A nonlinear ARDL approach for forecasting Malaysian imports

Mohamed AH  milad

Authors

Mohamed AH Milad Academy for Postgraduate Studies, Tripoli, Libya Author

Keywords:

A nonlinear ARDL, Forecasting, Malaysian Imports

Abstract

This study aims to develop a suitable model to forecast Malaysian imports within the sample. Imports are a key indicator that provides a clear picture of a country's macroeconomic situation, as defined by international bodies such as the World Bank. This study used a nonlinear ARDL approach proposed in this research. thereby facilitating the development of a more effective framework for forecasting Malaysia’s import volume and enhancing existing prediction methods. The prediction performance is assessed using the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results clearly show that a nonlinear ARDL model achieves high predictive accuracy. Its primary strengths lie in its predictive power and its ability to address regression model issues such as residual autocorrelation by providing structural and time-series explanations for those parts of the variance. Model parameters and predictions were estimated using 52 observations. Future research may expand on these findings by exploring other approaches, such as combined models that account for autocorrelation or heterogeneity, or by applying larger datasets to Malaysia’s imports and comparing the results with those of the present study.

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References

Aimer, N. (2019). THE IMPACT OF OIL PRICE SHOCKS ON THE ECONOMIC GROWTH OF LIBYA: AN ARDL-BOUND TESTING APPROACH. Journal of Empirical Economics and Social Sciences, 2(1), 59-81.

Albiman, M., & Suleiman, N. (2016). The relationship among export, import, capital formation and economic growth in Malaysia. Journal of Global Economics, 4(2), 2375-4389.

Cavusoglu, B., & Alsabr, M. (2017). An ARDL co-integration approach to inflation, FDI, and economic growth in Libya. Journal of Advanced Research in Law and Economics, 8(8 (30)), 2373-2382.

Duasa, J. (2007). Determinants of Malaysian trade balance: An ARDL bound testing approach. Global economic review, 36(1), 89-102.

Goyal, R. K., Jayakumar, N., & Hashim, M. (2011). A comparative study of experimental optimization and response surface optimization of Cr removal by emulsion ionic liquid membrane. Journal of hazardous materials, 195, 383-390.

Granger, C. W. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16(1), 121-130.

Kogid, M., Mulok, D., Ching, K. S., Lily, J., Ghazali, M. F., & Loganathan, N. (2011). Does import affect economic growth in Malaysia. The Empirical Economics Letters, 10(3), 297-307.

Milad, A., Ibrahim, M., & Marappan, S. (2015a). Regression analysis to forecast Malaysia's imports of crude material. International Journal of Management and Applied Science, 1(4), 121-130.

Milad, M. A., & Duzan, H. M. B. (2024). Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports. Open Journal of Statistics, 14(2), 163-178.

Milad, M. A., & Ibrahim, R. I. (2016). A Robust Composite Model Approach for Forecasting Malaysian Imports: A Comparative Study. Journal of Applied Sciences, 16(6), 279.

Milad, M. A., Ibrahim, R. I., & Marappan, S. (2015b). A comparison among two composite models (without regression processing) and (with regression processing), applied on Malaysian imports. Applied Mathematical Sciences, 9(116), 5757-5767.

Natsiopoulos, K., & Tzeremes, N. G. (2022). ARDL bounds test for cointegration: Replicating the Pesaran et al.(2001) results for the UK earnings equation using R. Journal of applied econometrics, 37(5), 1079-1090.

Omri, A., & Nguyen, D. K. (2014). On the determinants of renewable energy consumption: International evidence. Energy, 72, 554-560.

Parikh, A., & Edwards, R. (1980). Structural analysis and forecasts of commodity imports using cross-section relationships. Empirical Economics, 5, 109-127.

Pasaran, S., Shine, Y., & Smith, R. (2001). Bound testing approach to the analysis of level relationship. Journal of applied econometrics, 16(3), 289-326.

Phillips, P. C. (1986). Understanding spurious regressions in econometrics. Journal of Econometrics, 33(3), 311-340.

Qamruzzaman, M., & Karim, S. (2020). Nexus between economic volatility, trade openness and FDI: An application of ARDL, NARDL and asymmetric causality. Asian Economic and Financial Review, 10(7), 790.

SANUSI, N. A., MOOSIN, A. F., & KUSAIRI, S. (2020). Neural network analysis in forecasting the Malaysian GDP. The Journal of Asian Finance, Economics and Business, 7(12), 109-114.

Schorderet, Y. (2001). Revisiting Okun's law: an hysteretic perspective.

Semudram, M. (1982). A Macromodel of the Malaysian Economy: 1959–77. The Developing Economies, 20(2), 154-172.

Shamsudin, M. N., & Arshad, F. M. (2000). Short term forecasting of Malaysian crude palm oil prices. Short Term Forecasting of Malaysian Crude Palm Oil Prices.

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in honor of Peter Schmidt: Econometric methods and applications (pp. 281-314). Springer.