Nowcasting and Near-Term Forecasting Cambodia’s Economy

By Dyna Heng, Fei Han, Sovanney Chey, Raksmey Uch, Dy Kuchsa, Pholla Phork

July 12, 2024

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Dyna Heng, Fei Han, Sovanney Chey, Raksmey Uch, Dy Kuchsa, and Pholla Phork. Nowcasting and Near-Term Forecasting Cambodia’s Economy, (USA: International Monetary Fund, 2024) accessed December 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

Assessing the current state of the economy and forecast the economic outlook in the next few quarters are important inputs for policymakers. This paper presents a suite of models with an integrated approach to forecast Cambodia’s economy in the current and next few quarters. First, we estimate historical quarterly GDP using information extracted from high-frequency indicators to construct quarterly nowcasting model. Second, we forecast current economic activities using a high-frequency data such as credit, export, tourist arrival, foreign reserves, and trading partner’s GDP. Third, we present inflation forecasting models for Cambodia. Fourth, the paper present a vector autoregression model to forecast Cambodia’s GDP in the next few quarters using global forecasts of China’s and US’s economy as well as oil and rice price. This paper showcase how high-frequency data set can be utilized in assessing current economic activities in countries with limited and lagged data.

Subject: Credit, Econometric analysis, Economic forecasting, Exports, Inflation, International trade, Money, Prices, Vector autoregression

Keywords: Cambodia, Cambodia's GDP, Credit, Developing countries, Exports, Global, High-frequency data set, Inflation, Inflation forecasting model, Near-term forecasting Cambodia's economy, Nowcasting, Nowcasting GDP, Vector autoregression

Publication Details

Pages:

46
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2024/147
Stock No:

WPIEA2024147
ISBN:

9798400280931
ISSN:

1018-5941

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