Applied Econometrics: A Simple Introduction (Simple Introductions, #17) (eBook, ePUB)
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Applied Econometrics: A Simple Introduction offers a detailed guide to some of the central methods and applications of applied econometrics, with theory, models, calculations, and graphs to support analysis.S&P 500 equities, GSCI commodities, and US Treasury Bill risk-free rate datasets are assessed for their data distributions, autocorrelation, and stationarity. The Engle-Granger 2 step method, Johansen test and the Vector Error Correction Model test for and correct cointegration.ARMA models determine the optimal AR and MA processes to model returns data, and GARCH models assess the optimal p...
Applied Econometrics: A Simple Introduction offers a detailed guide to some of the central methods and applications of applied econometrics, with theory, models, calculations, and graphs to support analysis.
S&P 500 equities, GSCI commodities, and US Treasury Bill risk-free rate datasets are assessed for their data distributions, autocorrelation, and stationarity. The Engle-Granger 2 step method, Johansen test and the Vector Error Correction Model test for and correct cointegration.
ARMA models determine the optimal AR and MA processes to model returns data, and GARCH models assess the optimal p and q number of lags to model variance, using the Akaike Information Criterion. Alternative GARCH versions are examined.
Dynamic portfolio strategies are evaluated using Sharpe Ratio portfolio performance evaluation tools, with a focus on the 2007-8 global financial crisis period. Static portfolio strategies are assessed using ARMA return and GARCH variance forecasting. Results are used alongside established financial literature to assess the optimal portfolio strategy.
S&P 500 equities, GSCI commodities, and US Treasury Bill risk-free rate datasets are assessed for their data distributions, autocorrelation, and stationarity. The Engle-Granger 2 step method, Johansen test and the Vector Error Correction Model test for and correct cointegration.
ARMA models determine the optimal AR and MA processes to model returns data, and GARCH models assess the optimal p and q number of lags to model variance, using the Akaike Information Criterion. Alternative GARCH versions are examined.
Dynamic portfolio strategies are evaluated using Sharpe Ratio portfolio performance evaluation tools, with a focus on the 2007-8 global financial crisis period. Static portfolio strategies are assessed using ARMA return and GARCH variance forecasting. Results are used alongside established financial literature to assess the optimal portfolio strategy.
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