Modeling Vector Autoregressive and Autoregressive Distributed Lag of the Beef and Chicken Meat Prices during the Covid-19 Pandemic in Indonesia

Samingun Handoyo, Ying-Ping Chen, Tiara Mawidha Shelvi, Heni Kusdarwati


The impact of the COVID-19 pandemic has spread to all aspects of life. Modeling the price of beef and chicken meat is very important for the government to avoid extreme fluctuations of both commodities in the prices so that society's purchasing power can be maintained. This study has several objectives, namely building VAR and ARDL models from multiple time series data (beef and chicken meat prices), conducting variable selection with forwarding subset selection on input lag in the ARDL model, and measuring the performance of the VAR and ARDL models on the both of beef and chicken meat prices based on the value of RMSE, MAE, and R_square both in the training and testing set. The novelty in this study is to propose an identification method for the lag inputs of the ARDL model based on the criteria of both the Alkaide Information criteria (AIC) value and the adjusted R square value by visualizing both criteria for all possible amounts of lag inputs. The results of the identification of the VAR model structure using the conventional method in time series modeling are yielded the different lag inputs that are compared to the ARDL model structure with lag inputs identified by using the proposed method. The ARDL model of the beef and chicken meat prices has better performance than the VAR model both on training and testing sets. In addition, the resulting VAR model also clearly shows the occurrence of overfitting problems.

Keywords: ARDL modeling, feature selection, multiple time series, VAR modeling.


Full Text:



NUGROHO W. H., HANDOYO S., and AKRI Y. J. An Influence of Measurement Scale of Predictor Variable on Logistic Regression Modeling and Learning Vector Quantization Modeling for Object Classification. International Journal of Electrical and Computer Engineering, 2018, 8(1): 333-343.

HANDOYO S., & KUSDARWATI H. Implementation of Fuzzy Inference System for Classification of Dengue Fever on the villages in Malang. Basic Science International Conferences, 2019, 546(5): 052026.

HANDOYO S., CHEN Y. P., IRIANTO G., and WIDODO A. The Varying Threshold Values of Logistic Regression and Linear Discriminant for Classifying Fraudulent Firm. Mathematics and Statistics, 2021, 9(2): 135-143.

UTAMI H. N., CANDRA, and HANDOYO S. The Effect of Self Efficacy and Hope on Occupational Health Behavior in East Java of Indonesia. International Journal of Scientific & Technology Research, 2020, 9(2): 3571-3575.

KUSDARWATI H., and HANDOYO S. Modeling Treshold Liner in Transfer Function to Overcome Non Normality of the Errors. Basic Science International Conferences, 2019, 546(5): 052039.

HANDOYO S., MARJI, PURWANTO I. N., and JIE F. The Fuzzy Inference System with Rule Bases Generated by using the Fuzzy C-Means to Predict Regional Minimum Wage in Indonesia. International Journal of Operations and Quantitative Management, 2018, 24(4): 277-292.

KUSDARWATI H., & HANDOYO S. System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model. International Journal Electrical & Computer Engineering, 2018, 8(4): 2327-2337.

PUROHIT S. K., PANIGRAHI S., SETHY P. K., and BEHERA S. K. Time series forecasting of price of agricultural products using hybrid methods. Applied Artificial Intelligence, 2021: 1-19.

PRAVILOVIC S., BILANCIA M., APPICE A., and MALERBA D. Using multiple time series analysis for geosensor data forecasting. Information Sciences, 2017, 380: 31-52.

SHIH S. Y., SUN F. K., and LEE H. Y. Temporal pattern attention for multivariate time series forecasting. Machine Learning, 2018, 108(8): 1421-1441.

HANDOYO S., & CHEN Y. P. The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part. International Journal of Engineering Trends and Technology, 2020, 68(12): 118-122.

MARICA V. G., and HOROBET A. Conditional Granger Causality and Genetic Algorithms in VAR Model Selection. Symmetry, 2019, 11(8): 1004.

CHEN Y., & LIU D. Government spending shocks and the real exchange rate in China: Evidence from a sign-restricted VAR model. Economic Modelling, 2018, 68: 543-554.

CHANDIO A. A., JIANG Y., RAUF A., AHMAD F., AMIN W., and SHEHZAD K. Assessment of formal credit and climate change impact on agricultural production in Pakistan: a time series ARDL modeling approach. Sustainability, 2020, 12(13): 5241.

SOHAIL M. T., ULLAH S., MAJEED M. T., and USMAN A. Pakistan management of green transportation and environmental pollution: a nonlinear ARDL analysis. Environmental Science and Pollution Research, 2021, 28: 29046–29055.

IRCIO J., LOJO A., MORI U., and LOZANO J. A. Mutual information based feature subset selection in multivariate time series classification. Pattern Recognition, 2020, 108: 107525.

ZHU X., DU W., GENG G., and XUE B. Research on pork price prediction based on multi-dimensional feature analysis and machine learning. Proceedings of the 2021 ACM International Conference on Intelligent Computing and its Emerging Applications, 2021, pp. 111-116.

ZHANG H., WANG J., and MARTIN W. Factors affecting households' meat purchase and future meat consumption changes in China: a demand system approach. Journal of Ethnic Foods, 2018, 5(1): 24-32.

LUSK J. L., TONSOR G. T., and SCHULZ L. L. Beef and pork marketing margins and price spreads during COVID 19. Applied Economic Perspectives and Policy, 2021, 43(1): 4-23.

AGUIRRE A., & AGUIRRE L. A. Time series analysis of monthly beef cattle prices with nonlinear autoregressive models. Applied Economics, 2000, 32(3): 265-275.

ZENG B., LI S., MENG W., and ZHANG D. An improved gray prediction model for China’s beef consumption forecasting. PloS One, 2019, 14(9): 0221333.

ALDERINY M. M., ALRWIS K. N., AHMED S. B., and ALDAWDAHI N. M. Forecasting Saudi Arabia’s production and imports of broiler meat chickens and its effect on expected self-sufficiency ratio. Journal of the Saudi Society of Agricultural Sciences, 2020, 19(4): 306-312.

PAPARODITIS E., & POLITIS D. N. The asymptotic size and power of the augmented Dickey–Fuller test for a unit root. Econometric Reviews, 2018, 37(9): 955-973.

SHRESTHA M. B., & BHATTA G. R. Selecting appropriate methodological framework for time series data analysis. The Journal of Finance and Data Science, 2018, 4(2): 71-89.

PERVUKHINA E., EMMENEGGER J. F., GOLIKOVA V., and OSIPOV K. An optimization technique based on a vector autoregression model with state space representation: application to Ukrainian cargo transport data. Optimization, 2014, 63(1): 93-108.

SARKER B., & KHAN F. Nexus between foreign direct investment and economic growth in Bangladesh: an augmented autoregressive distributed lag bounds testing approach. Financial Innovation, 2020, 6(1): 1-18.

CEESAY E. K., FRANCIS P. C., JAWNEH S., NJIE M., BELFORD C., and FANNEH M. M. Climate change, growth in agriculture value-added, food availability and economic growth nexus in the Gambia: a Granger causality and ARDL modeling approach. SN Business & Economics, 2021, 1(7): 1-31.;h=repec:spr:snbeco:v:1:y:2021:i:7:d:10.1007_s43546-021-00100-6

HASTIE T., TIBSHIRANI R., and TIBSHIRANI R. Best subset, forward stepwise or lasso? Analysis and recommendations based on extensive comparisons. Statistical Science, 2020, 35(4): 579-592.

SMITH G. Step away from stepwise. Journal of Big Data, 2018, 5(1): 1-12.

HANDOYO S., & MARJI. The Fuzzy Inference System with Least Square Optimization for Time Series Forecasting. Indonesian Journal of Electrical Engineering and Computer Science, 2018, 7(3): 1015-1026


  • There are currently no refbacks.