You obtain the following estimates for an AR(2) model of some returns data yt = 0.803yt−1...
You obtain the following estimates for an AR(2) model of some returns data
yt = 0.803yt−1 + 0.682yt−2 + ut
Where ut is a white noise error process. By examining the characteristic equation, check the estimated model for stationarity.
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You obtain the following estimates for an AR(2) model of some
returns data
yt = 0.803yt−1...
Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt...
Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt follows an AR(2) model.
Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt...
Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt follows an AR(2) model.
Consider the following AR(1) model: 1. a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. the following random 2. Consider walk...
Consider the following AR(1) model: 1. a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. the following random 2. Consider walk model: yeBo yt-1 +ut, t-0,1,..,T a. Show that yt-3βο + yt-3 + ut + ut-1 + ut-2. b. Suppose that 0-0, show that y.-t βο +4 + ut-1 + + u! c. Suppose that that yo -0, and ut for all t are ii.d. with mean 0 and variance...
a) Consider the following moving average process, MA(2):
a) Consider the following moving average process, MA(2): Yt = ut + α1ut-1 + α2ut-2 where ut is a white noise process, with E(ut)=0, var(ut)=σ2 and cov(ut,us)=0 . Derive the mean, E(Yt), the variance, var(Yt), and the covariances cov( Yt,Yt+1 ) and cov(Yt,Yt+2 ), of this process. b) Give a definition of a (covariance) stationary time series process. Is the MA(2) process (covariance) stationary?
Consider the following model 1. Consider the following AR(1) model: a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. b. Show th...
Consider the following
model
1. Consider the following AR(1) model: a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. b. Show that 1 t-2 2. Consider the following random walk model: ytBo yt-1 +ut, t 0,1,...,T Show that ye 3o yt-3 + ut + Ut-1 +t-2 Suppose that yo - 0, show that yt - tPo + ut + ut-1++u, Suppose that that yo -0, and ut for all t...
3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Deriv...
3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Derive the AR (2) coefficients for y, as a function of Ao and λι
3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Derive the AR (2) coefficients for y, as a function of Ao and λι
Thank you. I. Derive the error correction model for a model of the type yt-A-+ ßiVt-1...
Thank you.
I. Derive the error correction model for a model of the type yt-A-+ ßiVt-1 + β2zı + ut-Show all your steps
1. [30 pts! Let Yǐ follow a moving average process of order 1 (ie, MA(1): where e is a white noise process with N(0,1). Suppose that you estimate the model using STATA. You obtain ê-1, ê-0.5 and ớ2-1...
1. [30 pts! Let Yǐ follow a moving average process of order 1 (ie, MA(1): where e is a white noise process with N(0,1). Suppose that you estimate the model using STATA. You obtain ê-1, ê-0.5 and ớ2-1. You also know e,-2 and E1-1-3. (a) Obtain the unconditional mean and variance of Y (b) Obtain Cor(Y, Yi-1). (c) Obtain the autocorrelation of order 1 for Y
1. [30 pts! Let Yǐ follow a moving average process of order 1 (ie,...
Consider the model, Yt = Bo + B1 X1,1+B1 X2,t + Ut, and this is estimated...
Consider the model, Yt = Bo + B1 X1,1+B1 X2,t + Ut, and this is estimated using OLS with 350 observations. You run some tests with the following results: DWH fails to reject, BG fails to reject, and White test rejects. Select the combination of approaches for the most appropriate estimation of the coefficients. A. Use 2 SLS B. Heteroskedastic correction using X2,t only. c. Use Newey-West HAC. D. Use OLS. Heteroskedastic correction using X1,t only. F. Use White's heteroskedastic-consistent...
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 f...
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...
Consider the following AR(1) model: 1. a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. the following random 2. Consider walk model: yeBo yt-1 +ut, t-0,1,..,T a. Show that yt-3βο + yt-3 + ut + ut-1 + ut-2. b. Suppose that 0-0, show that y.-t βο +4 + ut-1 + + u! c. Suppose that that yo -0, and ut for all t are ii.d. with mean 0 and variance...
Consider the following
model
1. Consider the following AR(1) model: a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. b. Show that 1 t-2 2. Consider the following random walk model: ytBo yt-1 +ut, t 0,1,...,T Show that ye 3o yt-3 + ut + Ut-1 +t-2 Suppose that yo - 0, show that yt - tPo + ut + ut-1++u, Suppose that that yo -0, and ut for all t...
3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Derive the AR (2) coefficients for y, as a function of Ao and λι
3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Derive the AR (2) coefficients for y, as a function of Ao and λι
Thank you.
I. Derive the error correction model for a model of the type yt-A-+ ßiVt-1 + β2zı + ut-Show all your steps
1. [30 pts! Let Yǐ follow a moving average process of order 1 (ie, MA(1): where e is a white noise process with N(0,1). Suppose that you estimate the model using STATA. You obtain ê-1, ê-0.5 and ớ2-1. You also know e,-2 and E1-1-3. (a) Obtain the unconditional mean and variance of Y (b) Obtain Cor(Y, Yi-1). (c) Obtain the autocorrelation of order 1 for Y
1. [30 pts! Let Yǐ follow a moving average process of order 1 (ie,...
Consider the model, Yt = Bo + B1 X1,1+B1 X2,t + Ut, and this is estimated using OLS with 350 observations. You run some tests with the following results: DWH fails to reject, BG fails to reject, and White test rejects. Select the combination of approaches for the most appropriate estimation of the coefficients. A. Use 2 SLS B. Heteroskedastic correction using X2,t only. c. Use Newey-West HAC. D. Use OLS. Heteroskedastic correction using X1,t only. F. Use White's heteroskedastic-consistent...
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...
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