IMPACT OF SOCIOECONOMIC FACTORS ON THE SAVING BEHAVIOR OF THE RA RESIDENT HOUSEHOLDS

Grigoryan Erik

ERIK GRIGORYAN

PhD Student at the Chair of Finance of Armenian State University of Economics

IMPACT OF SOCIOECONOMIC FACTORS ON THE SAVING1 BEHAVIOR OF THE RA RESIDENT HOUSEHOLDS

In the present paper the structure of bank deposits of the RA during 2009-2020, particularly the dynamics of bank deposits of the RA resident households are represented. The research focuses on the elaboration of the impact of four socioeconomic factors on saving behavior of resident households in the RA. Time series econometric model was constructed to estimate the impact of socioeconomic factors on the saving behavior of resident households, i.e. retail trade turnover (RTT), the officially registered unemployed (ORU), average monthly nominal wage (AMW), consumer price index (CPI). With the use of error correction (EC) version of autoregressive distributed lag (ARDL) model, the impact of the mentioned factors on saving behavior of the resident households both in short and long terms was estimated and illustrated. The results of this research may be useful especially for deposit portfolio management and financial planning in banks.

Keywords: saving behavior, resident households' deposits, social-economic factors,

ARDL model, EC model JEL: D10, E21, H31

DOI: 10.52174/1829-0280_2021_5_22

1 In this paper bank deposits were chosen as characterizing saving. See: Belekhova, G. V., & Rossoshanskii, A. I. (2018). Assessing the Factors that Determine People's Financial Behavior: an Experience of Using Regression Analysis Based on Panel Data. Economic and Social Changes: Facts, Trends, Forecast, //(5), 198-213. doi:10.15838/esc.2018.5.59.13. See: Malkina, M. (2019). Determinants of Private Savings in the Form of Bank Deposits: A Case Study on Regions of the Russian Federation. Economies, 7(2), 1-22. doi:10.3390/economies 7020063. https://abcfinance.am/news/news savings day.html

Introduction. Households by means of their savings and investments act as a locomotive of the economy, almost part of all bank deposits belongs to them. During the last two decades, the structure of bank deposits has changed, specifically the weight of deposits in the Armenian Dram (AMD) prevails within the deposits of resident households. Several groups of factors impact on saving behavior, i.e. socioeconomic, demographic, political, legal, etc.2 IN the research, however, only socioeconomic factors are analyzed. The goal of this paper is the estimation and elaboration of the impact of socioeconomic factors on the deposit (saving) behavior of the RA resident households. Consequently, the following issues are discussed below:

• the saving behavior of the RA resident households (whether they invest more in term deposits or demand deposits, in AMD or in foreign currency, including during recessions),

• from the chosen socioeconomic factors which are statistically significant factors at 5% significance level (both in short run and long run).

Time series data are analyzed to answer the above questions.

Literature Review. The research of people's financial behavior, specifically saving and credit behavior is considered one of the main contemporary issues in academic publications and literature on finance, which often sheds light on people's financial behavior and investment decision determinants from an academic perspective. Behavioral finance, which incorporates concepts from other social sciences such as psychology and sociology3, is a branch of finance that arose from classical finance to explain market irregularities.4 Financial behavior is based on the rational choice of the available distribution options of financial resources aiming to minimize losses and to get maximum gains, acting as an economic mechanism for population to adapt fluctuating economic situations.5 As described in literature, households make financial decisions depending on consumer preferences and expectations, or by investing, saving and borrowing, and by means of conducting cost-benefit analysis.6 In addition, people make mistakes all the time, thus the presumption about their rationality is false.7 As an economic category, financial behavior can be defined as an activity

2 Belekhova, G. V., & Rossoshanskii, A. I. (2018). Assessing the Factors that Determine People's Financial Behavior: an Experience of Using Regression Analysis Based on Panel Data. Economic and Social Changes: Facts, Trends, Forecast, //(5), 198-213. doi:10.15838/esc.2018.5.59.13

3 Hakobyan, A. A., & Grigoryan, E. M. (2021). Behavioral Finance and Traditional Finance: Theory and Analysis in Practice. Finance and Accounting, 4(1), 56-69.

5 Петенева, И. А. (2018). Влияние финансовых ожиданий на финансовое поведение населения России. Ученые записки Алтайского филиала Российской академии народного хозяйства и государственной службы при Президенте Российской Федерации (pp. 168-173). Барнаул: Алтайский филиал РАНХиГС.

6 Stango, V., & Zinman, J. (2009). Exponential growth bias and household finance. The Journal of Finance, 64(6), 2807-2849. doi:10.1111/j.1540-6261.2009.01518.x

7 ^p|iqnpji^b, t. Т., ^1ЬтрпщшЬ, U. T. (2020). 'Гшррш^йш^Ь ф^ЬшЬиЬЬр^ U ф^ЬшЬиЬЬр^ шфшЬ^.ш1|шЬ тЬип^шЬ 1пш№^тш1|шЬ ^Ьр^бш^п^. Sшpшàш2p¡>шb h ш2faшph (2), 175-178:

of people in borrowing, receiving, lending, spending money and so forth, to achieve different aims.8 Financial behavior is one of the financial capability (internal capacity to act in one's best financial interest, given socioeconomic and environmental conditions) concepts. Moreover, the latter is often measured in terms of behaviors arisen from internal and external factors, particularly savings behavior reflects people's long-term planning behavior.9 In addition, according to Gorshkova and Ksenda, investing-saving behavior is one of the strategies of the financial behavior in the framework of the institutional approach.10 Therefore, in the paper, bank deposits were chosen by the author as characterizing saving, and saving behavior was examined as a key discipline in financial behavior. Several empirical studies in different countries examine various macroeconomic determinants of bank deposits. From the discussed socioeconomic factors, the unemployment rate was significant, meanwhile others, including retail trade turnover were not important factors.11 Eriemo showed how bank deposits respond to changes in bank investment, consumer price index, interest rate and bank branches.12 Empirical studies prove that economic growth and inflation are significant factors determining bank deposit and bank credit growth.13141516

Research Methodology. In the paper, horizontal and vertical, comparative analysis of bank deposits were conducted, which was followed by the analysis of time series data in "EViews 10" statistical software.

Initially, the structure of bank deposits during 2009-2020 was analyzed, which was followed by the discussion of saving behavior of the RA resident households. On the basis of 2009-2020 time series data of retail trade turnover (RTT), average monthly nominal wage (AMW), the officially registered

8 Novikov, A. V., & Yarasheva, A. V. (2016). Financial Sociology: Textbook. Moscow: Finansovyi Universitet.

9 The World Bank. (2013, August). Financial Capability Surveys Around the World: Why Financial Capability is important and how surveys can help. 1-8. Retrieved from.

https://documents1.worldbank.org/curated/en/693871468340173654/pdf/807670WP0P14400Box 0379820B00PUBLIC0.pdf

10 Горшкова, Н. В., & Ксенда, В. М. (2019). Модели финансового поведения населения в условиях наноэкономики (налоговый аспект). Вестник Волгоградского государственного университета. Экономика, 21(4), 207-218. doi:10.15688/ek.jvolsu.2019.4.20

11 Belekhova, G. V., & Rossoshanskii, A. I. (2018). Assessing the Factors that Determine People's Financial Behavior: an Experience of Using Regression Analysis Based on Panel Data. Economic and Social Changes: Facts, Trends, Forecast, 11(5), 198-213. doi:10.15838/esc.2018.5.59.13

12 Eriemo, N. O. (2014). Macroeconomic Determinants of Bank Deposits in Nigeria. Journal of Economics and Sustainable Development, 5(10), 49-57.

13 Thaker, H. M., Ee, T. S., Sin, C. F., & Man, W. H. (2014). The Macroeconomic Determinants of Bank Credit in Malaysia: An Analysis via the Error Correction Model (ECM). Skyline Business Journal, IX(1), 1-8.

14 Pradhan, R. S., & Paneru, D. (2017). Macroeconomic determinants of bank deposit of Nepalese commercial banks. SSRN Electronic Journal, 1-12. doi:10.2139/ssrn.3044098

15 Yakubu, I. N., & Abokor, A. H. (2020). Factors determining bank deposit growth in Turkey: an empirical analysis. Rajagiri Management Journal, 14(2), 121-132. doi:10.1108/RAMJ-05-2020-0017

16 ^гффпг^шЬ, t. (2021). « nbq^^bbm mbuj^b mbmbunLpjnLbbbp^ фшр1|шфпр^Ь фшрршф&|п Црш ипд^ш[-тЬтЬиш|шЬ фпр&пЬЬЬр^ фЬшЬнштт^ 'Лшф^т^тЬ hршцшрш^ш-hmpjmh q^sш|шЬ hшhqhu, 15(4), 153-161:

unemployed (ORU), consumer price index (CPI), the monthly growth rates were calculated by using ln(Yt/Yt-i) formula, then the monthly growth rates time series data were imported in "EViews 10", and econometric model was constructed.

The selection of the aforementioned socioeconomic factors has 3 reasons: the higher the households' saving and credit activity is, the better the characteristics of the quality of life are;17 the probable impact on households' saving behavior thereof and the relationship between them; the provision of data by the RA National Statistical Committee (NSC) (data of other indicators included in the request sent to NSC were not provided). The data sample or the number of time periods T is equal to 144, including the period from January 2009 to December 2020. Monthly data of bank deposits were received from the website of the Central Bank of Armenia (CBA) and they are publicly available.18 The chosen dependent variable is calculated on a cumulative basis, including old and newly involved deposits. On the basis of monthly data provided by NSC of the RA respective growth rates were computed in "Microsoft Excel 2019" software and were imported into "EViews 10" statistical software to construct econometric model and estimate the effects of variables. The best model suggested by the software was chosen to estimate the effect of studied factors on dependent variable. Diagnostic tests (serial correlation, normality, heteroscedasticity, stationarity and other tests) were conducted to check whether the model is robust and unbiased.

Analysis. First, the weight of resident households' deposits in bank deposits in the RA will be discussed. As Figure 1 shows, the weight of resident households' deposits in bank deposits accounts on average 49%, which indicates that almost half of bank deposits is invested by resident households of the RA. Moreover, according to data available on CBA website, the sum of deposits had gone up, while refinancing rate declined until December 2020.19 This highlights the substantiality of the current analysis.

According to Figure 2, residents' deposits in foreign currency prevail in bank deposits in the RA, including those involved from private and state-owned enterprises, households, non-profit organizations, other financial institutions, during 2009-2020. On the other hand, it started to decrease (the weight of residents' deposits in AMD grew until 57.53%) indicating a positive trend, since it can be described as an increase in trust towards the Armenian Dram. Interestingly, the weight of residents' deposits in AMD in bank deposits has been growing since 2015.

17 Belekhova, G. V., & Rossoshanskii, A. I. (2018). Assessing the Factors that Determine People's Financial Behavior: an Experience of Using Regression Analysis Based on Panel Data. Economic and Social Changes: Facts, Trends, Forecast, //(5), 198-213. doi:10.15838/esc.2018.5.59.13

18 CBA website, credits and deposits section: https://www.cba.am/en/sitepages/statmonetaryfinancial.aspx

19 CBA website: https://www.cba.am/en/sitepages/fmompiinterestrates.aspx, https://www.cba.am/en/sitepages/fmofinancialmarkets.aspx

Thrr iveiglit of resident households uleposits 11 bank deposits n the RA

Figure 1. The weight of resident households' deposits in bank deposits in the

RA during 2009-202020

Figure 2. The ratio of residents' deposits in AMD to residents' deposits in foreign currency in the RA21

As Figure 3 shows, during the well-known financial crisis the weight of resident households' deposits in AMD drastically dropped, which again indicates that during unstable economic situations population are prone to cash out or keep their savings in foreign currency (currencies) in order to hedge exchange rate risks (sometimes having in minds a speculation).

Figure 3. The weight of resident households' deposits in AMD in bank deposits in the

RA during 2009-202022

20 The Figure is created by the author.

21 Ibid.

22 Ibid.

+d%))+*)+*'&('&%$$#%%))*))*'&&'&#%##%%))+*)+

Although the weight of households' deposits in AMD has essentially gone up to 49.76% since 2015, the period average is 33%, which means the trust towards AMD has been increasing throughout population.

86%

Figure 4. The weight of resident households' term deposits in bank deposits in the

RA during 2009-202023

Figure 4 reflects the weight of resident households' term deposits in bank deposits in the RA during the study period. As can be seen, in the middle of the period, the discussed indicator tends to grow up to its maximum values, which describes people's long-term savings/investment horizon, macroeconomic stability. It is obvious that especially due to the 44-day war in 2020 the weight of term deposits plummeted to 72.58%.

Figure 5. The ratio of residents' term and demand deposits in AMD to residents' term and demand deposits in foreign currency in the RA during 2009-202024

As can be seen from Figure 5, resident households in the RA are prone to invest their term deposits in foreign currencies, on the other hand, deposits in AMD have been growing during the last five years owing to which the difference between the ratio of residents' demand deposits in AMD to residents' demand deposits in foreign currency and the ratio of residents' term deposits in AMD to residents' term deposits in foreign currency notably declined. Nonetheless, people still tend to invest term deposits in foreign currency.

Next, the model constructed with the use of 2009-2020 monthly time series data will be discussed. The model includes the dependent variable: monthly

23 The figure is created by the author.

24 Ibid.

growth rate of resident households' deposits in the RA - LN (RHD) and the following variables: monthly growth rates of average monthly nominal wage - LN (AMW), consumer price index - LN(CPI), the officially registered unemployed -LN (ORU), retail trade turnover - LN (RTT).

Times series data graphs indicate non-stationarity of data, to accept or reject it, Unit root test should be conducted.25,26 In this paper, Augmented Dickey-Fuller (ADF) test was chosen, the results are summarized in Table 1.

Table 1

Augmented Dickey-Fuller test results27

Intercept Trend and

Intercept

Level First difference Level First difference

Variable t-stat p-value t-stat I p-value t-stat p-value t-stat p-value

LN(RHD) -2.018 0.279 -6.979 0.000 -3.526 0.041 -6.952 0.000

LN(AMW) -2.782 0.064 -10.774 0.000 -3.013 0.133 -10.707 0.000

LN(CPI) -13.394 0.000 -9.999 0.000 -13.341 0.000 -9.982 0.000

LN(ORU) -2.292 0.176 -3.502 0.009 -2.297 0.433 -3.536 0.040

LN(RTT) -2.731 0.072 -8.693 0.000 -3.219 0.085 -8.691 0.000

The unit root test on the monthly series at level data and transformed to first difference and including intercept and both trend and intercept was carried out on the ADF test method. Table 1 shows that CPI is stationary at the level data (because the p-value is less than 0.05, which means the null hypothesis - there is a unit root - will be rejected at 5% significance level), the remaining variables are stationary at first difference (including only intercept and both trend and intercept).

Johansen cointegration test was carried out, which proved the cointegration between variables (the null hypothesis - there is no cointegration - was rejected at 5% significance level). It can be inferred from the mentioned tests that ARDL econometric model can be used.28,29 The latter enables us to estimate the speed of adjustment of variables to long-term equilibrium, short- and long-term relationships between variables.30

Choosing the optimal lag length followed the model selection process. The former was performed based on the lag order selection criteria. Since the smallest of the criteria was Akaike information criteria (AIC), models with up to 12 lags were estimated based on the AIC, and the best model was proposed

25 Stock, J. H., & Watson, M. W. (2020). Introduction to Econometrics (Fourth ed.). Pearson Education Limited.

26 Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach (7 ed.). Cengage Learning, Inc.

27 The table was created by the author on the basis of the data exported from "EViews 10" statistical software.

28 Pesaran, M. H., & Shin, Y. (1999). An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. In Econometrics and Economic Theory in the 20th Century (pp. 371 - 413). Cambridge University Press. doi:10.1017/CC0L521633230.011

29 Hassler, U., & Wolters, J. (2006). Autoregressive Distributed Lag Models and Cointegration. Allgemeines, 90(1), 59-74.

30 Shrestha, M. B., & Bhatta, G. R. (2018). Selecting Appropriate Methodological Framework for Time Series Data Analysis. The Journal of Finance and Data Science, 1-19. doi:10.1016/j.jfds.2017.11.001

ARDL (1,12,10,0,8) model. By transforming the long-term version of this model, it can be arrived to Equation (1) or cointegration equation.

EC = LN_RHD_ - (0.5120*LN_AMW_ - 3.6605*LN_CPI_ -

0.2989*LN_ORU_ + 0.1253*LN_RTT_ ) (1)

As can be noted from Table 2, the model is robust and has quite good fit, the adjusted R-squared is 77%. To check the existence of serial correlation, Lagrange multiplier test (LM test) was conducted. The null hypothesis of that test was not rejected at 5% significance level, which indicated no autocorrelation in time series. The residuals of the model are normally distributed since the Jarque-Bera test (value was 0.814) null hypothesis (the series are normally distributed) was not rejected at 5% significance level. To check the heteroscedasticity of the residuals, Breusch-Pagan-Godfrey test was carried out. Since the null hypothesis was not rejected at 5% significance level, it could be argued that the error term was homoscedastic. For checking model's stability, CUSUM and CUSUM of Squares tests were applied. According to Figure 6 and Figure 7, the null hypothesis again was not rejected at 5% significance level, which means the constructed model was stable (blue line does not cross red lines).

I-CUSUM--S* Bianfcaiim I [-CUSUM ufSqujms---S* Sgnlfcaiii» |

Figure 6. Stability test: CUSUM test3' Figure 7. Stability test: CUSUM of

Squares test32

Equation (1) shows that 1% change in AMW, CPI, ORU and RTT will cause a change in resident households' deposits by 0.512%, -3.660%, -0.299%, 0.125%, respectively. These coefficients show the estimated long-term relationship between the variables. The mentioned coefficients are summarized below in Table 2.

As illustrated in Table 2, all the socioeconomic factors except RTT were significant in long-term at 5% significance level. The null hypothesis of the F-Bounds test was rejected (F-value was 26.643), in other words, the relationship between variables was long-term.

Only CPI and RTT were significant in the short-term at 5% significance level, which pinpointed that by changing the values of those factors it is possible to impact resident households' saving behavior in the short-term. By the way, RTT was not significant in the long-run, but significant in the short-run. On the one

31 The figure is exported from "EViews 10" statistical software.

32 Ibid.

hand, AMW and ORU were significant in the long-term at 5% significance level, on the other hand - not significant in the short-term. Coifficient of CointEq(-l) was negative, significant, therefore variables were, indeed, cointegrated. In the short-term, 1% change in CPI would entail -0.328% change, and RTT 1% change could entail 0.034% change in dependent variable.

Table 2

Short- and Long-term estimates of EC version of ARDL model33

Variable | Coefficient | Std. Error | t-Statistic Prob. -1

LN_AMW_ LN_CPI_ LN_0RU_ 0.512 o ccr} 0.183 1 i^O/l 2.794 0.007

-3.660 -0.299 1.604 0.091 -2.283 -3.282 0.025 0.002

LN_RTT_ 0.125 0.083 1.504 0.136

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Short-term estimates

D(LN_AMW_) D(LN_CPI_) D(LN_RTT_) CointEq(-1) Adjusted R-squared Prob(F-statistic) 0.079 0.043 H 1 1.835 o m 0.070 H H/17

-0.328 0.034 -1.127 0.1 63 0.013 0.095 -2.0I3 2.634 -11.814 0.047 0.010 0.000

0.770 0.000

Durbin-Watson stat 2.118

Thus, as a result of model diagnostics tests, it turned out that model residuals are normally distributed, are homoscedastic, there is no serial correlation, and the model is stable. In this analysis, only four socioeconomic factors were included owing to some limitations (e.g. lack of monthly data of other groups of factors such as political, legal, demographic, financial, sociocultural, etc.), therefore no forecast is given, and further studies are needed.

Conclusions. Resident households' deposits in the RA comprise 49% of overall bank deposits, which underlines the significance of the research of population financial behavior. The analysis of bank deposits in the RA and its structure during 2009-2020, particularly saving behavior of resident households allow making the following conclusions:

• Due to uncertainty, resident households tend to cut term deposits during recessions.

• Resident households prefer investing in term deposits in foreign currency, although the weight of deposits in AMD has sustainably grown during the last 5 years.

• Out of the socioeconomic factors included in the model, AMW, CPI, ORU are significant in the 5% significance level in the long term.

• In the short term and 5% significance level consumer price index and retail trade turnover indicators are statistically significant, in other words, these factors faster affect saving behavior of resident households.

• Out of the discussed socioeconomic factors only inflation (CPI) significantly affects saving behavior both in short and long terms.

33 The table was created by the author on the basis of the data estimated by (long run form and error correction form) and exported from "EViews 10" statistical software.

Thus, by changing values of the discussed socioeconomic factors (by the interventions of the Government and the CBA) it is possible to affect population's saving behavior both in short and long terms. To promote household savings in the long run, the number of ORU should be reduced and inflation restrained, meanwhile by increasing AMW. In the short term, household saving behavior is influenced by inflation and RTT. In the case of inflation (which usually exists), household deposits decline, which in its turn may attract households to invest in business. The results are consistent with the findings mentioned in literature review; particularly CPI and ORU are significant factors in our model, too.

These findings may be useful for deposit portfolio managers and financial planners in banks, macroeconomic policymakers, households. Nevertheless, further studies are needed to estimate the impact of other factors (such as political, legal, sociocultural) on the saving behavior of people.

References

4. Горшкова, Н. В., & Ксенда, В. М. (2019). Модели финансового поведения населения в условиях наноэкономики (налоговый аспект). Вестник Волгоградского государственного университета. Экономика, 21(4), 207-218. doi:10.15688/ek.jvolsu.2019.4.20

5. Петенева, И. А. (2018). Влияние финансовых ожиданий на финансовое поведение населения России. Ученые записки Алтайского филиала Российской академии народного хозяйства и государственной службы при Президенте Российской Федерации (pp. 168-173). Барнаул: Алтайский филиал РАНХиГС.

6. Belekhova, G. V., & Rossoshanskii, A. I. (2018). Assessing the Factors that Determine People's Financial Behavior: an Experience of Using Regression Analysis Based on Panel Data. Economic and Social Changes: Facts, Trends, Forecast, 11(5), 198-213. doi:10.15838/esc.2018.5.59.13

7. Dubyna, M., Zhavoronok, A., Kudlaieva, N., & Lopashchuk, I. (2021). Transformation of Household Credit Behavior in the Conditions of Digitalization of the Financial Services Market. Journal of Optimization in Industrial Engineering, 195-200. doi:10.22094/J0IE.2020.677835

8. Eriemo, N. 0. (2014). Macroeconomic Determinants of Bank Deposits in Nigeria. Journal of Economics and Sustainable Development, 5(10), 49-57.

9. Granger, C. W. (1969). Instigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3), 424-438.

10. Hakobyan, A. A., & Grigoryan, E. M. (2021). Behavioral Finance and Traditional Finance: Theory and Analysis in Practice. Finance and Accounting, 4(1), 56-69.

11. Hassler, U., & Wolters, J. (2006). Autoregressive Distributed Lag Models and Cointegration. Allgemeines, 90(1), 59-74.

12. Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12, 231-254.

13. Kizyma, T. (2019). Credit behavior of households: structural and analytical aspects (Kredytna povedinka domohospodarstv: strukturno-analitychnyi aspekt). Svit finansiv (The world of finance), 55(1), 7-18.

14. Malkina, M. (2019). Determinants of Private Savings in the Form of Bank Deposits: A Case Study on Regions of the Russian Federation. Economies, 7(2), 1-22. doi:10.3390/economies7020063

15. Novikov, A. V., & Yarasheva, A. V. (2016). Financial Sociology: Textbook. Moscow: Finansovyi Universitet.

16. Pesaran, M. H., & Shin, Y. (1999). An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. In Econometrics and Economic Theory in the 20th Century (pp. 371 - 413). Cambridge University Press. doi:10.1017/CC0L521633230.011

17. Pradhan, R. S., & Paneru, D. (2017). Macroeconomic determinants of bank deposit of Nepalese commercial banks. SSRN Electronic Journal, 1-12. doi:10.2139/ssrn.3044098

18. Shrestha, M. B., & Bhatta, G. R. (2018). Selecting Appropriate Methodological Framework for Time Series Data Analysis. The Journal of Finance and Data Science, 1-19. doi:10.1016/j.jfds.2017.11.001

19. Stango, V., & Zinman, J. (2009). Exponential growth bias and household finance. The Journal of Finance, 64(6), 2807-2849. doi:10.1111/j.1540-6261.2009.01518.x

20. Stock, J. H., & Watson, M. W. (2020). Introduction to Econometrics (Fourth ed.). Pearson Education Limited.

21. Thaker, H. M., Ee, T. S., Sin, C. F., & Man, W. H. (2014). The Macroeconomic Determinants of Bank Credit in Malaysia: An Analysis via the Error Correction Model (ECM). Skyline Business Journal, IX(1), 1-8.

22. The World Bank. (2013, August). Financial Capability Surveys Around the World: Why Financial Capability is important and how surveys can help. 1-8. Retrieved from. https://documents1.worldbank.org/curated

/en/693871468340173654/pdf/807670WP0P14400Box0379820B0 0PUBLIC0.pdf

23. Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach (7 ed.). Cengage Learning, Inc.

24. Yakubu, I. N., & Abokor, A. H. (2020). Factors determining bank deposit growth in Turkey: an empirical analysis. Rajagiri Management Journal, 14(2), 121-132. doi:10.1108/RAMJ-05-2020-0017

25. Webpage of the National Statistical Committee of the RA. (n.d.). Retrieved from. https://www.armstat.am/am/

26. Website of the Central Bank of the RA. (n.d.). Retrieved from https://www.cba.am/am/SitePages/statmonetaryfinancial.aspx

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^Ьр, Ь^чЬиЬр Ь^' Jшhpшöш^ ш^итр^ цш2тп^шцЬи ц.рш^д^ш0 q.npöшqnLp^Ьph ph^Q, Jh2h^ шJ-иш^ш^ ш^ш^ш^ш^ ш2^шmш4шpÖQ, ищшпп^ш^ш^ ц.ЪЬрЬ b^^^Q: ARDL (autoregressive distributed lag) Jn^ih EC тшррЬршф Jh2ngn^ q.hшhшm4ЬL U ршgшhшJm4ЬL t цлрбп^ЬрЬ шq^ЬgnLpJnLtiQ пЬцЬп<ЬЬт т^ш^Ь т^тЬит-р|ш№ЬрЬ ^шррш^бЬ фш' ^шp6шdшJ^ЬmnLJ

U Ьp^шpшdшJ^ЬmnLJ: UJU шp^JnLtiftiЬpQ ^шрп^ ЬЬ

оц.тш^шр Ь|_ hшm^шщЬu ршh^ЬpnLJ, шЦшЬ^шфЬ фшрЬ-ph (щпртфЬ|) ^шnш4шpJшh U фф^ш^иш^ш^ щ|шhш4npJшh hшJшp:

^^ЬшршпЬр. |yhшJпqпLpJшh 1(шр£шфд, nbqptybhyi lpbшJfth ipbyitium.pjm.hhbpft ш^шЬ^ЬЬр, ипд^ш-фЬ^Ьиш^шЬ qnpönhhbp, ARDL dnq.b[, EC rfrnjb[: JEL: D10, E21, H31

DOI: 10.52174/1829-0280_2021_5_22

ЭРИК ГРИГОРЯН

Аспирант кафедры финансов АГЭУ

Влияние социально-экономических факторов на сберегательное поведение домашних хозяйств-резидентов РА.- В статье представлена структура депозитов, привлеченных коммерческими банками РА в 2009-2020 гг., в частности, динамика депозитов домашних хозяйств. Исследование было направлено на выявление воздействия четырех социально-экономических факторов на сберегательное поведение домашних хозяйств-резидентов РА. Была составлена эко-нометрическая модель временных рядов, чтобы оценить влияние социально-экономических факторов на сберегательное поведение домашних хозяйств-резидентов. Такими факторами являются розничный товарооборот, число официаль-

но зарегистрированных безработных, среднемесячная номинальная заработная плата, индекс потребительских цен. С помощью EC версии модели ARDL (autoregressive distributed lag) было оценено влияние этих факторов на сберегательное поведение в краткосрочной и долгосрочной перспективе. Результаты данной статьи могут быть полезны банкам для управления депозитным портфелем и финансового планирования.

Ключевые слова: сберегательное поведение, вклады домашних хозяйств-резидентов, социально-экономические факторы, модель ARDL, модель EC. JEL: D10, E21, H31

DOI: 10.52174/1829-0280_2021_5_22

IMPACT OF SOCIOECONOMIC FACTORS ON THE SAVING BEHAVIOR OF THE RA RESIDENT HOUSEHOLDS Текст научной статьи по специальности «Экономика и бизнес»

Аннотация научной статьи по экономике и бизнесу, автор научной работы — Grigoryan Erik

Похожие темы научных работ по экономике и бизнесу , автор научной работы — Grigoryan Erik

Текст научной работы на тему «IMPACT OF SOCIOECONOMIC FACTORS ON THE SAVING BEHAVIOR OF THE RA RESIDENT HOUSEHOLDS»