
	Where results make sense

Topic: Autoregressive moving average model

Related Topics

Autoregressive conditional heteroskedasticity

Time series

Regression analysis

Financial mathematics

Heteroskedasticity

Variance

Value at risk

Technical analysis

Least squares

Econometrics

Arch

Call option

Short selling

Fundamental analysis

Put option

	Autoregressive moving average model - Wikipedia, the free encyclopedia
		are the parameters of the model and the ε
		The moving average model is essentially a finite impulse response filter with some additional interpretation placed on it.
		ARMA models in general can, after choosing p and q, be fitted by least squares regression to find the values of the parameters which minimise the error term.
	en.wikipedia.org /wiki/Autoregressive_moving_average_model (815 words)

	Autoregressive conditional heteroskedasticity - Wikipedia, the free encyclopedia
		In econometrics, an autoregressive conditional heteroskedasticity (ARCH, Engle (1982)) model considers the variance of the current error term to be a function of the variances of the previous time period's error terms.
		If an autoregressive moving average model(ARMA model) is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH, Bollerslev(1986)) model.
		IGARCH or Integrated Generalized Autoregressive Conditional Heteroskedasticity is a restricted version of the GARCH model, where the sum of the persistent parameters sum up to one.
	en.wikipedia.org /wiki/Autoregressive_conditional_heteroskedasticity (274 words)

	Autoregressive Moving Average Error Processes
		Autoregressive moving average error processes (ARMA errors) and other models involving lags of error terms can be estimated using FIT statements and simulated or forecast using SOLVE statements.
		The conditional least-squares method of estimating moving average error terms is not optimal because it ignores the startup problem.
		This model states that the errors for Y1 depend on the errors of both Y1 and Y2 (but not Y3) at both lags 1 and 2, and that the errors for Y2 and Y3 depend on the previous errors for all three variables, but only at lag 1.
	support.sas.com /91doc/getDoc/etsug.hlp/model_sect53.htm (3399 words)

	ARMA
		Autoregressive models are simply a linear regression of the current value of the series against one or more prior values of the series.
		The primary idea behind the moving average model is that the random shocks are propogated to future values of the series.
		Model validation is similar to the nonlinear fitting case (i.e., various residual plots).
	www.itl.nist.gov /div898/software/dataplot/refman1/auxillar/arma.htm (867 words)

	Forecast Pro \| Resources
		Since models with many parameters often fit the historical data well, but forecast poorly, the BIC balances a reward for goodness-of-fit with a penalty for model complexity.
		A forecasting model is an equation, or set of equations, that the forecaster uses to represent and extrapolate features in the data.
		Model complexity is measured by the number of parameters that must be fitted to the historic data.
	www.forecastpro.com /resources/glossary (948 words)

	NPG - Abstract (Site not responding. Last check: 2007-11-05)
		It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account.
		The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model.
		Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
	www.copernicus.org /EGU/npg/12/1/55.htm?FrameEngine=false (304 words)

	Autoregressive Models (Site not responding. Last check: 2007-11-05)
		The autoregressive model is one of a group of linear prediction formulas that attempt to predict an output y[n] of a system based on the previous outputs (y[n-1],y[n-2]...) and inputs (x[n], x[n-1], x[n-2]...).
		A model which depends only on the previous outputs of the system is called an autoregressive model (AR), while a model which depends only on the inputs to the system is called a moving average model (MA), and of course a model based on both inputs and outputs is an autoregressive-moving-average model (ARMA).
		Several methods and algorithms exist for calculating the coefficients of the AR model, all of which are implemented by the matlab command 'ar'.
	www.owlnet.rice.edu /~elec431/projects96/digitalbb/autoregression.html (346 words)

	VB: Stochastic Model of OASDI program
		The simplest of all time-series models is the random walk.
		The naming of this model is apt because the coefficients of the time-series equation are obtained by regressing the equation on itself, more precisely, with its own p lagged values.
		The equations used to model the assumptions are autoregressive moving average models with the additional requirement that the mean of the variable Y
	www.ssa.gov /OACT/NOTES/as117/LR_Stochastic_VB.html (807 words)

	Article: ARIMA Model
		ARIMA (AutoRegressive Integrated Moving Average) model, introduced by Box and Jenkins in 1976, includes three types of parameters: the autoregressive parameters (p), the number of differencing passes (d), and moving average parameters (q).
		For example, a model described as (0, 1, 2) means that it contains 0 (zero) autoregressive (p) parameters and 2 moving average (q) parameters which were computed for the series after it was differenced once.
		For example, the model (0,1,2)(0,1,1) describes a model that includes no autoregressive parameters, 2 regular moving average parameters and 1 seasonal moving average parameter, and these parameters were computed for the series after it was differenced once with lag 1, and once seasonally differenced.
	www.public.iastate.edu /~qmeng/basics/arima.html (700 words)

	S-WoPEc: Modelling High Frequency Financial Count Data
		The papers advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data.
		Paper [1] advances the INMA model to model the number of transactions in stocks in intra-day data.
		Using the BINMA model for AstraZeneca and Ericsson B it is found that there is positive correlation between the stock transactions series.
	swopec.hhs.se /umnees/abs/umnees0656.htm (350 words)

	6.4.4.6. Box-Jenkins Model Identification
		The first step in developing a Box-Jenkins model is to determine if the series is stationary and if there is any significant seasonality that needs to be modeled.
		At the model identification stage, our goal is to detect seasonality, if it exists, and to identify the order for the seasonal autoregressive and seasonal moving average terms.
		Moving average model, order identified by where plot becomes zero.
	www.itl.nist.gov /div898/handbook/pmc/section4/pmc446.htm (714 words)

	Limiting distributions of maximum likelihood estimators for unstable autoregressive moving-average time series with ...
		BAI, J. On the partial sums of residuals in autoregressive and moving average models.
		TSAY, R. and TIAO, G. Consistent estimates of autoregressive parameters and the extended sample autocorrelation function of nonstationary and stationary ARMA models.
		Results on estimation and testing for unit roots in the nonstationary autoregressive moving average model.
	projecteuclid.org /Dienst/UI/1.0/Summarize/euclid.aos/1030563979 (621 words)

	What is an autoregressive moving average model (ARMA)?
		where the Î¸1,..., Î¸q are the parameters of the model and the Îµt, Îµt-1,...
		Taking the AR model and the MA model, we get the ARMA model.
		AnswerThe dependence of Xt on past values and the error terms Îµt is assumed to be linear unless specified otherwise.
	cnx.org /content/m13395/latest (561 words)

	Time series -
		Time series prediction is the use of a model to predict future events based on known past events: to predict future data points before they are measured.
		Two broad classes of practical importance are the moving average (MA) models, and the autoregressive (AR) models.
		These two classes depend linearly on previous data points and are treated in more detail in the article on autoregressive moving average models (ARMA).
	psychcentral.com /psypsych/Time_series (388 words)

	Robust Bayesian estimation of autoregressive-moving average models - Barnett, Kohn, Sheather (ResearchIndex) (Site not responding. Last check: 2007-11-05)
		It enforces stationarity on the autoregressive parameters and invertibility on the moving average parameters, and takes account of uncertainty about the correct model by averaging the parameter estimates and forecasts of future observations over the set of permissible models.
		16 Bayes inference in regression models with ARMA (context) - Chib, Greenberg - 1994
		2 the reparameterization of autoregressive models by partial a..
	citeseer.ist.psu.edu /154827.html (530 words)

	Nuffield College Economics Working Papers Ref: 2001-W11 (Site not responding. Last check: 2007-11-05)
		A dynamic model for limited dependent variables is proposed, which estimation does not rely on simulation methods.
		It can be shown that the latent process implied by the limited dependent autoregressive moving average model is covariance stationary.
		Parameter estimates of this model are shown to be consistent but inefficient estimates of the parameters of a standard latent autoregressive moving average model, for which a maximum likelihood estimator is computationally burdensome.
	www.economics.ox.ac.uk /Research/WP/PaperDetails.asp?PaperID=176 (196 words)

	NAG C Library, Mark 7 : g05pcc
		The parameters in the model are thus the
		The model (1) must be both stationary and invertible.
		Shea B L (1988) A note on the generation of independent realisations of a vector autoregressive moving average process J.
	www.nag.co.uk /numeric/cl/manual/xhtml/G05/g05pcc.xml (1260 words)

	Estimating Terminal Lake Level Frequencies
		An autoregressive moving average (ARMA) model with a deterministic trend component for the annual storage changes is used as the basis.
		Using the validated ARMA model and simulation techniques, many sequences of annual incremental storage time series are generated.
		Both the average level and within-year maximum fluctuation distributions represent the stochastic behavior of the lake and thus are found to be necessary to the development of the level-frequency relationship.
	www.pubs.asce.org /WWWdisplay.cgi?8901386 (228 words)

	Nonlinear Modeling, System Identification and the NARMAX method (Site not responding. Last check: 2007-11-05)
		The NARMAX [Nonlinear AutoRegressive Moving Average model with eXogeneous inputs] model was developed by Auxetics personnel.
		Narmax allows models to be constructed, from data sets, in a manner that exposes the relationships between the system variables in an explicit manner.
		In either case the contribution that each model term makes to the system output is expressed as a percentage, this allows the model to be built by adding in the most significant term first, then the next most significant etc until the required prediction accuracy is obtained.
	www.auxetics.org.uk /nonlinearnarmax.html (186 words)

	Cusum Charts For Monitoring An Autocorrelated Process
		CUSUM charts based on plotting the residuals from model forecasts, or on plotting the original observations, are considered.
		The performance of CUSUM charts is studied for this model and compared to the performance of Shewhart and EWMA charts.
		We believe that this process model is general enough to fit a wide variety of processes encountered in applications, yet it is simple enough to be easy to explain and to fit to process data.
	www.asq.org /pub/jqt/past/vol33_issue3/qtec_33_3_316.htm (1024 words)

	Overview (Site not responding. Last check: 2007-11-05)
		An ARIMA model predicts a value in a response time series as a linear combination of its own past values, past errors (also called shocks or innovations), and current and past values of other time series.
		When an ARIMA model includes other time series as input variables, the model is sometimes referred to as an ARIMAX model.
		The ARIMA class of time series models is complex and powerful, and some degree of expertise is needed to use them correctly.
	www.okstate.edu /sas/v8/sashtml/ets/chap7/sect1.htm (352 words)

	Notation for ARIMA Models
		A dependent time series that is modeled as a linear combination of its own past values and past values of an error series is known as a (pure) ARIMA model.
		The numerator factors for a transfer function for a predictor series are like the MA part of the ARMA model for the noise series.
		The denominator factors for a transfer function for a predictor series are like the AR part of the ARMA model for the noise series.
	www.asu.edu /it/fyi/dst/helpdocs/statistics/sas/sasdoc/sashtml/ets/chap30/sect13.htm (555 words)

	Information about Moving (Site not responding. Last check: 2007-11-05)
		With little fanfare, waves of empty nesters are moving to the Carolinas to be near their grandchildren -- giving up good jobs, decades-old friendships and beautiful homes without regrets.
		About 43 million people in the United States move each year, according to the Census Bureau, and summertime is the busiest season.
		Paul Barnes is a Moving enthusiast and freelance author.
	moving-agents.info (399 words)

	Identification of periodic autoregressive moving average models and their application to the modeling of river flows
		Identification of periodic autoregressive moving average models and their application to the modeling of river flows
		This article develops model identification and simulation techniques based on a periodic autoregressive moving average (PARMA) model to capture the seasonal variations in river flow statistics.
		A careful statistical analysis of the PARMA model residuals, including a truncated Pareto model for the extreme tails, produces a realistic simulation of these river flows.
	www.agu.org /pubs/crossref/2006/2004WR003772.shtml (270 words)

	SSRN-Testing the Fit of a Vector Autoregressive Moving Average Model by Efstathios Paparoditis
		The method evaluates in a certain way the closeness of the sample spectral density matrix of the observed process to the spectral density matrix of the parametric model postulated under the null and uses for this purpose nonparametric estimation techniques.
		The asymptotic distribution of the test statistic is established and an alternative, bootstrap-based method is developed in order to estimate more accurately this distribution under the null hypothesis.
		Goodness-of-fit diagnostics useful in understanding the test results and identifying sources of model inadequacy are introduced.
	papers.ssrn.com /sol3/papers.cfm?abstract_id=736421 (258 words)

	Journal of Agriculture, Science and Technology - Vol. 3, No. 1 (2001) (Site not responding. Last check: 2007-11-05)
		The gamma and exponentially distributed processes which are used as basic models for positive time series fall in the class of non-gaussian processes.
		The gamma autoregressive moving average (GARMA(p,q)) model of order p and q and the exponential autoregressive moving average (EARMA(p,q)) model of order p and q are consequently developed.
		The distributions of developed models, unlike those studied by Lawrance and Lewis (1980), can be determined given either the distribution of the innovation sequence {et} or that of the process itself.
	www.ajol.info /viewarticle.php?id=20921 (233 words)

	Autoregressive Moving Average Models (Site not responding. Last check: 2007-11-05)
		, respectively, for the obvious reason that (2.1) resembles a regression model and (2.2) a moving average.
		In general, a constant term can occur on the right-hand side of (2.3) signalling a nonzero mean process.
		Since an ARMA model is defined by its AR and MA coefficients and the white noise variance (the noise is assumed to be normally distributed), the object
	documents.wolfram.com /applications/timeseries/UsersGuidetoTimeSeries/1.2.1.html (302 words)

	April (Site not responding. Last check: 2007-11-05)
		Autoregressive moving average model : Autoregressive moving average model,Statistics,Autoregressive conditional heteroskedasticity,Deret waktu,Statistik,Autoregressive integrated moving average,George E.P. Box,Infinite impulse response,Finite impulse response
		Bayesian inference : Bayesian inference,Statistics,Inferensi Bayes,Bayes' theorem,Bayesian model comparison,Bayesian probability,Evidence,False positive,Likelihood,Likelihood principle,Bayesian inference nyaeta statistica
		Binary classification : Binary classification,Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not.
	www.toshare.info /w/su/01.htm (919 words)

Try your search on: Qwika (all wikis)