Comparison of Spatial Weights in Modeling Inflation and Money Supply Using the Spatial Vector Autoregressive Model with Calendar Variations

The Turkish economy has faced many economic difficulties throughout it's history. At this point, predicting inflation accurately is very important for policy makers, businesses, investors and consumers. This study aims to estimate the Turkish Consumer Price Index. Producer price index, M1 money supply, gold price, dollar price, natural gas price and interest rate variables were used to estimate the CPI for Turkey. The variables used in the research were obtained through EVDS, the Central Bank's Electronic Data Management System. Monthly data from January 2003 to August 2023 was used in the study. The obtained data were estimated using DDPG, XGBoost, SVR, KNN and CNN-BiLSTM methods. Model performances were compared using RMSE, MSE, MAE, MAPE and R2 statistical coefficients. When model performances were evaluated, the best CPI prediction for Turkey was obtained by the SVR method.

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Addition of Subset and Dummy Variables in the Threshold Spatial Vector Autoregressive with Exogenous Variables Model to Forecast Inflation and Money Outflow

Setiawan,

Sohibien,

Prastyo

et al. 2024

Economies

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The TSpVARX model can be used in inflation and money outflow forecasting by accommodating the reciprocal relationship among endogenous variables, the influence of exogenous variables, inter-regional linkages, and the nonlinearity of the relationship between endogenous and predetermined variables. However, the impact of some events, such as Eid al-Fitr and fuel price adjustments, still cannot be accommodated in the TSpVARX model. This condition causes inflation and money outflow forecasting using TSpVARX to be unsatisfactory. Our study is to improve the forecasting performance of the TSpVARX model by adding subset and dummy variables. We use a 12th lag subset variable to capture seasonal effects and a dummy variable to represent fuel price changes. These additions enhance the model’s accuracy in forecasting inflation and money outflow by accounting for recurring patterns and specific events, like fuel price changes. Based on the RMSE values of the training and testing data, we can conclude that forecasting inflation and money outflow using TSpVARX with the addition of subset and dummy variables is better than the regular TSpVARX. The inflation and money outflow forecasting generated after the addition of subset and dummy variables are also more fluctuating as in the movement of the actual data.

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Comparison of Spatial Weights in Modeling Inflation and Money Supply Using the Spatial Vector Autoregressive Model with Calendar Variations

Cited by 3 publications

References 8 publications

Spatial Vector Autoregressive with Metric Exogenous Variable (SpVARX) for Inflation and Outflow Forecasting

Spatial Vector Autoregressive with Metric Exogenous Variable (SpVARX) for Inflation and Outflow Forecasting

Forecasting Consumer Price Index Using Macroeconomic Variables: A Comparative Analysis of Machine Learning and Deep Learning Approaches

Addition of Subset and Dummy Variables in the Threshold Spatial Vector Autoregressive with Exogenous Variables Model to Forecast Inflation and Money Outflow

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