Time Series Notes (5) - Parameter estimation

Introduction

The hyper parameters of a time series model, for example, $p,d,q$ for an $\text{ARIMA}(p,d,q)$ model is already known. And the model will be completely determined if the values of parameters like

$\phi_1,\dots,\phi_p,\theta_1,\dots,\theta_p,\theta_0\text{ and }\sigma_a^2$

are further known.

Then we have to use methods to estimate these values based on observed values from ${Z_t}$.

Conditional least squares (CLS)

The conditional least square is derived from the basic linear regression, where we can see $Z_{t+1}$ as $Y$ in the linear regression and the other $Z_t,\dots,Z_{t-p}$ as the $X_i$ In the linear regression.

Consider an $\text{AR}(p)$ model $Z_t=\theta_0+\phi_1Z_{t-1}+\phi_2Z_{t-2}+\cdots+\phi_pZ_{t-p}+a_t$, the conditional least square estimates are

$(\hat\theta_0,\hat\theta_1,\dots,\hat\theta_p)=\underset{\theta_0,\phi_1,\dots,\phi_p}{\text{argmin}}\sum_{t=p+1}^n(Z_t-\theta_0-\phi_1Z_{t-1}-\cdots-\phi_pZ_{t-p})^2$

and

$\hat\sigma_a^2=\frac{1}{n-p}\sum_{t=p+1}^n(Z_t-\hat\theta_0-\hat\phi_1Z_{t-1}-\cdots-\hat\phi_pZ_{t-p})^2$

Notice there is a common term in above two expressions. We call the term

$S_C(\theta_0,\phi_1,\dots,\phi_p)=\sum_{t=p+1}^n(Z_t-\theta_0-\phi_1Z_{t-1}-\cdots-\phi_pZ_{t-p})^2$

the conditional sum of squares function. In this way, we can rewrite the formulas into the following styles:

\begin{align} & (\hat\theta_0,\hat\theta_1,\dots,\hat\theta_p)=\text{argmin}\space S_C(\theta_0,\phi_1,\dots,\phi_p) \\ & \hat\sigma_a^2=\frac{1}{n-p}S_C(\hat\theta_0,\hat\phi_1,\dots,\hat\phi_p) \end{align}

CLS on $\text{AR}(1)$ process

The conditional sum of squares function for $\text{AR}(1)$ process is

$S_C(\mu,\theta_0)=\sum_{t=2}^n[Z_t-\theta_0-\phi Z_{t-1}]^2$

Therefore, the CLS estimates $(\hat\theta_0,\hat\phi_0)$ satisfy

\begin{align} & \hat\phi=\frac{\sum_{t=2}^n(Z_t-\bar y)(Z_{t-1}-\bar x)}{\sum_{t=2}^n(Z_{t-1}-\bar x)^2} \\ & \bar y=\hat\theta_0+\hat\phi\bar x \end{align}

where

$\bar y=\frac{1}{n-1}\sum_{t=2}^nZ_t,\space\space\space\space\bar x=\frac{1}{n-1}\sum_{t=2}^nZ_{t-1}$

Especially, for large $n$,

$\bar y\approx\bar Z=\frac{1}{n}\sum_{t=1}^nZ_t,\space\space\space\space\bar x\approx\bar Z$

Therefore,

$\hat\theta_0\approx(1-\hat\phi)\bar Z,\space\space\space\space\hat\mu=\frac{\hat\theta_0}{1-\hat\phi}\approx\bar Z$

and

$\hat\phi=\frac{\sum_{t=2}^n(Z_t-\bar y)(Z_{t-1}-\bar x)}{\sum_{t=2}^n(Z_{t-1}-\bar x)^2}\approx\frac{\sum_{t=2}^n(Z_t-{\color{red}\bar Z})(Z_{t-1}-{\color{red}\bar Z})}{\sum_{t=2}^n(Z_{t-1}-{\color{red}\bar Z})^2}\approx\frac{\sum_{t=2}^n(Z_t-{\color{red}\bar Z})(Z_{t-1}-{\color{red}\bar Z})}{\sum_{\color{green}t=1}^n(Z_{t-1}-{\color{red}\bar Z})^2}=r_1(\hat\rho_1)$

CLS on $\text{MA}(1)$ process

Suppose we have an invertible process $Z_t=a_t+\theta a_{t-1}$, where $\vert\theta\vert<1$, the conditional sum of squares function is

$S_C(\theta)=\sum_{t=1}^n(a_t)^2$

By invertibility and truncate at $0=Z_0=Z_{-1}=Z_{-2}=\cdots$, the above sum of squares can be written as

$S_C(\theta)=\sum_{t=1}^n[Z_t-\theta Z_{t-1}+\theta^2Z_{t-2}-\cdots+(-\theta)^{t-1}Z_1]^2$

Note that if $a_0=0$, then we have

$a_1=Z_1,\space\space\space\space a_2=Z_2-Z_1=Z_2-\theta a_1,\space\space\space\space a_3=Z_3-\theta Z_2+\theta^2Z_1=Z_3-\theta a_2,\space\space\cdots\space\space a_n=Z_n-\theta a_{n-1}$

It is impossible to directly calculate the conditional least square estimates for MA and ARMA models. Some numerical optimization methods, such as Gaussian Newton, are usually used to search the estimates.

Maximum likelihood (ML) and unconditional least squares (ULS)

For any set of observations $Z_1,Z_2,\cdots,Z_n$ (time series or otherwise), the likelihood function $L$ is defined to be the probability (density) value of obtaining the data actually observed. However, it is considered as a function of the parameters in the model.

Advantage: First, all of the information in the data is used rather than just the first and second moments, as is the case with least squares. Second, many large-sample results are known under very general conditions.

Disadvantage: The method needs a specific joint probability density function of the process, which is sometimes complex.

ULS and ML on $\text{AR}(1)$ model

Consider an $\text{AR}(1)$ model $Z_t-\mu=\phi(Z_{t-1}-\mu)+a_t$, where the white noise ${a_t}\sim i.i.d.N(0,\sigma_a^2)$, and the unknown parameters are $\mu,\phi,\sigma_a^2$.

The random variable $Z_t$, conditional on ${Z_{t-1},Z_{t-2},\dots}$, will follow the normal distribution $N{\mu+\phi(Z_{t−1} −\mu),\sigma_a^2}$, and hence has the density

$f(z|Z_{t-1},Z_{t-2},\dots)=f(z|Z_{t-1})=(2\pi\sigma_a^2)^{-\frac{1}{2}}\exp\{-\frac{[z-\mu-\phi(Z_{t-1}-\mu)]^2}{2\sigma_a^2}\}$

Replace $Z_t$ as its MA presentation, we have the likelihood function for $Z_1,\dots,Z_n$ is

$\begin{split} L(\phi,\mu,\sigma_a^2)&=f(z_n,z_{n-1},\dots,z_1) = f(z_n|z_{n-1},\dots,z_1)f(z_{n-1},\dots,z_1) \\ &=f(z_n|z_{n-1})f(z_{n-1},\dots,z_1)=\cdots \\ &=f(z_n|z_{n-1})f(z_{n-1}|z_{n-2})\cdots f(z_2|z_1)f(z_1) \\ &=(2\pi\sigma_a^2)^{-\frac{n}{2}}(1-\phi^2)^{\frac{1}{2}}\exp\{-\frac{1}{2\sigma_a^2}S(\phi,\mu)\} \end{split}$

where

$\begin{split} {\color{green}S(\phi,\mu)}&=\sum_{t=2}^n[(Z_t-\mu)-\phi(Z_{t-1}-\mu)]^2+(1-\phi^2)(Z_1-\mu)^2 \\ &={\color{red}S_C(\phi,\mu)}+(1-\phi^2)(Z_1-\mu)^2 \end{split}$

is called the unconditional sum of squares function.

When $\mu=0$, we let $Z_1=0$, the ML method is exactly the same as the CLS method.

The ML estimates for $\mu,\phi,\sigma_a^2$ will minimize the log-likelihood function

$-\frac{n}{2}\log(2\pi)-\frac{n}{2}\log(\sigma_a^2)+\frac{1}{2}\log(1-\phi^2)-\frac{1}{2\sigma_a^2}S(\phi,\mu)$

As a compromise between conditional least squares (CLS) estimates and full maximum likelihood (ML) estimates, the unconditional least squares (ULS) estimates are

\begin{align} & (\hat\phi,\hat\mu)=\text{argmin}\space S(\phi,\mu)=\text{argmin}\{S_C(\phi,\mu)+(1-\phi^2)(Z_1-\mu)\} \\ & \hat\sigma_a^2=\frac{1}{n-1}S(\hat\phi,\hat\mu) \end{align}

Properties of the estimates

The CLS, ULS and ML estimates have the same large-sample properties.

Asymptotic variances of the estimates for a few low order ARMA models are as follows:

\begin{align} \text{AR}(1)&:\text{Var}(\hat\phi)\approx\frac{1}{n}(1-\phi^2) \\ \text{AR}(2)&:\left\{\begin{matrix}\text{Var}(\hat\phi_1)\approx\text{Var}(\hat\phi_2)\approx\frac{1}{n}(1-\phi_2^2)\\\text{corr}(\hat\phi_1,\hat\phi_2)\approx-\frac{\phi_1}{1-\phi_2}=-\rho_1\end{matrix}\right. \\ \text{MA}(1)&:\text{Var}(\hat\theta)\approx\frac{1}{n}(1-\theta^2) \\ \text{MA}(2)&:\left\{\begin{matrix}\text{Var}(\hat\theta_1)\approx\text{Var}(\hat\theta_2)\approx\frac{1}{n}(1-\theta_2^2)\\\text{corr}(\hat\theta_1,\hat\theta_2)\approx\frac{\theta_1}{1+\theta_2}\end{matrix}\right. \\ \text{ARMA}(1,1)&:\left\{\begin{matrix}\text{Var}(\hat\phi)\approx\frac{1}{n}(1-\phi^2)(\frac{1+\phi\theta}{\phi+\theta})^2\\\text{Var}(\hat\theta)\approx\frac{1}{n}(1-\theta^2)(\frac{1+\phi\theta}{\phi+\theta})^2\\\text{corr}(\hat\phi,\hat\theta)\approx\frac{\sqrt{(1-\phi^2)(1-\theta^2)}}{1+\phi\theta}\end{matrix}\right. \end{align}

When estimating an $\text{AR}(1)$ process with the $\text{AR}(2)$ model, the variance of the estimates will increase. It is the same for the $\text{MA}$ models.

For the $\text{ARMA}(1,1)$ process with $\phi+\theta\sim0$, the variance may be very large.