Can you forecast an independent white noise series? At any time white nose is uncorrelated with anything in the past. What happens if the future is uncorrelated with anything in the present or past?
If it is an independent white noise, it can not be predicted.
Since it is independent in nature, it can not be predicted based on dependent variables.
Now, since it is a white noise, the future has no correlation with the past, thus there is no meaning of using past data to predict the future.
Thus, by definition, an independent white noise can not be predicted.
Can you forecast an independent white noise series? At any time white nose is uncorrelated with...
(A). Draw the Autocorrelaogram and Partial Autocorrelogram for a White Noise Time Series Process. (B). Assume that the optimal h-steps ahead forecast is noted as fth for a MA(1). Lets also assume that the optimal point forecast is a conditional expectation: Where Qt is the information set at time "t" and "h" is the forecast horizon. Now we can write the MA(1) process at time "t+1" as follows; Ü. What is the optimal one period ahead forecast, f,i? (ii). What...
Recall that a time series {εt} is called a white noise process if i. E[εt] = 0 t ; ii. Cov(εs, εt) = 0 s ≠ t ; iii. Var(εt) = σ2 < ∞ Construct the autocorrelation function f(h), h=0,-+1,-+2,… for the white noise process.
(White noise is not necessarily i.i.d.). Suppose that {Wt} and {Zt} are independent and identically distributed (i.i.d.) sequences, also independent of each other, with P(Wt = 0) = P(Wt = 1) = 1/2 and P(Zt = −1) = P(Zt = 1) = 1/2. Define the time series Xt by Xt = . Show that {Xt} is white but not i.i.d. w (1 – W-1) ZŁ
Let A and B be two independent white-noise processes. What can you say about the time series properties of the process Y = A+B? How will your answer change if the processes A and B are correlated at lag one?
4. Calculate the variance of the time series rt (i.e. Var(rt)) for the following ARMA(1,1) model: where the variance of the white noise series is 0.09. 4. Calculate the variance of the time series rt (i.e. Var(rt)) for the following ARMA(1,1) model: where the variance of the white noise series is 0.09.
: Assume Yt is a time series process and Et is a white noise process with mean zero and constant variance. (a). Write an equation for AR(4) process. (b). Write an equation for AR(5) process. (c). Write an equation for MA(3) process. (d). Write down an equation for MA(2) process. (e). Write an equation for ARMA (4,2) process. (f). Do more research and write an equation for ARIMA (4,0,2) proce
2. A diode can be used as a white noise generator. Calculate the magnitude of shot noise if 100 V is applied to a diode in series with a 10 Ω resistor when measured with an instrument that has a 100 kHz bandwidth. a. If the shot noise signal above is taken across the resistor and amplified by 105 calculate the shot noise at the amplifier output b. 2. A diode can be used as a white noise generator. Calculate...
3. Let Zt) be a Gaussian white noise, that is, a sequence of i.i.d. normal r.v.s each with mean zero and variance 1. Let Y% (a) Using R generate 300 observations of the Gaussian white noise Z. Plot the series and its acf. (b) Using R, plot 300 observations of the series Y -Z. Plot its acf. c) Analyze graphs from (a) and (b). Can you see a difference between the plots of graphs of time series Z and Y?...
1. If you were to graph a time series and it followed a trend that was close to linear, then what type of forecasting model would you use? Multiple Choice Bass model Bivariate linear regression Simple moving average Gompertz curve 2. Visualization of data allows you to ____________________. Multiple Choice be as transparent to management as required more clearly identify the dependent and independent variables better understand if you need more data see stark differences that would not be apparent...
2.4 Let (e) be a zero mean white noise process. Suppose that the observed process is Y = e, + 0,-1, where is either 3 or 1/3. (a) Find the autocorrelation function for {Y} both when 0 = 3 and when 0 = 1/3. (b) You should have discovered that the time series is stationary regardless of the value of and that the autocorrelation functions are the same for 0 = 3 and 0 = 1/3. For simplicity, suppose that...