# Book Notes: Intro to Time Series and Forecasting -- Ch5 ARMA Models Estimation and Forecasting

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### Parameter estimation for ARMA$$(p, q)$$

• When the orders $$p, q$$ are known, estimate the parameters $\boldsymbol\phi = (\phi_1, \ldots, \phi_p), \quad \boldsymbol\theta = (\theta_1, \ldots, \theta_q), \quad \sigma^2$
• There are $$p+q+1$$ parameters in total
• Preliminary estimations
• Yule-Walker and Burgâ€™s algorithm: good for AR$$(p)$$
• Innovation algorithm: good for MA$$(q)$$
• Hannan-Rissanen algorithm: good for ARMA$$(p, q)$$
• More efficient estimation: MLE

• When the orders $$p, q$$ are unknown, use model selection methods to select orders
• Minimize one-step MSE: FPE
• Penalized likelihood methods: AIC, AICC, BIC

# Yule-Walker Estimation

### Yule-Walker equations

• $$\{X_t\}$$ is a casual AR$$(p)$$ process $X_t = \phi_1 X_{t-1} + \cdots + \phi_p X_{t-p} + Z_t$

• Multiplying each side by $$X_t, X_{t-1}, \ldots, X_{t-p}$$, respectively, and taking expectation, we got the Yule-Walker equations $\sigma^2 = \gamma(0) - \phi_1 \gamma(1) - \cdots \phi_p \gamma(p)$ $\underbrace{\left[ \begin{array}{cccc} \gamma(0) & \gamma(1) & \cdots &\gamma(p-1) \\ \gamma(1) & \gamma(0) & \cdots &\gamma(p-2) \\ \vdots & \vdots & \vdots & \vdots \\ \gamma(p-1) & \gamma(p-2) & \cdots &\gamma(0) \\ \end{array} \right]}_{\boldsymbol\Gamma_p} \underbrace{ \left[ \begin{array}{c} \phi_1 \\ \phi_2 \\ \vdots \\ \phi_p \\ \end{array} \right]}_{\boldsymbol\phi} = \underbrace{ \left[ \begin{array}{c} \gamma(1) \\ \gamma(2) \\ \vdots \\ \gamma(p) \\ \end{array} \right]}_{\boldsymbol\gamma_p}$

• Vector representation $\boldsymbol\Gamma_p \boldsymbol\phi = \boldsymbol\gamma_p, \quad \sigma^2 = \gamma(0) - \boldsymbol\phi' \boldsymbol\gamma_p$

### Yule-Walker estimator and its properties

• Yule-Walker estimators $$\hat{\boldsymbol\phi} = (\hat{\phi}_1, \cdots, \hat{\phi}_p)$$ are obtained by solving the hatted version of the Yule-Walker equations $\hat{\boldsymbol\phi} = \hat{\boldsymbol\Gamma}_p^{-1}\hat{\boldsymbol\gamma}_p, \quad \hat{\sigma}^2 = \hat{\gamma}(0) - \hat{\boldsymbol\phi}' \hat{\boldsymbol\gamma}_p$

• The fitted model is causal and $$\hat{\sigma}^2 \geq 0$$ $X_t = \hat{\phi}_1 X_{t-1} + \cdots + \hat{\phi}_p X_{t-p} + Z_t, \quad Z_t \sim \textrm{WN}(0, \hat{\sigma}^2)$

• Asymptotic normality $\hat{\boldsymbol\phi} \stackrel{\cdot}{\sim} \textrm{N}\left( \boldsymbol\phi, \frac{\sigma^2 \boldsymbol\Gamma_p^{-1}}{n}\right)$

### Yule-Walker estimator is a moment estimator: because it is obtained by equating theoretical and sample moments

• Usually moment estimators have much higher variance than MLE

• But Yule-Walker estimators of AR$$(p)$$ process have the same asymptotic distribution as the MLE

• Moment estimators can fail for MA$$(q)$$ and general ARMA

• For example, MA$$(1)$$: $$X_t = Z_t + \theta Z_{t+1}$$ with $$\{Z_t\}\sim \textrm{WN} (0, \sigma^2)$$. $\gamma(0) = (1+\theta^2)\sigma^2, \quad \gamma(1) = \theta \sigma^2 \quad \Longrightarrow \quad \rho(1) = \frac{\theta}{1+\theta^2}$ Moment estimator of $$\theta$$ is obtained by solving $\hat{\rho}(1) = \frac{\hat{\theta}}{1+\hat{\theta}^2} \quad \Longrightarrow \quad \hat{\theta} = \frac{1 \pm \sqrt{1 - 4 \hat{\rho}(1)^2}}{2 \hat{\rho}(1)}$ This can yield complex $$\hat{\theta}$$ if $$|\hat{\rho}(1)| > 1/2$$, which can happen if $$\rho(1) = 1/2$$, i.e., $$\theta = 1$$

### Innovations algorithm: estimate MA coefficients

• Fitted innovations MA$$(m)$$ model $X_t = Z_t + \hat{\theta}_{m1} Z_{t-1} + \cdots + \cdots + \hat{\theta}_{mm}Z_{t-m}, \quad \{Z_t\} \sim \textrm{WN}(0, \hat{v}_m)$ where $$\hat{\boldsymbol\theta}_m$$ and $$\hat{v}_m$$ are from the innovations algorithm with ACVF replaced by the sample ACVF

• For a MA$$(q)$$ process, the innovations algorithm estimator $$\hat{\boldsymbol\theta}_q = (\hat{\theta}_{q1}, \ldots, \hat{\theta}_{qq})'$$ is NOT consistent for $$(\theta_1, \ldots, \theta_q)'$$

• Choice of $$m$$: increase $$m$$ until the vector $$(\hat{\theta}_{m1}, \ldots, \hat{\theta}_{mq})'$$ stabilizes

# Maximum Likelihood Estimation

### Likelihood function of a Gaussian time series

• Suppose $$\{X_t\}$$ is a Gaussian time series with mean zero

• Assume that covariance matrix $$\boldsymbol\Gamma_n = E(\mathbf{X}_n \mathbf{X}_n')$$ is nonsingular

• One-step predictors using innovations algorithm: $$\hat{X}_1 = 0$$ and $\hat{X}_{j+1} = P_{j} X_{j+1} % = \phi_{j1}X_j + \ldots + \phi_{jj} X_1$ with MSE $$v_j = E\left(X_{j+1} - \hat{X}_{j+1}\right)^2$$

• Example: AR$$(1)$$ $\hat{X}_j = \begin{cases} 0, & j = 1 \\ \phi \hat{X}_{j-1} & j \geq 2 \end{cases}, \quad v_j = \begin{cases} \frac{\sigma^2}{1-\phi^2}, & j = 0 \\ \sigma^2 & j \geq 1 \end{cases}$
• Likelihood function \begin{align*} L & \propto \left| \boldsymbol\Gamma_n \right|^{-1/2} \exp \left( -\frac{1}{2} \mathbf{X}_n' \boldsymbol\Gamma_n^{-1} \mathbf{X}_n\right)\\ & = \left( v_0 v_1 \cdots v_{n-1} \right)^{-1/2} \exp \left[ -\frac{1}{2} \sum_{j=1}^n \frac{(X_j - \hat{X}_j)^2}{v_{j-1}}\right] \end{align*}

### Maximum likelihood estimation of ARMA$$(p, q)$$

• Innovations MSE $$v_j = \sigma^2 r_j$$, where $$r_j$$ depends on $$\boldsymbol\phi$$ and $$\boldsymbol\theta$$

• Maximizing the likelihood is equivalent to minimizing $-2\log L(\boldsymbol\phi, \boldsymbol\theta, \sigma^2) = n\log(\sigma^2) + \sum_{j=1}^n \log(r_{j-1}) + \frac{S(\boldsymbol\phi, \boldsymbol\theta)}{\sigma^2},$ where $S(\boldsymbol\phi, \boldsymbol\theta) = \sum_{j=1}^n \frac{(X_j - \hat{X}_j)^2}{r_{j-1}}$

• MLE $$\hat{\sigma}^2$$ can be expressed with MLE $$\hat{\boldsymbol\phi}, \hat{\boldsymbol\theta}$$ $\hat{\sigma}^2 = \frac{S\left(\hat{\boldsymbol\phi}, \hat{\boldsymbol\theta}\right)}{n}$

• MLE $$\hat{\boldsymbol\phi}, \hat{\boldsymbol\theta}$$ are obtained by minimizing $\log\left[ \frac{S(\boldsymbol\phi, \boldsymbol\theta)}{n} \right]+ \frac{1}{n} \sum_{j=1}^n \log(r_{j-1})$ Not depend on $$\sigma^2$$!

### Asymptotic normality of MLE

• When $$n$$ is large, for a causal and invertible ARMA$$(p, q)$$ process, $\left[ \begin{array}{c} \hat{\boldsymbol\phi}\\ \hat{\boldsymbol\theta} \end{array} \right] \stackrel{\cdot}{\sim}\textrm{N}_{p+1} \left( \left[ \begin{array}{c} \hat{\boldsymbol\phi}\\ \hat{\boldsymbol\theta} \end{array} \right], \frac{\mathbf{V}}{n} \right)$

• For an AR$$(p)$$ process, MLE has the same asymptotic distribution as the Yule-Walker estimator $\mathbf{V} = \sigma^2 \boldsymbol\Gamma_p^{-1} \quad \Longrightarrow \quad \hat{\boldsymbol\phi} \stackrel{\cdot}{\sim} \textrm{N}\left( \boldsymbol\phi, \frac{\sigma^2 \boldsymbol\Gamma_p^{-1}}{n}\right)$

### Examples of $$\mathbf{V}$$

• AR$$(1)$$ $\mathbf{V} = 1 - \phi_1^2$

• AR$$(2)$$ $\mathbf{V} = \left[ \begin{array}{cc} 1 - \phi_2^2 & -\phi_1(1 + \phi_2)\\ -\phi_1(1 + \phi_2) & 1 - \phi_2^2\\ \end{array} \right]$

• MA$$(1)$$ $\mathbf{V} = 1 - \theta_1^2$

• MA$$(2)$$ $\mathbf{V} = \left[ \begin{array}{cc} 1 - \theta_2^2 & \theta_1(1 - \theta_2)\\ \theta_1(1 - \theta_2)& 1 - \theta_2^2\\ \end{array} \right]$

• ARMA$$(1,1)$$ $\mathbf{V} = \frac{1 + \phi \theta}{(\phi + \theta)^2} \left[ \begin{array}{cc} (1 - \phi^2)(1 + \phi \theta) & -(1 - \theta^2)(1 - \phi^2) \\ -(1 - \theta^2)(1 - \phi^2) & (1 - \phi^2)(1 + \phi \theta) \\ \end{array} \right]$

# Order Selection

### Order selection

• Why? Harm of using too large $$p, q$$ to fit models:

• Large errors arising from parameter estimation of the model
• Large MSEs of forecasts
• FPE: only for AR$$(p)$$ processes $\text{FPE} = \hat{\sigma}^2 \frac{n+p}{n-p}$

• AIC: for ARMA$$(p, q)$$; approximate Kullback-Leibler discrepancy of the fitted model and the true model, a penalized likelihood method $\text{AIC} = -2\log (\hat{L}) + 2(p + q + 1)$

• AICC: for ARMA$$(p, q)$$; a bias-corrected version of AIC, a penalized likelihood method $\text{AICC} = -2\log (\hat{L}) + 2(p + q + 1) \cdot \frac{n}{n-p-q-2}$

# Diagnostic Checking

### Residuals and rescaled residuals

• Residuals of an ARMA$$(p, q)$$ process $\hat{W}_t = \frac{X_t - \hat{X}_t\left(\hat{\boldsymbol\phi}, \hat{\boldsymbol\theta}\right)} {\sqrt{r_{t-1}\left(\hat{\boldsymbol\phi}, \hat{\boldsymbol\theta}\right)}}, \quad t = 1, \ldots, n$
• Residuals $$\{\hat{W}_t\}$$ should be similar to white noises $$\{Z_t\}$$
• Rescaled residuals $\hat{R}_t = \frac{\hat{W}_t}{\hat{\sigma}}, \quad \hat{\sigma} = \sqrt{\frac{\sum_{t=1}^n \hat{W}_t^2}{n}}$
• Residuals residuals should be approximately $$\textrm{WN}(0, 1)$$

### Residual diagnostics

1. Plot $$\{\hat{R}_t\}$$ and look for patterns

2. Compute the sample ACF of $$\{\hat{R}_t\}$$
• It should be close to the $$\textrm{WN}(0, 1)$$ sample ACF
3. Apply Chapter 1 tests for IID noises

### References

• Brockwell, Peter J. and Davis, Richard A. (2016), Introduction to Time Series and Forecasting, Third Edition. New York: Springer