TY - GEN AB - 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 naïve 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. AU - Hoa, Tran Thanh CY - Geneva DA - 2017 DA - 2017 DO - 10.71609/iheid-cq2j-3d16 DO - doi ID - 294965 L1 - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf L1 - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf?subformat=pdfa L2 - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf L2 - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf?subformat=pdfa L4 - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf L4 - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf?subformat=pdfa LK - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf LK - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf?subformat=pdfa N2 - 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 naïve 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. PB - The Graduate Institute of International and Development Studies, International Economics Department PP - Geneva PY - 2017 PY - 2017 T1 - Forecasting inflation in Vietnam with univariate and vector autoregressive models TI - Forecasting inflation in Vietnam with univariate and vector autoregressive models UR - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf UR - https://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf?subformat=pdfa Y1 - 2017 ER -