Time Series Models Based on Generalized Linear Models: Some Further Results

Biometrics ◽  
1994 ◽  
Vol 50 (2) ◽  
pp. 506 ◽  
Author(s):  
W. K. Li
Keyword(s):  
Time Series ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Time Series Models

scholarly journals Book Reviews

2016 ◽  
Vol 27 (3) ◽  
Author(s):  
Klaus Pötzelberger ◽  
Werner G. Müller ◽  
Hans Kellerer ◽  
Mushtaq Hussain ◽  
Michael Schimek ◽  
...  
Keyword(s):  
Time Series ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Extreme Values ◽  
Multivariate Time Series ◽  
Empirical Processes ◽  
Lack Of Fit ◽  
Probability And Statistics ◽  
Lack Of Fit Tests

Weak Convergence and Empirical Processes (A.W. van der Vaart, J.A. Wellner)Quasi-Likelihood and its Application. A General Approach to Parameter Estimation(C.C. Heyde)Wahrscheinlichkeitsrechnung und Statistik in Beispielen und Aufgaben (V. Nollau,L. Partzsch, R. Storm, C. Lange)A First Course in Multivariate Statistics (B. Flury)Empirische Forschungsmethoden (W. Stier)Applying Generalized Linear Models (J.K. Lindsey)Analyse von Tabellen und kategorialen Daten (H.J. Andreß, J.A.Hagenaars, S. Kühnel)Elements of Multivariate Time Series Analysis (G.C. Reinsel)Nonparametric Smoothing and Lack-of-Fit Tests (J.D. Hart)Modelling Extremal Events for Insurance and Finance (P. Embrechts, C. Klüppelberg,T. Mikosch)Statistical Analysis of Extreme Values (R.D. Reiss, M. Thomas)Das Quotenverfahren (A. Quatember)Prophetentheorie (F. Harten, A. Meyerthole, N. Schmitz)Advances in Combinational Methods and Applications to Probability and Statistics.(N. Balakrishnan)

scholarly journals Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming

2020 ◽  
Vol 15 (2) ◽  
pp. 47
Author(s):  
Arturo Rodríguez ◽  
Joaquín Trigueros
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Forecasting Model ◽  
Time Series Models ◽  
Arima Models ◽  
Quarterly Earnings ◽  
Earnings Per Share ◽  
Forecast Combinations

In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.

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