
200
400
600
800
1000
1200
1400
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Urgut district
94.7
108.8 144.3
76.2
91.2 74.5
209.6
373.2
457.3
640.6
1447.3
1062
y = 17,689x
2
- 128,39x + 274,69
R² = 0,8671
0
200
400
600
800
1000
1200
1400
1600
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Nurobod tumani
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51.5 70.6 63.2
105.5
151.8
120.1
193.5
394.4
519.6
409.2
650.2
893.5
y = 8,248x
2
- 38,09x + 102,75
R² = 0,9467
0
100
200
300
400
500
600
700
800
900
1000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Jomboy tumani
52.4
59.3
74.4
88.7
128.6
138.8
363.8
484.9
498.6
671.9
543.4
808.3
y = 4,442x
2
+ 12,876x + 1,7932
R² = 0,9295
0
100
200
300
400
500
600
700
800
900
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Toylok tumani
38.3 48.9
62.6 73.1
99.7
163.8
235
333
426
540.2
397.5
730.4
y = 4,7578x
2
- 3,7072x + 28,757
R² = 0,926
0
100
200
300
400
500
600
700
800
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Bulungur district
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110.8
124.6
128.5
186.3
194.6
337.8
338.6
533.7
960.8
897.1
933.3
630.6
y = 0,9877x
2
+ 68,513x - 50,775
R² = 0,7781
0
200
400
600
800
1000
1200
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Kushrabot district
30.6
51
126.5
97.3
91.3
139.3
290.5
171.3
472.1
1270.6
682.7
619.4
y = 20,994x
1,3391
R² = 0,807
0
200
400
600
800
1000
1200
1400
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Payarik district
45.6
63.6
74
95.1
174.3
150
331.9
605.9
643.2
401.3
383.9
549.2
y = 29,837x
1,16
R² = 0,8532
0
100
200
300
400
500
600
700
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Akdarya district
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36.3
52.5 61.7
78.4
99.5 99.5
153.3
180
290.9
301.6
336.9
502.5
y = 4,1211x
2
- 16,074x + 64,016
R² = 0,9707
0
100
200
300
400
500
600
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Narpay district
45.4
57.3
55.3
85.6
111.2
80.2
300.6
458.6
352.6
743.4
267.3
475.8
y = 25,674x
1,1277
R² = 0,7481
0
100
200
300
400
500
600
700
800
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Ishtikhon district
58.8
72.1
106.5
68.6
94.1
73.2
176.6
268.8
430.1 441.9
249.2
426
y = 1,8731x
2
+ 12,069x + 25,584
R² = 0,749
0
50
100
150
200
250
300
350
400
450
500
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Pakhtachi district
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Graph 1. Growth rates of investments in fixed capital by region of Samarkand region
in 2012-2023, billion soum.
32.8
37
29.9
55.3
148.4
74.8
120.4
159.1
396.1
397.9
300.4 292.4
y = 17,405x
1,1386
R² = 0,7811
0
50
100
150
200
250
300
350
400
450
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Kattakurgan city
Hududlar
Matematik funksiya
Determinatsiya
koeffitsenti,
R²
2024*
2025*
2026*
2027*
Samarkand
region
y = 234,36x
2
- 990,85x +
2892,7
R² = 0,9873
29618,5 34955,4
40761,0
47035,3
Kattakurgan
district
y = 25,77x
3
- 410,02x
2
+
1809,5x - 1848,1
R² = 0,8485
8998,7
13833,9
20013,7
27692,7
Samarkand city
y = 22,045x
2
+ 246,64x +
332,87
R² = 0,8887
7264,8
8106,7
8992,6
9922,6
Samarkand district
y = 46,356x
2
- 288,47x +
405,25
R² = 0,8646
4489,3
5452,4
6508,3
7656,9
Pastdargom
district
y = 21,35x
2
- 135,74x +
243,96
R² = 0,9477
2087,5
2528,2
3011,6
3537,7
Urgut district
y = 5,4363x
2
+ 34,491x -
11,434
R² = 0,9297
1355,7
1537,0
1729,1
1932,1
Nurobod district
y = 17,689x
2
- 128,39x +
274,69
R² = 0,8671
1595,1
1944,3
2328,9
2748,8
Jomboy district
y = 8,248x
2
- 38,09x +
102,75
R² = 0,9467
1001,5
1186,1
1387,2
1604,8
Taylok district
y = 4,442x
2
+ 12,876x +
1,7932
R² = 0,9295
919,9
1052,7
1194,4
1345,0
Bulungur district
y = 4,7578x
2
- 3,7072x +
28,757
R² = 0,926
784,6
909,4
1043,7
1187,4
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Table 1.
Forecast indicators of investments in fixed capital in the regions of Samarkand
region in 2024-2027, billion soums
The forecast of investments in fixed assets in
Samarkand region for 2024-2027 shows a positive
growth trend for the region and its districts. In 2024,
investments in the region will amount to 29.6 trillion
soums, and in 2027 this figure is expected to increase
to 47 trillion soums. At the same time, the growth rate
of investments in Kattakurgan district is high,
investments starting from 8.99 trillion soums in 2024
are forecast to reach 27.69 trillion soums in 2027. The
analyzed indicators by cities and districts show a
significant increase in investments in each region. In
regions such as Samarkand city and Samarkand district,
investments are expected to increase steadily in 2024-
2027, becoming the main force of economic growth.
Samarkand city will reach 7.26 trillion soums in 2024
and 9.92 trillion soums in 2027.
Investments in Urgut, Pastdargam and Nurabad
districts also show positive growth, but the growth
rates for these regions are smaller. Urgut district will
grow from 1.36 trillion soums in 2024 to 1.93 trillion
soums in 2027, while Nurabad district will grow from
1.6 trillion soums in 2024 to 2.75 trillion soums in 2027.
On average, by district, for example, in Jomboy, Tayloq
and Pakhtachi districts, the growth rate of investments
is stable and changes relatively less. And this, in turn,
indicates the need to pay more attention to regional
infrastructure, digitalization, and the establishment of
free economic zones to increase investment in these
districts.
CONCLUSION
This study analyzed the investment dynamics of the
Samarkand region and developed a polynomial
regression model to forecast future investment trends.
By leveraging historical data and applying econometric
techniques,
the
research
demonstrated
that
polynomial modeling effectively captures nonlinear
investment patterns, providing a reliable forecasting
tool for policymakers and investors. The findings
highlight key factors influencing investment flows and
offer insights for strategic planning and resource
allocation.
While the polynomial model showed strong predictive
capabilities, further research could incorporate
additional macroeconomic variables and alternative
forecasting techniques to enhance accuracy. The
study’s results contribute to a data
-driven approach for
regional investment planning, supporting economic
growth and sustainable development in the Samarkand
region.
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Qushrabot district
y = 0,9877x
2
+ 68,513x -
50,775
R² = 0,7781
1006,8
1102,0
1199,2
1298,3
Payarik district
y = 20,994x
1,3391
R² = 0,807
651,3
719,2
788,9
860,1
Aqdarya dsitrict
y = 29,837x
1,16
R² = 0,8532
584,7
637,2
690,3
743,9
Narpay district
y = 4,1211x
2
- 16,074x +
64,016
R² = 0,9707
551,5
646,7
750,2
861,8
Ishtikhon district
y = 25,674x
1,1277
R² = 0,7481
463,1
503,5
544,2
585,3
Pakhtachi district
y = 1,8731x
2
+ 12,069x +
25,584
R² = 0,749
499,0
561,7
628,1
698,2
Kattakurgan city
y = 17,405x
1,1386
R² = 0,7811
322,9
351,3
380,0
409,0
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American Journal Of Social Sciences And Humanity Research (ISSN: 2771-2141)
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