how do you determine the order of Lags in ARMA model?
Use any sophisticated tool like R or python to execute these steps
1. Which test is used for the following a. omission of relevant variables b. parameter stability 2. when can we introduce dummies in models? 3. What is stationarity and Why is it desired to run a model? 4. how do you determine the order of Lags in ARMA model? 5. List and explain the Box - Jenkins model adequacy test for the ARMA/ARIMA
4. Consider the ARMA (2, 3) process, I( 0.1%-1 +0.12%-2 + Ze + 0.3Zn-1-0.045-2-0.012Zt-3, where fZ) is a white noise process with unit variance. It is known that the above process is overestimated [4 marks (b) Hence, determine the stationarity and invertibility of the process. [4 marks (c) Find the first three lags of the autocorrelation function (ACF) for the process. [12 marks) (5 marks] (a) Suggest a parsimonious model for the above process. (d) Find the first three lags...
the stationarity condition for a mixed seasonal ARMA model
List and explain the Box- Jenkins model adequacy test for ARMA/ARIMA.
2. Consider an ARMA(1,1) process, X4 = 0.5X:-1 +0+ - 0.25a4-1, where az is white noise with zero mean and unit variance. (a) Is the model stationary? Explain your answer briefly. (b) Is the model invertible? Explain your answer briefly. (c) Find the infinite moving-average representation of Xt. Namely, find b; such that X =< 0;&–; j=0 (d) Evaluate the first three lags of the ACF and PACF.
Place the fiscal policy timing lags in order from earliest to latest. Not all lags will be used. Earliest lag Latest lag Answer Bank decision lagimplementation lag information lag fiscal lagrecognition lag presidential lag Keynesian lag
If you model a time series Yt using a stationary ARMA process with a nonzero constant (µ unequal to 0) and use it to forecast future values of Yt, then as you forecast further and further into the future, the confidence interval widths for your forecasts will (a) continue to increase and eventually reach arbitrarily large values. (b) gradually decay to zero. (c) cutoff to zero after some lag. (d) converge to a non-zero limiting value.
Consider ARMA(2.1) model X4 - X:-1 +62X-2 = 2+ 2-1. When the process is stationary and causal?
where at is a white noise process with unit variance. It is known that the above process is overestimated.(a) Suggest a parismony model ARMA(2,1) for the above process.(b) Hence, determine the stationarity and invertibility of the process.(c) Find the mean, the variance and the first two lags of the autocovariance function of theprocess.(d) Find the first three lags of the autocorrelation function (ACF) for the process.(e) Find the first three lags of the partial autocorrelation coefficients.
Problem 5 Find the characteristic equation and compute its roots for the following ARMA model S(k) = 0.15(k – 1) + 0.02S(k – 2) + e(k) + 2e(k − 2)