SERII DE TIMP ÎN ECONOMIE: TEORIE ȘI APLICAȚII

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Time series analysis and forecasting cover a wide range of application areas, from social sciences to natural sciences, earth sciences and other fields of interest. Although the methods are applied to various data, the paper focuses on applications to economic data useful to students of economics, but also to researchers, teachers and practitioners. The paper is structured in 12 chapters, which combine the theoretical framework with numerous applications, addressing the following topics: defining time series, trend modeling through elementary functions and moving averages, estimating the seasonal component, exponential smoothing methods, random processes, the autocorrelation function and the partial autocorrelation function, tests for checking the stationarity of time series, integrated autoregressive moving average models, the Box-Jenkins procedure, SARIMA models, vector-autoregressive models, cointegration and apparent (false) regression, vector error correction models, distributed lag autoregressive models, Granger causality, the Toda-Yamamoto causality test, models with simultaneous equations.