Based on correlogram, write down MA(q) or AR(p) model.
Based on correlogram, write down MA(q) or AR(p) model.
Homework Answers
Answer:
correlogram:
In time series analysis, a correlogram, also known as an autocorrelation plot, is a plot of the sample autocorrelations versus (the time lags). If cross-correlation is used, the result is called a cross-correlogram
Based on correlogram,
Ma Model :
MA model. For many time series the first difference is often sufficient to render a series stationary.
A simple mechanism to determine whether a series is non-stationary relates to mean reversion so series that trend do not mean revert and are normally non-stationary.
While series that criss-cross the mean value are generally
stationary.
Write down the AR(2) model and prove whether it is stationary or not.
Write down the AR(2) model and prove whether it is stationary or not.
3. Write the complete model for the flowing: (a) AR(P = 2)d=12 (b) MAQ = 2)d=12...
3. Write the complete model for the flowing: (a) AR(P = 2)d=12 (b) MAQ = 2)d=12 (c) ARMA(P = 1,Q = 2).-12 (d) ARMA(P = 2,Q=0)d=12 (e) ARMA(p = 0,9 = 2) (P = 1,Q = 2)d-12 (f) SARIM A(p = 1, d = 19 = 1) (P = 1, D = 1,Q = 1).-12
2. FoRECASTING wITH MA PROCESSES. (i) How to check the invertibility of an MA(1) model? (ii) Suppose we use an MA model to model the process represented in Figure 2. Write down the model and find the...
2. FoRECASTING wITH MA PROCESSES. (i) How to check the invertibility of an MA(1) model? (ii) Suppose we use an MA model to model the process represented in Figure 2. Write down the model and find the estimates of the coefficients. (15 marks) 200 225 250 275 300 325 350 375 400 Sample: 1 2000 Included observations: 1999 Autocorrelation Partial Correlation AC PAC 10.498 0.498 2-0.042-0.386 -0.081 0.220 40.042 -0.183 5 0.003 0.157 6 0.013-0.127 7 0.019 0.131 8 0.013-0.112...
Convert the following auto-regressive (AR) model into a moving average (MA) model: Y, = 1 +...
Convert the following auto-regressive (AR) model into a moving average (MA) model: Y, = 1 + 0.19-1 +ęt
using the liners to write down the equation. 16 zód D=Q=20-21 s= ? 10- £Q P=2+ - Q Q = -412P p*= 6 Q*=8
using the liners to write down the equation.
16 zód D=Q=20-21 s= ? 10- £Q P=2+ - Q Q = -412P p*= 6 Q*=8
consider the ARIMA model 8. Consider the ARIMA model X,-4 + Xt-1 + W-0.75W,-1, W, ~ WN(0, σ*) a. Identify p, d, and q. Write the corresponding ARMA (p,q) model. b. Find E VX and VarVX 8. Cons...
consider the ARIMA model
8. Consider the ARIMA model X,-4 + Xt-1 + W-0.75W,-1, W, ~ WN(0, σ*) a. Identify p, d, and q. Write the corresponding ARMA (p,q) model. b. Find E VX and VarVX
8. Consider the ARIMA model X,-4 + Xt-1 + W-0.75W,-1, W, ~ WN(0, σ*) a. Identify p, d, and q. Write the corresponding ARMA (p,q) model. b. Find E VX and VarVX
Suppose that the following equations describe a patent-protected monopolist. AR: P = 4-0.0025(Q) MR: P = 4-0.005(Q) ...
Suppose that the following equations describe a patent-protected monopolist. AR: P = 4-0.0025(Q) MR: P = 4-0.005(Q) MC: P = 0.4+0.003(Q) If the firm is maximizing profits, what will their total revenue be?
Based on the information below, answer the questions (a)-(g) Price (P) Quantity (Q) Revenue Marginal Revenue...
Based on the information below, answer the questions (a)-(g) Price (P) Quantity (Q) Revenue Marginal Revenue 20 0 18 2 16 4 14 6 12 8 10 10 8 12 6 14 4 16 2 18 0 20 (a) Based on the information above, write down the demand equation. (b) Write the marginal revenue equation. (c) Given that the marginal cost is Q, what would be the profit-maximizing level of Q? (d) What would be the profit-maximizing level of P?...
explain which time series model is more likely to describe the time series, an AR(1), AR(2),...
explain which time series model is more likely to describe the
time series, an AR(1), AR(2), MA(1) or MA(2) process? Justify your
answer
Sample: 1983 2017 Included observations: 35 Autocorrelation Partial Correlation AC PAC Q-Stat Prob 1 1 I 1 1 inta I 1 1 0.517 0.517 10.164 0.001 2 0.234 -0.044 12.318 0.002 3 0.234 0.178 14.526 0.002 4 0.158 -0.041 15.570 0.004 5 0.216 0.193 17.576 0.004 6 0.281 0.103 21.098 0.002 7 0.106 -0.144 21.619 0.003 8...
It is decided to use an AR model (a linear prediction model) of an observed signal...
It is decided to use an AR model (a linear prediction model) of
an observed signal x(n) in order to estimate the
signal’s power spectral density. The model linear prediction
parameters will be determined using least squares
analysis, based on the equations:
1. In a particular experiment we observe x(n) = 1.0, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 for n = 0, 1, 2, 3, 4,
5, 6, 7, 8, 9 respectively. Fill in specific numeric values...
3. Write the complete model for the flowing: (a) AR(P = 2)d=12 (b) MAQ = 2)d=12 (c) ARMA(P = 1,Q = 2).-12 (d) ARMA(P = 2,Q=0)d=12 (e) ARMA(p = 0,9 = 2) (P = 1,Q = 2)d-12 (f) SARIM A(p = 1, d = 19 = 1) (P = 1, D = 1,Q = 1).-12
2. FoRECASTING wITH MA PROCESSES. (i) How to check the invertibility of an MA(1) model? (ii) Suppose we use an MA model to model the process represented in Figure 2. Write down the model and find the estimates of the coefficients. (15 marks) 200 225 250 275 300 325 350 375 400 Sample: 1 2000 Included observations: 1999 Autocorrelation Partial Correlation AC PAC 10.498 0.498 2-0.042-0.386 -0.081 0.220 40.042 -0.183 5 0.003 0.157 6 0.013-0.127 7 0.019 0.131 8 0.013-0.112...
Convert the following auto-regressive (AR) model into a moving average (MA) model: Y, = 1 + 0.19-1 +ęt
using the liners to write down the equation.
16 zód D=Q=20-21 s= ? 10- £Q P=2+ - Q Q = -412P p*= 6 Q*=8
consider the ARIMA model
8. Consider the ARIMA model X,-4 + Xt-1 + W-0.75W,-1, W, ~ WN(0, σ*) a. Identify p, d, and q. Write the corresponding ARMA (p,q) model. b. Find E VX and VarVX
8. Consider the ARIMA model X,-4 + Xt-1 + W-0.75W,-1, W, ~ WN(0, σ*) a. Identify p, d, and q. Write the corresponding ARMA (p,q) model. b. Find E VX and VarVX
explain which time series model is more likely to describe the
time series, an AR(1), AR(2), MA(1) or MA(2) process? Justify your
answer
Sample: 1983 2017 Included observations: 35 Autocorrelation Partial Correlation AC PAC Q-Stat Prob 1 1 I 1 1 inta I 1 1 0.517 0.517 10.164 0.001 2 0.234 -0.044 12.318 0.002 3 0.234 0.178 14.526 0.002 4 0.158 -0.041 15.570 0.004 5 0.216 0.193 17.576 0.004 6 0.281 0.103 21.098 0.002 7 0.106 -0.144 21.619 0.003 8...
It is decided to use an AR model (a linear prediction model) of
an observed signal x(n) in order to estimate the
signal’s power spectral density. The model linear prediction
parameters will be determined using least squares
analysis, based on the equations:
1. In a particular experiment we observe x(n) = 1.0, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 for n = 0, 1, 2, 3, 4,
5, 6, 7, 8, 9 respectively. Fill in specific numeric values...
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