Forecasting Inflation in Lao PDR:
A Comparison of ARIMA and VAR Models

 

 

Received:
July 14, 2024

Revised:
August 8, 2024

Accepted:
September 6, 2024

 

Alounny Vorachakdaovy

Student in Master of Business Administration, Majoring in Finance,

Faculty of Economics and Business Management, National University of Laos.

Dr.Piya Wongpit

Associate Professor of Finance and Banking Department,

Faculty of Economics and Business Management, National University of Laos.

(Corresponding Author)

 

 

Keywords:

Inflation Forecasting,
ARIMA and VAR

ABSTRACT

This study aims to use a univariate time series in the form of an Autoregressive Integrated Moving Average (ARIMA) model developed by Box and Jenkins and a multivariate time series model in the form of a Vector Autoregressive model (VAR) to forecast inflation for Lao PDR. Our focus is to use change in quarterly inflation data obtained from the Bank of Lao PDR over the period 2005: Q1 to 2023: Q3 to analyze the forecast performance of the two models using measures of accuracy such as RMSE and MAPE statistics. So, the best forecasting model for predicting inflation in Lao PDR will be selected based on different diagnostic and evaluation criteria. ARIMA model (1,1,3) was the best model, and the VAR model used a vector error correction model. The Impulse Response Function and Variance Decomposition analyses reveal consistent results, indicating that the variables experience sudden changes or shocks. However, the Var model had the least minimum square error and is the closest approximate to current inflation at 26.4 percent in Q3:2023 in Lao PDR. The study forecasted core inflation using VAR for the quarterly of 2023: Q3 to 2024: Q4 to be 28.8 percent.

 

 

 

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