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Autoregressive–moving-average model
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51	Beyond ARMA Models: Nonstationarity & Seasonality • now know how to handle time series in a certain ‘comfort zone’ − no worrisome trends or seasonal patterns − sample ACF and PACF decay fairly rapidly − simpl Add to Reading List Source URL: faculty.washington.edu Language: English - Date: 2015-02-27 11:14:32 XT Partial autocorrelation function Moving-average model Statistics Autoregressive integrated moving average Noise
52	20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013 www.mssanz.org.au/modsim2013 Time Series Properties of Liquidation Discount F. Chan a , J. Gould a , R. Singh a and J.W. Y Add to Reading List Source URL: www.mssanz.org.au Language: English - Date: 2013-11-19 22:06:19 Mathematical finance Econometrics Time series analysis Financial economics Autoregressive conditional heteroskedasticity Autoregressive fractionally integrated moving average Volatility Economic model Time series Statistics Economics Finance
53	20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013 www.mssanz.org.au/modsim2013 Diagnostic checking for Non-stationary ARMA Models: An Application to Financial Data S.-Q. Li Add to Reading List Source URL: www.mssanz.org.au Language: English - Date: 2013-11-19 22:07:14 Parametric statistics Autoregressive–moving-average model Noise Autoregressive conditional heteroskedasticity T-statistic Statistics Time series analysis Econometrics
54	Statistics 519, Winter Quarter 2015 Problem Set 6 Problem[removed]points). Let {Xt } be an invertible MA(1) process; i.e., we can write Xt = Zt + θZt−1 , where {Zt } ∼ WN(0, σ 2 ), and \|θ\| < 1. Use the Levinson–Du Add to Reading List Source URL: faculty.washington.edu Language: English - Date: 2015-02-18 11:21:01 Covariance and correlation Signal processing Moving-average model Partial autocorrelation function Estimator Normal distribution Autoregressive model Innovation Statistics Noise Estimation theory
55	Testing Causality Between Two Vectors in Multivariate GARCH Models Add to Reading List Source URL: fbe.unimelb.edu.au Language: English - Date: 2013-08-05 02:22:00 Estimation theory Autoregressive conditional heteroskedasticity Regression analysis Causality Vector autoregression Economic model Autoregressive–moving-average model Maximum likelihood Time series Statistics Time series analysis Econometrics
56	Kaipio, Jari. Simulation and Estimation of Nonstationary EEG. Kuopio University Publications C. Natural and Environmental Sciences[removed]p. ISBN[removed]ISSN[removed]ABSTRACT Add to Reading List Source URL: dsp-book.narod.ru Language: English - Date: 2013-05-06 00:56:54 Signal processing Econometrics Estimation theory Spectral density estimation Autoregressive–moving-average model Electroencephalography Moving-average model Autoregressive conditional heteroskedasticity Autoregressive model Statistics Noise Time series analysis
57	Microsoft PowerPoint - Slides_on_ARIMA_models--Robert_Nau.pptx Add to Reading List Source URL: people.duke.edu Language: English - Date: 2014-11-29 15:39:14 Box–Jenkins Autoregressive integrated moving average Moving-average model Autocorrelation Seasonality Forecasting Time series Autoregressive model Regression analysis Statistics Time series analysis Partial autocorrelation function
58	Bangko Sentral ng Pilipinas BSP Working Paper Series Forecasting the Volatility of Philippine Inflation using GARCH Models Add to Reading List Source URL: www.bsp.gov.ph Language: English - Date: 2013-11-05 13:22:15 Econometrics Noise Autoregressive conditional heteroskedasticity Technical analysis Mathematical finance Time series Volatility Forecasting Autoregressive–moving-average model Statistics Time series analysis Economics
59	Microsoft Word - BONDON Pascal + PASCAL frederic (24 Mar[removed]docx Add to Reading List Source URL: lx2.saas.hku.hk Language: English - Date: 2015-03-20 07:17:20 Summary statistics Data analysis Signal processing Autoregressive–moving-average model Covariance matrix Covariance Sample mean and sample covariance Estimation theory Central limit theorem Statistics Covariance and correlation Matrices
60	ITSM-R Reference Manual George Weigt [removed] November 11, 2013 Contents Add to Reading List Source URL: eigenmath.sourceforge.net Language: English - Date: 2014-12-07 12:48:19 Covariance and correlation Error Measurement Partial autocorrelation function Autoregressive integrated moving average Errors and residuals in statistics Time series Variance Autoregressive–moving-average model Statistics Regression analysis Time series analysis