Forecasting for a Credit Loan from Households in South Korea

Jeong,, D. (2017). Forecasting for a Credit Loan from Households in South Korea. The Journal of Industrial Distribution & Business, 8(4), 15-21, https://doi.org/https://doi.org/10.13106/ijidb.2017.vol8.no4.15

copy

Abstract

Purpose - In this work, we examined the causal relationship between credit loans from households (CLH), loan collateralized with housing (LCH) and an interest of certificate of deposit (ICD) among others in South Korea. Furthermore, the optimal forecasts on the underlying model will be obtained and have the potential for applications in the economic field. Research design, data, and methodology - A total of 31 realizations sampled from the 4th quarter in 2008 to the 4th quarter in 2016 was chosen for this research. To achieve the purpose of this study, a regression model with correlated errors was exploited. Furthermore, goodness-of-fit measures was used as tools of optimal model-construction. Results - We found that by applying the regression model with errors component ARMA(1,5) to CLH, the steep and lasting rise can be expected over the next year, with moderate increase of LCH and ICD. Conclusions - Based on 2017-2018 forecasts for CLH, the precipitous and lasting increase can be expected over the next two years, with gradual rise of two major explanatory variables. By affording the assumption that the feedback among variables can exist, we can, in the future, consider more generalized models such as vector autoregressive model and structural equation model, to name a few.

keywords: Credit Loan for Households, Multiple Regression with Correlated Errors, Exponential Smoothing Method, Forecast

Reference

Akaike, H. (1970). Statistical predictor identification. Annals of the Institute of Statistical Mathematics, 22, 203-217.

Anderson, J. R. (1994). Simpler exponentially weighted moving averages with irregular updating periods. Journal of the Operational Research Society, 45, 486.

Anderson, T. W. (1971). The statistical analysis of time series. New York: Wiley.

Archibald, B. C. (1990). Parameter space of the Holt-Winters’ model. International Journal of Forecasting, 6, 199-209.

Archibald, B. C., & Koehler, A. B. (2003). Normalization of seasonal factors in Winters’ methods. International Journal of Forecasting, 19, 143-148.

Bartolomei, S .M., & Sweet, A. L. (1989). A note on a comparison of exponential smoothing methods for forecasting seasonal series. International Journal of Forecasting, 5, 111-116.

Bianchi, L., Jarrett, J., & Hanumara, R. C. (1998). Improving forecasting for telemarketing centers by ARIMA modeling with intervention. International Journal of Forecasting, 14, 497-504.

Bowerman, B. L., O’Connell, R., & Koehler, A. B. (2005). Forecasting, time series, and regression (4th edition). Pacific Grove, CA: Duxbury Press.

Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd edition). Englewood Cliffs, NJ: Prentice Hall.

10.

Brown, R. G. (1959). Statistical forecasting for inventory control. New York: McGraw-Hill.

11.

Brown, R. G. (1963). Smoothing, forecasting and prediction of discrete time series. Englewood Cliffs, NJ:Prentice-Hall.

12.

Broze, L., & Mélard, G. (1990). Exponential smoothing:Estimation by maximum likelihood. Journal of Forecasting, 9, 445-455.

13.

Chatfield, C. (1988). What is the ‘best method’ of forecasting?. Journal of Applied Statistics, 15, 19-38.

14.

Chatfield, C. (1993). Calculating interval forecasts. Journal of Business and Economic Statistics, 11, 121-135.

15.

Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society, Series A, 158, 419-466.

16.

Chatfield, C. (1996). Model uncertainty and forecast accuracy. Journal of Forecasting, 15, 495-508.

17.

Chatfield, C. (1997). Forecasting in the 1990s. Journal of the Royal Statistical Society, Series D, 46, 461-473.

18.

Chatfield, C. (2002). Confessions of a pragmatic statistician. Journal of the Royal Statistical Society, Series D, 51, 1-20.

19.

Chio, E. Y. (2016). The effects of household debt on household consumption through quantile regression analysis. Journal of Human and Social Science, 17(1), 589-613.

20.

Fuller, W. A. (1976). Introduction to statistical time series. New York: John Wiley & Sons, Inc.

21.

Gardner, E. S. Jr. (1985). Exponential smoothing: the state of the art. Journal of Forecasting, 4, 1–28.

22.

Gardner, E. S. Jr. (2006). Exponential smoothing: The state of the art Part II. International Journal of Forecasting, 22, 637–677.

23.

Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.

24.

Hurvich, C. M., & Tsai, C. L. (1990). The impact of model selection on inference in linear regression. American Statistician, 44, 214–217.

25.

Hwang, J. Y., & Lee, S. H. (2015). Household debt and its impacts on consumption and income in Korea. Financial Policy Reviews, 17(2), 127-153.

26.

Jeong, D. B. (2009). Demanding forecasting of time series I. Seoul, Korea: Hannarae Academy.

27.

Jeong, D. B. (2010). Demanding forecasting of time series I. Seoul, Korea: Hannarae Academy.

28.

Jeon, S. M., & Lee, K. S. (2013). Dynamic correlation analysis between housing price and household loans. The Review of Eurasian Studies, 10(3), 1-27.

29.

Jeon, S. M., & Lee, K. S. (2016). Relationship between asset prices and household loans using a structural vector error correction model. The Korean Journal of Financial Engineering, 15(1), 93-123.

30.

Lee, S. N. (2016). An empirical study on affecting factors of households debt. Journal of Digital Convergence, 14(5), 177-183.

31.

Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297303.

32.

Pankratz, A. (1983). Forecasting with univariate Box-Jenkins models: concepts and cases. New York: John Wiley & Sons, Inc.

33.

Pankratz, A. (1991). Forecasting with dynamic regression models. New York: John Wiley & Sons, Inc.

34.

Roberts, S. A. (1982). A general class of Holt-Winters type forecasting models. Management Science, 28(7), 808-820.

35.

Rosas, A. L., & Guerrero, V. M. (1994). Restricted forecasts using exponential smoothing techniques. International Journal of Forecasting, 10(4), 515-527.

36.

Ryoo, J. Y., & Jeon, J. Q. (2017). Emprical study on the choice criterion of services of household loans and determinants of housing mortgage rate. Korea Journal of Business Administration, 30(2), 1-28.

37.

Tsay, R. S., & Tiao, G. C. (1984). Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARMA Models. Journal of the American Statistical Association, 79, 84-96.

38.

Trigg, D. W., & Leach, D. H. (1967). Exponential smoothing with an adaptive response rate. Operational Research Quarterly, 18, 5359.

39.

Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324–342.

40.

Wilkinson, L., & Dallal, G. E. (1981). Tests of significance in forward selection regression with an F-to enter stopping rule. Technometrics, 23, 377–380.

바로가기메뉴

Article Contents

Vol.8 No.4

Forecasting for a Credit Loan from Households in South Korea

Abstract

Reference

The Journal of Industrial Distribution & Business