Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162466
Authors: 
Tran Thanh Hoa
Year of Publication: 
2017
Series/Report no.: 
Graduate Institute of International and Development Studies Working Paper 05-2017
Abstract: 
In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naive benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on Atkeson-Ohanian (2001), Gosselin-Tkacz (2001) and the specific properties of inflation in Vietnam. Then, I compute the pseudo out-of-sample root mean square error (RMSE) as a measure of forecast accuracy for the candidate models and benchmarks, using rolling window and expanding window forecasting evaluation strategies. The process is applied to both monthly and quarterly data from Vietnam for the period from 2000 through the first half of 2015. I also apply the forecastencompassing Diebold-Mariano test to support choosing statistically better forecasting models from among the different candidates. I find that VAR_m2 is the best monthly model to forecast inflation in Vietnam, whereas AR(6) is the best of the quarterly forecasting models, although it provides a statistically insignificantly better forecast than the benchmark BM2_q.
Subjects: 
Inflation
Forecast
Univariate Models
Vector Autoregressive Models
Forecast Accuracy
JEL: 
C22
C32
C51
C53
E31
E37
Document Type: 
Working Paper

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