# Derivation of the distribution of $\hat{\phi}=[\hat{\phi}_1, \cdots, \hat{\phi}_p]$ in AR(p) models

## Background

Consider the following AR($$p$$) model:

$$\dot{X_t} = \phi_1 \dot X_{t-1} + \phi_2 \dot X_{t-2} + \cdots + \phi_p \dot X_{t-p} + \epsilon$$

where $$\dot{X} := X - \mu = X - \mathbb{E}(X)$$, and $$\epsilon \overset{iid}{\sim} N(0, \sigma_\epsilon^2)$$.

## Problem

Derive that the asymptotic distribution of $$\hat{\phi}:=[\hat{\phi}_1, \cdots, \hat{\phi}_p]$$ follows,

$$\sqrt{n}(\hat{\phi} - \phi) \sim N(0, \sigma_\epsilon^2 \Gamma^{-1})$$

where $$\Gamma := \begin{bmatrix} \gamma_0 & \gamma_1 & \gamma_2 & \cdots & \gamma_{k-1} \\ \gamma_1 & \gamma_0 & \gamma_1 & \cdots & \gamma_{k-2} \\ \gamma_2 & \gamma_1 & \gamma_0 & \cdots & \gamma_{k-3} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \gamma_{k-1} & \gamma_{k-2} & \gamma_{k-3} & \cdots & \gamma_0\\ \end{bmatrix}$$

with $$\gamma_k := \text{Cov}(X_t, X_{t-k})$$ : the autocovariance of lag $$k$$.

## Try

To estimate $$\phi_1, \cdots, \phi_p$$, we usually consider the Yule-Walker equation,

$$\mathbf{\rho} := \begin{bmatrix} \rho_1 \\ \rho_2 \\ \vdots \\ \rho_p \end{bmatrix} = \begin{bmatrix} 1 & \rho_1 & \rho_2 & \cdots & \rho_{k-1} \\ \rho_1 & 1 & \rho_1 & \cdots & \rho_{k-2} \\ \rho_2 & \rho_1 & 1 & \cdots & \rho_{k-3} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \rho_{k-1} & \rho_{k-2} & \rho_{k-3} & \cdots & 1 \\ \end{bmatrix} \begin{bmatrix} \phi_1 \\ \phi_2 \\ \vdots \\ \phi_p \end{bmatrix} = \mathbf{P}\mathbf{\phi}$$

where $$\phi_i$$ : the coefficient of the model, and $$\rho_k$$ is the autocorrelation function(ACF) of lag $$k$$.

I think we can use the fact that we can estimate $$\mathbf{\phi}$$

$$\hat{\mathbf{\phi}} = \hat{\mathbf{P}}^{-1}\hat{\mathbf{\rho}}$$

where $$\hat{\mathbf{\rho}}=[\hat{\rho_1}, \cdots, \hat{\rho_k}]^T$$, which is the estimated version of $$\rho$$.

We can estimate $$\rho_k$$ which is a function of lag $$k$$ given data $$\mathbf{x}$$, as follows.

$$\hat{\rho}_k := \frac{ \sum_{t=1}^{n-k} (x_t - \bar{x})(x_{t+k} - \bar{x}) }{\sum_{t=1}^n (x_t - \bar{x})^2}$$

where $$\bar{x} := \left(\sum_{t=1}^n x_t \right)/n$$.

But I cannot proceed to show the result I want,

$$\sqrt{n}(\hat{\phi} - \phi) \sim N(0, \sigma_\epsilon^2 \Gamma^{-1})$$

$$X_t = \pmb X_{t-1}^T \pmb \phi + n_t , \qquad t = p+1,p+2,\ldots$$ where $$\pmb X_{t-1} = [X_{t-1} \ldots X_{t-p} ]$$and $$\pmb \phi = [\phi_1 \ldots \phi_p]$$. Stacking all such equations, we get $$\pmb y = \pmb X \pmb \phi + \pmb n$$ where $$\pmb y = [X_{p+1} \ldots X_N]^T$$ and $$\pmb X = \begin{bmatrix} \pmb X_{p}^T\\ \pmb X_{p+1}^T\\ \vdots \\ \pmb X_{n-1}^T \end{bmatrix}$$ Using OLS, we have $$\hat{\pmb{\phi}} = (\pmb X^T \pmb X)^{-1} \pmb X^T \pmb y = (\pmb X^T \pmb X)^{-1} \pmb X^T (\pmb X \pmb \phi + \pmb n) = \pmb \phi + (\pmb X^T \pmb X)^{-1} \pmb X^T \pmb n$$ Notice that from the above equation $$\mathsf{E}( \hat{\pmb{\phi}} ) = \pmb \phi +(\pmb X^T \pmb X)^{-1} \underbrace{\mathsf{E} \pmb X^T \pmb n}_0 \pmb \phi$$. and $$\mathsf{var}(\hat{\pmb{\phi}}) = \mathsf{E}( (\hat{\pmb{\phi}} - \pmb\phi)(\hat{\pmb{\phi}} - \pmb\phi)^T ) =(\pmb X^T \pmb X)^{-1} \pmb X^T \mathsf{E} (\pmb n \pmb n^T) \pmb X (\pmb X^T \pmb X)^{-1}$$ assuming white noise with same variance across time $$\sigma_\epsilon^2$$, we get $$\mathsf{var}(\hat{\pmb{\phi}}) = \mathsf{E}( (\hat{\pmb{\phi}} - \pmb\phi)(\hat{\pmb{\phi}} - \pmb\phi)^T ) =(\pmb X^T \pmb X)^{-1} \pmb X^T \sigma_\epsilon^2 \pmb I \pmb X (\pmb X^T \pmb X)^{-1} = \sigma_\epsilon^2 (\pmb X^T \pmb X)^{-1}(\pmb X^T \pmb X)(\pmb X^T \pmb X)^{-1}=\sigma_\epsilon^2 (\pmb X^T \pmb X)^{-1}$$
Now assuming your AR(p) is strictly stationary and ergodic and that $$\mathsf{E}(X_t^4)$$ exists, then the central limit theorem applies $$\sqrt{n}( \hat{\pmb{\phi}} - \pmb{\phi}) \longrightarrow N(0,\mathsf{var}(\hat{\pmb{\phi}}))$$
Note that $$(\pmb X^T \pmb X)^{-1} = \Gamma^{-1}$$
• Thank you! But isn't it true that $X^TX \approx N\Gamma$? As far as I can see, for example, $\mathbb{E}[(X^TX)_{11}] =\mathbb{E}[X^2_{p} + X ^2_{p+1} \cdots X^2_{N-1} ] = (N-p)\gamma_0$.. Commented May 23, 2019 at 6:27
• It's just a scaling factor, you're right. Depends if your initial estimator was chosen to be $k. \hat{\alpha}$ or $\hat{\alpha}$, where $k = \frac{1}{\sqrt{N}}$ something like that Commented May 23, 2019 at 6:33