10/4/2023 0 Comments Polyroot# analysis using AR(1) model for means and ARCH(1) model for variancesĬan <- th + th * (y - th)Ĭondit.var <- th * ( 1 + th * (y - th) ^ 2) UBS <- ts(UBSCreditSuisse $UBS_LAST, start = c( 2000, 1), frequency = 365.25) Perform a likelihood ratio test to test whether the AR(2) coefficient is significative.Fit an AR(2) model using a conditional likelihood for the mean and obtain the standard errors of your estimated coefficients.Comment on the fit using standard diagnostic plots (Q-Q plot, ((P)ACF, cumulative periodogram). Obtain the maximum likelihood estimates using nlm or optim as well as the standard errors.Create a function that fits an AR(1)-ARCH(1) model by modifying the code provided above and apply it to y.6.4.1 Exercise 1: Jussy air temperature.6.3 Diagnostics for missing values and smoothing.6.1.1 Exercise 1: Jussy air temperature.6 Notes on irregular time series and missing values".5.2.1 Exercise 1: Dynamic linear model for the Nile river dataset.5.2 State-space models and the Kalman filter.5.1 Simulation-based prediction intervals for ARIMA-GARCH models.4.3.2 Exercise 1: Southern oscillation index (SOI) and fish recruitement.4.3.1 Smoothing and seasonally adjusted values.4.2 Summary of nonparametric spectral estimation.3.5.3 Exercice 3: International Business Machines (IBM) stock. ![]()
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