FORECASTING CONSUMER PRICE INDEX (CPI) USING TIME SERIES MODELS AND MULTI REGRESSION MODELS (ALBANIA CASE STUDY)
Author(s) -
Eralda Gjika,
Llukan Puka,
Oriana Zaçaj
Publication year - 2018
Publication title -
deleted journal
Language(s) - English
Resource type - Conference proceedings
DOI - 10.3846/bm.2018.51
Subject(s) - inflation (cosmology) , econometrics , time series , index (typography) , series (stratigraphy) , regression analysis , consumer price index (south africa) , regression , economic indicator , economic forecasting , autoregressive integrated moving average , price index , economics , computer science , statistics , mathematics , monetary policy , macroeconomics , machine learning , paleontology , physics , theoretical physics , world wide web , biology
In this work we analyse the CPI index as the official index to measure inflation in Albania, Harmo-nized Indices of Consumer Prices (HICPs) as the bases for comparative measurement of inflation in European countries and other financial indicators that may affect CPI. This study is an attempt to model CPI based on combination of multiple regression model with time series forecasting models. In the first approach, time series models were used directly on the CPI time series index to obtain the forecast. In the second approach, the time series models (SARIMA, ETS) were used to model and simulate forecast for each subcomponent with significant correlation to CPI and then use the multiple regression model to obtain CPI forecast. The projection of this indicator is important for understand-ing the country's economic and social development. This study helps researchers in the field of time series modeling, economic analysis and investments.
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