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Forecasting inflation -- An empirica...

Zhao, Peng.

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Forecasting inflation -- An empirical study on predictive power of various time-series models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Forecasting inflation -- An empirical study on predictive power of various time-series models./
作者:	Zhao, Peng.
面頁冊數:	60 p.
附註:	Source: Masters Abstracts International, Volume: 54-06.
Contained By:	Masters Abstracts International54-06(E).
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1594275
ISBN:	9781321912579

Forecasting inflation -- An empirical study on predictive power of various time-series models.
Zhao, Peng.

Forecasting inflation -- An empirical study on predictive power of various time-series models. - 60 p.

Source: Masters Abstracts International, Volume: 54-06.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2015.

In this thesis, I investigate the background and causes of major inflation in recent history and empirically study forecasting of future quarterly inflation rates for three typical countries and regions: the United States, the United Kingdom, and the Eurozone. In particular, I empirically investigate the predictive power of four commonly used econometric models: the AR model, the ADL model, the ARIMA model, and the VAR model.

ISBN: 9781321912579Subjects--Topical Terms:

517137
Economics.

Forecasting inflation -- An empirical study on predictive power of various time-series models.
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245 1 0 $a Forecasting inflation -- An empirical study on predictive power of various time-series models.
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500 $a Adviser: Craig A. Depken, II.
502 $a Thesis (M.S.)--The University of North Carolina at Charlotte, 2015.
520 $a In this thesis, I investigate the background and causes of major inflation in recent history and empirically study forecasting of future quarterly inflation rates for three typical countries and regions: the United States, the United Kingdom, and the Eurozone. In particular, I empirically investigate the predictive power of four commonly used econometric models: the AR model, the ADL model, the ARIMA model, and the VAR model.
520 $a I compare each model's forecasting accuracy by calculating the corresponding RMSFE (Root Mean Squared Forecast Error) of pseudo-out-of-sample forecasting for each country or region. The model that exhibits the smallest RMSFE is my preferred model.
520 $a The results suggest that, for each country or region in my dataset, the ARIMA model significantly outperforms the other three models. By determining ARIMA as the most preferable model, I use the ARIMA model to forecast the future (two-year ahead) quarterly inflation rate.
590 $a School code: 0694.
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