Where results make sense
 About us   |   Why use us?   |   Reviews   |   PR   |   Contact us

# Topic: Autoregressive

###### In the News (Tue 25 Jun 19)

 [No title] The methods under consideration include the blocks-of-blocks bootstrap, the stationary bootstrap, two versions of the autoregressive (AR) sieve bootstrap (the encompassing model sieve and the AIC model sieve bootstrap), the Cholesky factor bootstrap, and three versions of a hybrid method called the pre-whitened block bootstrap. Statistics of Interest Having generated a sequence of pseudo data, we fit an autoregressive sieve approximation to the bootstrap data, bias-correct the autoregressive coefficient estimates and construct the implied impulse response estimates. On the Importance of the Autoregressive Lag Order for the Sieve Bootstrap Our results suggest that the accuracy of bootstrap inference is more susceptible to the shape of the underlying impulse response function than to the dominant root of the population process. www.uh.edu /~jberkowi/bbk.doc   (8947 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 autoregressive process can be applied to the structural equation errors or to the endogenous series themselves. If there are no exogenous components to the vector autoregression model, including no intercepts, then assign zero to each of the variables. www.uc.edu /sashtml/ets/chap14/sect49.htm   (3387 words)

 Preliminary Autoregressive Models These preliminary autoregressive models are used to estimate the autoregressive order of the process and limit the order of the autocovariances considered in the state vector selection process. Unlike a univariate autoregressive model, a multivariate autoregressive model has different forms, depending on whether the present observation is being predicted from the past observations or from the future observations. Although the autoregressive models can be used for prediction, their primary value is to aid in the selection of a suitable portion of the sample covariance matrix for use in computing canonical correlations. www.asu.edu /it/fyi/dst/helpdocs/statistics/sas/sasdoc/sashtml/ets/chap18/sect17.htm   (717 words)

 Parks Method (Autoregressive Model) The model assumed is first-order autoregressive with contemporaneous correlation between cross sections. A consistent estimator of the first-order autoregressive parameter is then obtained in the usual manner, as follows: Finally, the autoregressive characteristic of the data can be removed (asymptotically) by the usual transformation of taking weighted differences. www.uni.edu /sasdoc/ets/chap20/sect21.htm   (240 words)

 Nonlinear Autoregressive Models and Fingerprints In a full nonlinear autoregressive model, quadratic (or higher-order) terms are added to the linear autoregressive model. Control AC-19,716-723] showed that, for a linear autoregressive model, a significant improvement in the fit is associated with a reduction in the residual variance of at least 2V/N, where V is the variance without the candidate additional term, and N is the number of data points. We showed that the same criterion, a reduction in residual variance by at least 2V/N, is a criterion for the significance of a single nonlinear term as well. www-users.med.cornell.edu /~jdvicto/nlardf.html   (541 words)

 Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime, Randal Douc, ... An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations. projecteuclid.org /Dienst/UI/1.0/Summarize/euclid.aos/1098883789   (996 words)

 SSRN-Small Sample Properties of Forecasts from Autoregressive Models Under Structural Breaks by M. Hashem Pesaran, ... Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural breaks. This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under a structural break. papers.ssrn.com /sol3/papers.cfm?abstract_id=429984   (383 words)

 [No title]   (Site not responding. Last check: 2007-11-01) This chapter is organized into sections that discuss and illustrate each of these special cases of the spatial autoregressive model as well as the most general model form in (2.1). It should be noted that Pace and Barry (1997) when confronted with the task of providing measures of dispersion for spatial autoregressive estimates based on sparse algorithms suggest using likelihood ratio tests to determine the significance of the parameters. The variable we wish to explain using the first-order spatial autoregressive model is the proportion of total possible votes cast for both candidates. www.rri.wvu.edu /WebBook/LeSage/spatial/spatial.html   (12086 words)

 Autoregressive Error Model Note that in this parameterization, the signs of the autoregressive parameters are reversed from the parameterization documented in most of the literature. At the same values of the autoregressive parameters, the ULS and ML standard errors will always be larger than those computed from Yule-Walker. An autoregressive error model should not be used as a nostrum for models that simply do not fit. www.asu.edu /sas/sasdoc/sashtml/ets/chap8/sect20.htm   (1426 words)

 ARS | Publication request: Evaluation of An Autoregressive Repeatability Animal Model for Analysis of First Lactation ... Technical Abstract: Three models were compared: Model 1 had a first order autoregressive covariance structure among test day environmental components, Model 2 was a repeatability animal model using test day records, and Model 3 was an animal model using 305-day records. Data on 12,071 first lactations of Holstein cows with a total of 106,472 TD records were available on milk, fat, and protein yields and somatic cell scores (SCS). The autoregression covariance structure helped prevent overestimation of heritability and repeatability for all four traits. www.ars.usda.gov /research/publications/publications.htm?SEQ_NO_115=156080   (428 words)

 Systat Software Inc. - AutoSignal - HTML Help If a model can be successfully fitted to a data stream, it can be transformed into the frequency domain instead of the data upon which it is based, producing a continuous and smooth spectrum. This is the basic premise of the spectra produced using autoregressive (AR) modeling. In an AR model, a value at time t is based upon a linear combination of prior values (forward prediction), upon a combination of subsequent values (backward prediction), or both (forward-backward prediction). www.systat.com /products/AutoSignal/help/?sec=1073   (1662 words)

 Oxford Scholarship Online: Likelihood-Based Inference in Cointegrated Vector Autoregressive Models It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model. The main result on the structure of cointegrated processes as defined by the error correction model is Grangers representation theorem. The statistical results include derivation of the trace test for cointegrating rank, test on cointegrating relations, and test on adjustment coefficients and their asymptotic distributions. www.oxfordscholarship.com /oso/public/content/economicsfinance/0198774508/toc.html   (246 words)

 Recursive estimation of a drifted autoregressive parameter, Eduard Belitser Recursive estimation of a drifted autoregressive parameter, Eduard Belitser Suppose the \$X_0,\dots, X_n\$ are observations of a one-dimensional stochastic dynamic process described by autoregression equations when the autoregressive parameter is drifted with time, i.e. Verulava, Y. Convergence of the stochastic approximation algorithm for estimating the autoregression parameter. projecteuclid.org /getRecord?id=euclid.aos/1015952001   (220 words)

 Jeffrey R. Russell Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Conditional Duration Model; Robert F. Engle and Jeffrey R. Russell; Department of Economics, UC San Diego, 1995. Autoregressive Conditional Duration: A New Model for Irregularly Spaced Time Series Data; Robert F. Engle and Jeffrey R. Russell; Department of Economics, UC San Diego, 1995. Forecasting Transaction Rates: The Autoregressive Conditional Duration Model; Robert F. Engle and Jeffrey R. Russell; NBER Working Papers no.4966; Cambridge: National Bureau of Economic Research, 1994. www.lib.uchicago.edu /e/busecon/busfac/Russell.html   (401 words)

 Glossary of research economics The ARCH econometric model for this (introduced by Engle (1982)) is that the variance of the series itself is an AR (autoregressive) time series, often a linear one. An ARIMA process is made up of sums of autoregressive and moving-average components, and may not be stationary. An ARMA process is a stationary one made up of sums of autoregressive and moving-average components. www.econterms.com /econtent.html   (14590 words)

 A Pattern Test for Distinguishing Between Autoregressive and Mean-Shift Data Statistical methods such as control charts and change-point analysis assume a series of independent observations collected over time.  At one or more points in time the mean may shift.  Let X The data shown in Figures 2 and 3 were generated using the first order autoregressive model: Values of f=0.7 and f=-0.7 were used respectively in Figures 2 and 3.  When f=0, the autoregressive model reduces to what is called the white noise model where X www.variation.com /cpa/tech/pattern.html   (304 words)

 Autoregressive Models   (Site not responding. Last check: 2007-11-01) W.D. Penny and S.J. Roberts (2000) Bayesian Multivariate Autoregressive Models with Structured Priors Technical Report PARG-00-11, Department of Engineering Science, Oxford University. W.D. Penny and S.J. Roberts (2000) Bayesian Methods for Autoregressive Models IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, December 2000. W.D. Penny and S.J. Roberts (2000) Variational Bayes for Non-Gaussian Autoregressive Models IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, December 2000. www.fil.ion.ucl.ac.uk /~wpenny/publications/ar.html   (111 words)

 Genetic Evaluation of Dairy Cattle with Test-Day Models with Autoregressive Covariance Structures and with a 305-d ... Genetic Evaluation of Dairy Cattle with Test-Day Models with Autoregressive Covariance Structures and with a 305-d Model -- Sawalha et al. Genetic Evaluation of Dairy Cattle with Test-Day Models with Autoregressive Covariance Structures and with a 305-d Model Abbreviation key: AR(1) = first-order autoregressive, CS = compound symmetry, PBV = predicted breeding values, TD = test day. jds.fass.org /cgi/content/abstract/88/9/3346   (366 words)

 EconPapers: A Family of Autoregressive Conditional Duration Models Working Paper: A family of autoregressive conditional duration models (2003) Journal Article: A family of autoregressive conditional duration models (2006) This item may be available elsewhere in EconPapers: Search for items with the same title. econpapers.repec.org /paper/fgvepgewp/440.htm   (142 words)

 St. Louis Fed: Michael J. Dueker - Do Real Exchange Rates Have Autoregressive Unit Roots Under theAlternative of Long ... Do Real Exchange Rates Have Autoregressive Unit Roots Under theAlternative of Long Memory and Breaking Trends? In this paper, we estimate (by maximum likelihood) the parameters of univariate fractionallyintegrated real exchange rate time series models, and test for autoregressive unit roots under thealternative of a covariance stationary long-memory process. We use quarterly dollar-based realexchange rates (since 1957) for seventeen OECD countries, and show that the finding of unitautoregressive roots does not go away even with this more sophisticated alternative. research.stlouisfed.org /econ/dueker/mjdwp4.html   (109 words)

 EconPapers: A family of autoregressive conditional duration models EconPapers: A family of autoregressive conditional duration models New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin Working Paper: A Family of Autoregressive Conditional Duration Models (2002) econpapers.repec.org /paper/fgvepgewp/501.htm   (154 words)

 Exact Geometry and Autoregressive Models This paper derives exact expressions for the statistical curvature and related geometric quantities in the first order autoregressive models. To our knowledge, this item is not available for download. Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using ideas.repec.org /p/fth/louvco/9648.html   (206 words)

Try your search on: Qwika (all wikis)

About us   |   Why use us?   |   Reviews   |   Press   |   Contact us