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Forecasting inflation rate: methods of univariate time series forecasting
(05/06/2019) Karameris, Konstantinos; Kyriazidou, Ekaterini; Tzavalis, Elias; Dimelis, Sophia
This dissertation attempts to forecast Greece's inflation rate, using functional univariate time series techniques. The goal is to cover a plethora of the most used methods of forecasting time series involving both Econometric and Machine Learning models. Some of the applied methods are the following: Simple Exponential Smoothing (SES), Holt's linear trend, Holt-Winter's Seasonal model and Box-Jenkins ARIMA (Autoregressive Integrated Moving Averages) methodology along with its extension the SARIMA (Seasonal Arima) methodology. Forecasting the inflation rate is of high importance given that the inflation is a key indicator of a country's economic activity. These forecasts can be used for the purpose of fiscal and monetary policy making, or in the private sector as the financial and labor market are greatly affected by changes in the inflation rate.
Markov Switching Models
Tsarouchas, Nikolaos-Marios; Athens University of Economics and Business, Department of Informatics; Dimelis, Sophia
This thesis displays a presentation of the Hamilton's Markov Switching model both in simple and State Space form. Moreover, the model is applied in the India's GDP and DJIA Index using R. This thesis is based on three chapters of Markov Switching models. First chapter covers the Classical approach, the parameters of which are estimated taking into consideration only the data sample and inferences are made conditional to that data. This presentation consists of two parts. The first part refers to the simple form of Markov Switching model which can be estimated by the EM-algorithm. The second part refers to the State Space form of Markov Switching model which can be estimated by Kalman Filter. The second chapter presents the Bayesian approach, according to which we treat the parameters as individual random variables with their own prior distributions which are determined by researcher beliefs or randomly by a Dirichlet process before the posterior distribution is determined taking into consideration the sample data. Similar to the first chapter both forms of the model are presented. In the Bayesian approach the parameters are estimated with Markov Chain Monte Carlo methods such as the Gibbs Sampling. In the last chapter a two-state Markov Switching model is applied in India's real GDP and the DJIA Index. The results of the implementation show that the Markov Switching model can fit well financial data and can detect the regimes with effectiveness.