Asymmetric Error Correction Model Sas
Contents |
Exogenous VariablesParameter Estimation and Testing on RestrictionsCausality Testing Syntax Functional SummaryPROC VARMAX StatementBOUND StatementBY StatementCAUSAL StatementCOINTEG StatementGARCH StatementID StatementINITIAL StatementMODEL StatementNLOPTIONS StatementOUTPUT StatementRESTRICT StatementTEST Statement Details Missing ValuesVARMAX ModelDynamic
Engle Granger Cointegration Test Sas
Simultaneous Equations ModelingImpulse Response FunctionForecastingTentative Order SelectionVAR and VARX ModelingBayesian VAR proc varmax and VARX ModelingVARMA and VARMAX ModelingModel Diagnostic ChecksCointegrationVector Error Correction ModelingI(2) ModelMultivariate GARCH ModelingOutput Data SetsOUT= Data
Johansen Cointegration Test
SetOUTEST= Data SetOUTHT= Data SetOUTSTAT= Data SetPrinted OutputODS Table NamesODS GraphicsComputational Issues Examples Analysis of U.S. Economic VariablesAnalysis of German Economic VariablesNumerous ExamplesIllustration of ODS Graphics References vector error correction model Vector Error Correction Model Subsections: Example of Vector Error Correction Model Cointegration Testing A vector error correction model (VECM) can lead to a better understanding of the nature of any nonstationarity among the different component series and can also improve longer term forecasting over an unconstrained model. The VECM(p) form with the cointegration rank is written as where is the differencing operator, such that ; , where and are matrices; is a matrix. It has an equivalent VAR(p) representation as described in the preceding section. where is a identity matrix. Example of Vector Error Correction Model An example of the second-order nonstationary vector autoregressive model is with This process can be given the following VECM(2) representation with the cointegration rank one: The following PROC IML statements generate simulated data for the VECM(2) form specified above and plot the data as shown in Figure 35.12: proc iml; sig = 100*i(2); phi = {-0.2 0.1, 0.5 0.2, 0.8 0.7, -0.4 0.6}; call varmasim(y,phi) sigma=sig n=100 initial=0 seed=45876; cn = {'y1' 'y2'}; create simul2 from y[colname=cn]; append from y; quit; data simul2; set simul2; date = intnx( 'year', '01jan1900'd, _n_-1 ); format date year4. ; run; proc timeseries data=simul2 vectorplot=series; id date interval=year; var y1 y2; run; Figure 35.12: Plot of Generated Data Process Cointegration Testing The following statements use the Johansen cointegr
SAS® for Cointegration and Error Correction Mechanism Approaches: Estimating a Capital Asset Pricing Model (CAPM) for House Price Index ReturnsArticle with 128 Reads1st Ismail Mohamed2nd Theresa R DiventiAbstractMany researchers erroneously use the framework of linear regression models to analyze time series data when predicting changes over time or when extrapolating from present conditions to future conditions. Caution is needed when interpreting the results of these regression models. Granger and Newbold (1974) discovered the existence of 'spurious regressions' that can occur when the variables in a regression are nonstationary. While these regressions appear to look good in http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/etsug_varmax_gettingstarted04.htm terms of having a high R 2 and significant t-statistics, the results are meaningless. Both analysis and modeling of time series data require knowledge about the mathematical model of the process. This paper introduces a methodology that utilizes the power of the SAS DATA STEP, and PROC X12 and REG procedures. The DATA STEP uses the SAS LAG and DIF functions to manipulate the https://www.researchgate.net/publication/228462207_Using_SASR_for_Cointegration_and_Error_Correction_Mechanism_Approaches_Estimating_a_Capital_Asset_Pricing_Model_CAPM_for_House_Price_Index_Returns data and create an additional set of variables including Home Price Index Returns (HPI_R1), first differenced, and lagged first differenced. PROC X12 seasonally adjusts the time series. Resulting variables are manipulated further (1) to create additional variables that are tested for stationarity, (2) to develop a cointegration model, and (3) to develop an error correction mechanism modeled to determine the short-run deviations from long-run equilibrium. The relevancy of each variable created in the data step to time series analysis is discussed. Of particular interest is the coefficient of the error correction term that can be modeled in an error correction mechanism to determine the speed at which the series returns to equilibrium. The main finding is that Metropolitan Statistical Areas (MSAs) with very slow short-run acceleration paths to the equilibrium have higher returns and risk associated with house price returns than MSAs with very rapid speed-of-adjustment coefficients. I INTRODUCTION The purpose of this paper is to develop an approach specifically for one-equation models that can be used in place of the more complex standard SAS program routines PROC ARIMA. The ARIMA methodology developed by Box and Jenkins (1976) has gained enormou
Erasmus code: 11.2 ISCED code: 0542 ECTS credits: 4 http://informatorects.uw.edu.pl/en/courses/view?prz_kod=1100-4_SASASC Language: Polish Organized by: Faculty of Physics Time Series Analysis with SAS 1100-4_SASASC 1. Decomposition of time series and simple extrapolative models - classical decomposition methods in additive and multiplicative form- X12 procedure- moving average and exponential smoothing,- seasonal smoothing- Holt and error correction Holt-Winters models,- forecasting in extrapolative modelsLiterature: Evans (2003) 2. Univariate time series – modeling and forecasting- stochastic process, deterministic process and time series – definitions,- weak and strong stationarity of time series,- random walk (with/without drift), white noise,- stationarity testing, unit root tests: error correction model DF/ADF, KPSS- autocorrelation and partial autocorrelation functions, correlograms, - autoregressive process AR(p) and its features,- moving average process MA(q) and its characteristics,- ARMA(p,q) models, stationarity conditions, Box-Jenkins procedure, information criteria AIC, SBC (BIC), parameter estimation and model diagnostics,- Portmanteau test, Box-Pierce and Ljung-Box tests,- integrated series, integration level, differentiation of series,- ARIMA models for integrated series,- forecasting in ARMA/ARIMA models, ex-ante forecast error, confidence intervals for the forecast, ex-post measures of forecast quality (absolute and percentage)- seasonal SARIMA models – estimation and forecasting,Literature: Brooks (2008), Charemza, Deadman (1997), Enders (2004) 3. Modeling volatility - stylised facts in financial time series, leptokurtic series, “fat tails”, leverage effect,- homoskedasticity vs. heteroskedasticity,- conditional vs. unconditional variance,- ARCH(q) process and its features, testing for conditional heteroskedasticity,- estimation of ARCH models,- g