Deterministic vs stochastic elements

Regression with autocorrelated errors

Regression with temporal random effects

Dynamic Factor Analysis (DFA)

• Forms of covariance matrix

• Constraints for model fitting

• Interpretation of results

Consider this simple model, consisting of a mean \(\mu\) plus error

\[ y_i = \mu + e_i ~ \text{with} ~ e_i \sim \text{N}(0,\sigma^2) \]

The right-hand side of the equation is composed of deterministic and stochastic pieces

\[ y_i = \underbrace{\mu}_{\text{deterministic}} + \underbrace{e_i}_{\text{stochastic}} \]

Sometime these pieces are referred to as fixed and random

\[ y_i = \underbrace{\mu}_{\text{fixed}} + \underbrace{e_i}_{\text{random}} \]

This can also be seen by rewriting the model

\[ y_i = \mu + e_i ~ \text{with} ~ e_i \sim \text{N}(0,\sigma^2) \]

as

\[ y_i \sim \text{N}(\mu,\sigma^2) \]

We can expand the deterministic part of the model, as with linear regression

\[ y_i = \underbrace{\alpha + \beta x_i}_{\text{mean}} + e_i ~ \text{with} ~ e_i \sim \text{N}(0,\sigma^2) \]

so

\[ y_i \sim \text{N}(\alpha + \beta x_i,\sigma^2) \]

Consider a simple model with a mean \(\mu\) plus white noise

\[ y_t = \mu + e_t ~ \text{with} ~ e_t \sim \text{N}(0,\sigma^2) \]

We can expand the deterministic part of the model, as before with linear regression

\[ y_t = \underbrace{\alpha + \beta x_t}_{\text{mean}} + e_t ~ \text{with} ~ e_t \sim \text{N}(0,\sigma^2) \]

so

\[ y_t \sim \text{N}(\alpha + \beta x_t,\sigma^2) \]

These do not look like white noise!

There is significant autocorrelation at lag = 1

We can expand the stochastic part of the model to have autocorrelated errors

\[ y_t = \alpha + \beta x_t + e_t \\ e_t = \phi e_{t-1} + w_t \]

with \(w_t \sim \text{N}(0,\sigma^2)\)

We can expand the stochastic part of the model to have autocorrelated errors

\[ y_t = \alpha + \beta x_t + e_t \\ e_t = \phi e_{t-1} + w_t \]

with \(w_t \sim \text{N}(0,\sigma^2)\)

We can write this model as our standard state-space model

\[ \begin{align} y_t &= \alpha + \beta x_t + e_t \\ &= e_t + \alpha + \beta x_t\\ &\Downarrow \\ y_t &= x_t + a + D d_t + v_t\\ \end{align} \]

with

\(x_t = e_t\), \(a = \alpha\), \(D = \beta\), \(d_t = x_t\), \(v_t = 0\)

\[ \begin{align} e_t &= \phi e_{t-1} + w_t \\ &\Downarrow \\ x_t &= B x_t + w_t\\ \end{align} \]

with

\(x_t = e_t\) and \(B = \phi\)

\[ y_t = \alpha + \beta x_t + e_t \\ e_t = \phi e_{t-1} + w_t \\ \Downarrow \\ y_t = a + D d_t + x_t\\ x_t = B x_t + w_t \]

\[ y_t = a + D d_t + x_t \\ \Downarrow \\ y_t = Z x_t + a + D d_t + v_t \]

y = data         ## [1 x T] matrix of data
a = matrix("a")  ## intercept
D = matrix("D")  ## slope
d = covariate    ## [1 x T] matrix of measured covariate
Z = matrix(1)    ## no multiplier on x 
R = matrix(0)    ## v_t ~ N(0,R); want v_t = 0 for all t

\[ x_t = B x_t + w_t \\ \Downarrow \\ x_t = B x_t + u + C c_t + w_t \]

B = matrix("b")  ## AR(1) coefficient for model errors
Q = matrix("q")  ## w_t ~ N(0,Q); var for model errors
u = matrix(0)    ## u = 0
C = matrix(0)    ## C = 0
c = matrix(0)    ## c_t = 0 for all t

Recall our simple model

\[ y_t = \underbrace{\mu}_{\text{fixed}} + \underbrace{e_t}_{\text{random}} \]

We can expand the random portion

\[ y_t = \underbrace{\mu}_{\text{fixed}} + ~ \underbrace{f_t + e_t}_{\text{random}} \]

\[ e_t \sim \text{N}(0, \sigma) \\ f_t \sim \text{N}(f_{t-1}, \gamma) \]

We can expand the random portion

\[ y_t = \underbrace{\mu}_{\text{fixed}} + ~ \underbrace{f_t + e_t}_{\text{random}} \]

\[ e_t \sim \text{N}(0, \sigma) \\ f_t \sim \text{N}(f_{t-1}, \gamma) \]

This is simply a random walk observed with error

\[ y_t = \mu + f_t + e_t ~ \text{with} ~ e_t \sim \text{N}(0, \sigma) \\ f_t = f_{t-1} + w_t ~ \text{with} ~ w_t \sim \text{N}(0, \gamma) \\ \Downarrow \\ y_t = a + x_t + v_t ~ \text{with} ~ v_t \sim \text{N}(0, R) \\ x_t = x_{t-1} + w_t ~ \text{with} ~ w_t \sim \text{N}(0, Q) \]

We can expand the fixed portion

\[ y_t = \underbrace{\alpha + \beta x_t}_{\text{fixed}} + ~ \underbrace{f_t + e_t}_{\text{random}} \]

\[ e_t \sim \text{N}(0, \sigma) \\ f_t \sim \text{N}(f_{t-1}, \gamma) \]

\[ y_t = \alpha + \beta x_t + f_t + e_t ~ \text{with} ~ e_t \sim \text{N}(0, \sigma) \\ f_t = f_{t-1} + w_t ~ \text{with} ~ w_t \sim \text{N}(0, \gamma) \\ \Downarrow \\ y_t = a + D d_t + x_t + v_t ~ \text{with} ~ v_t \sim \text{N}(0, R) \\ x_t = x_{t-1} + w_t ~ \text{with} ~ w_t \sim \text{N}(0, Q) \]

\[ y_{i,t} = x_{i,t} + a_i + v_{i,t} \\ x_{i,t} = x_{i,t-1} + w_{i,t} \]

with

\(v_{i,t} \sim \text{N}(0, R)\)

\(w_{i,t} \sim \text{N}(0, Q)\)

\[ y_{1,t} = x_{1,t} + a_1 + v_{1,t} \\ y_{2,t} = x_{2,t} + a_2 + v_{2,t} \\ \vdots \\ y_{n,t} = x_{n,t} + a_2 + v_{n,t} \\ \]

\[ x_{1,t} = x_{1,t-1} + w_{1,t} \\ x_{2,t} = x_{2,t-1} + w_{2,t} \\ \vdots \\ x_{n,t} = x_{n,t-1} + w_{n,t} \]

\[ \mathbf{y}_t = \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

with

\(\mathbf{v}_t \sim \text{MVN}(\mathbf{0}, \mathbf{R})\)

\(\mathbf{w}_t \sim \text{MVN}(\mathbf{0}, \mathbf{Q})\)

We often observe covariance among environmental time series, especially for those collected close to one another in space

Are there some common patterns here?

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

We can make (test) assumptions by specifying different forms for \(\mathbf{Z}\)

\[ \begin{bmatrix} y_1 \\ y_2 \\ y_3 \\ y_4 \\ y_5 \end{bmatrix}_t = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 1 \\ \end{bmatrix} \times \begin{bmatrix} x_{JF} \\ x_N \\ x_S \end{bmatrix}_t + \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ a_4 \\ a_5 \end{bmatrix} + \begin{bmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \\ v_5 \end{bmatrix}_t \]

\[ \begin{bmatrix} x_{JF} \\ x_N \\ x_S \end{bmatrix}_t = \begin{bmatrix} x_{JF} \\ x_N \\ x_S \end{bmatrix}_{t-1} + \begin{bmatrix} w_{JF} \\ w_N \\ w_S \end{bmatrix}_t \]

What if our observations were instead a mixture of 2+ states?

For example, we sampled haul-outs located between several breeding sites

\[ \begin{bmatrix} y_1 \\ y_2 \\ y_3 \\ y_4 \\ y_5 \end{bmatrix}_t = \begin{bmatrix} 0.8 & 0.2 & 0 \\ 0.2 & 0.7 & 0.1 \\ 0 & 0.9 & 0.1 \\ 0 & 0.3 & 0.7 \\ 0 & 0.1 & 0.9 \\ \end{bmatrix} \times \begin{bmatrix} x_{JF} \\ x_N \\ x_S \end{bmatrix}_t + \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ a_4 \\ a_5 \end{bmatrix} + \begin{bmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \\ v_5 \end{bmatrix}_t \]

\[ \begin{bmatrix} x_{JF} \\ x_N \\ x_S \end{bmatrix}_t = \begin{bmatrix} x_{JF} \\ x_N \\ x_S \end{bmatrix}_{t-1} + \begin{bmatrix} w_{JF} \\ w_N \\ w_S \end{bmatrix}_t \]

What if our observations were a mixture of states, but we didn’t know how many or the weightings?

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

What are the dimensions of \(\mathbf{Z}\)?

What are the elements within \(\mathbf{Z}\)?

DFA is a dimension reduction technique, which models \(n\) observed time series as a function of \(m\) hidden states (patterns), where \(n \gg m\)

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

data: \(\mathbf{y}_t\) is \(n \times 1\)

loadings: \(\mathbf{Z}\) is \(n \times m\) with \(n > m\)

states: \(\mathbf{x}_t\) is \(m \times 1\)

Goal is to reduce some large number of correlated variates into a few uncorrelated factors

Calculating the principal components requires us to estimate the covariance of the data

\[ \text{PC} = \text{eigenvectors}(\text{cov}(\mathbf{y})) \]

There will be \(n\) principal components (eigenvectors) for an \(n \times T\) matrix \(\mathbf{y}\)

We reduce the dimension by selecting a subset of the components that explain much of the variance (eg, the first 2)

We need to estimate the covariance in the data \(\mathbf{y}\)

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t, ~ \text{with} ~ \mathbf{v}_t \sim \text{MVN}(\mathbf{0}, \mathbf{R}) \]

so

\[ \text{cov}(\mathbf{y}_t) = \mathbf{Z} \text{cov}(\mathbf{x}_t) \mathbf{Z}^\top + \mathbf{R} \] In PCA, we require \(\mathbf{R}\) to be diagonal, but not so in DFA

\[ \mathbf{R} \stackrel{?}{=} \begin{bmatrix} \sigma & 0 & 0 & 0 \\ 0 & \sigma & 0 & 0 \\ 0 & 0 & \sigma & 0 \\ 0 & 0 & 0 & \sigma \end{bmatrix} ~\text{or}~~ \mathbf{R} \stackrel{?}{=} \begin{bmatrix} \sigma_1 & 0 & 0 & 0 \\ 0 & \sigma_2 & 0 & 0 \\ 0 & 0 & \sigma_3 & 0 \\ 0 & 0 & 0 & \sigma_4 \end{bmatrix} \]

\[ \mathbf{R} \stackrel{?}{=} \begin{bmatrix} \sigma & 0 & 0 & 0 \\ 0 & \sigma & 0 & 0 \\ 0 & 0 & \sigma & 0 \\ 0 & 0 & 0 & \sigma \end{bmatrix} ~\text{or}~~ \mathbf{R} \stackrel{?}{=} \begin{bmatrix} \sigma_1 & 0 & 0 & 0 \\ 0 & \sigma_2 & 0 & 0 \\ 0 & 0 & \sigma_3 & 0 \\ 0 & 0 & 0 & \sigma_4 \end{bmatrix} \]

\[ \mathbf{R} \stackrel{?}{=} \begin{bmatrix} \sigma & \gamma & \gamma & \gamma \\ \gamma & \sigma & \gamma & \gamma \\ \gamma & \gamma & \sigma & \gamma \\ \gamma & \gamma & \gamma & \sigma \end{bmatrix} ~\text{or}~~ \mathbf{R} \stackrel{?}{=} \begin{bmatrix} \sigma_1 & 0 & 0 & 0 \\ 0 & \sigma_2 & 0 & \gamma_{2,4} \\ 0 & 0 & \sigma_3 & 0 \\ 0 & \gamma_{2,4} & 0 & \sigma_4 \end{bmatrix} \]

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

What form should we use for \(\mathbf{Z}\)?

\[ \mathbf{Z} \stackrel{?}{=} \begin{bmatrix} z_1 \\ z_2 \\ z_3 \\ z_4 \\ z_5 \end{bmatrix} ~\text{or}~~ \mathbf{Z} \stackrel{?}{=} \begin{bmatrix} z_{1,1} & z_{2,1} \\ z_{1,2} & z_{2,2} \\ z_{1,3} & z_{2,3} \\ z_{1,4} & z_{2,4} \\ z_{1,5} & z_{2,5} \end{bmatrix} ~\text{or}~~ \mathbf{Z} \stackrel{?}{=} \begin{bmatrix} z_{1,1} & z_{2,1} & z_{3,1} \\ z_{1,2} & z_{2,2} & z_{3,2} \\ z_{1,3} & z_{2,3} & z_{3,3} \\ z_{1,4} & z_{2,4} & z_{3,4} \\ z_{1,5} & z_{2,5} & z_{3,5} \end{bmatrix} \]

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

What form should we use for \(\mathbf{Z}\)?

\[ \mathbf{Z} \stackrel{?}{=} \begin{bmatrix} z_1 \\ z_2 \\ z_3 \\ \vdots \\ z_5 \end{bmatrix} ~\text{or}~~ \mathbf{Z} \stackrel{?}{=} \begin{bmatrix} z_{1,1} & z_{2,1} \\ z_{1,2} & z_{2,2} \\ z_{1,3} & z_{2,3} \\ \vdots & \vdots \\ z_{1,n} & z_{2,n} \end{bmatrix} ~\text{or}~~ \mathbf{Z} \stackrel{?}{=} \begin{bmatrix} z_{1,1} & z_{2,1} & z_{3,1} \\ z_{1,2} & z_{2,2} & z_{3,2} \\ z_{1,3} & z_{2,3} & z_{3,3} \\ \vdots & \vdots & \vdots \\ z_{1,n} & z_{2,n} & z_{3,n} \end{bmatrix} \]

We’ll use model selection criteria to choose (eg, AICc)

Unless \(\mathbf{Z}\) is unconstrained in some manner, there are an infinite number of combinations of \(\mathbf{Z}\) and \(\mathbf{x}\) that will equal \(\mathbf{y}\)

Therefore we need to impose some constraints on the model

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{a} = \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ \vdots \\ a_n \end{bmatrix} \]

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{a} = \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ \vdots \\ a_n \end{bmatrix} \]

We will set the first \(m\) elements of \(\mathbf{a}\) to 0

For example, if \(n = 5\) and \(m = 2\)

\[ \mathbf{a} = \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ a_4 \\ a_5 \end{bmatrix} \Rightarrow \mathbf{a} = \begin{bmatrix} 0 \\ 0 \\ a_3 \\ a_4 \\ a_5 \end{bmatrix} \]

For example, if \(n = 5\) and \(m = 2\)

\[ \mathbf{a} = \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ a_4 \\ a_5 \end{bmatrix} \Rightarrow \mathbf{a} = \begin{bmatrix} 0 \\ 0 \\ a_3 \\ a_4 \\ a_5 \end{bmatrix} \Rightarrow \mathbf{a} = \begin{bmatrix} 0 \\ 0 \\ 0 \\ 0 \\ 0 \end{bmatrix} \]

Note, however, that this causes problems for the EM algorithm so we will often de-mean the data and set \(a_i = 0\) for all \(i\)

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{Z} = \begin{bmatrix} z_{1,1} & z_{2,1} & \dots & z_{m,1} \\ z_{1,2} & z_{2,2} & \dots & z_{m,2} \\ z_{1,3} & z_{2,3} & \dots & z_{m,3} \\ \vdots & \vdots & \ddots & z_{m,4} \\ z_{1,n} & z_{2,n} & \dots & z_{m,n} \end{bmatrix} \]

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{Z} = \begin{bmatrix} z_{1,1} & z_{2,1} & \dots & z_{m,1} \\ z_{1,2} & z_{2,2} & \dots & z_{m,2} \\ z_{1,3} & z_{2,3} & \dots & z_{m,3} \\ \vdots & \vdots & \ddots & z_{m,4} \\ z_{1,n} & z_{2,n} & \dots & z_{m,n} \end{bmatrix} \]

We will set the upper right triangle of \(\mathbf{Z}\) to 0

For example, if \(n = 5\) and \(m = 3\)

\[ \mathbf{Z} = \begin{bmatrix} z_{1,1} & 0 & 0 \\ z_{1,2} & z_{2,2} & 0 \\ z_{1,3} & z_{2,3} & z_{3,3} \\ z_{1,4} & z_{2,3} & z_{3,4} \\ z_{1,5} & z_{2,5} & z_{3,5} \end{bmatrix} \]

For the first \(m - 1\) rows of \(\mathbf{Z}\), \(z_{i,j} = 0\) if \(j > i\)

An additional constraint is necessary in a Bayesian context

\[ \mathbf{Z} = \begin{bmatrix} \underline{z_{1,1}} & 0 & 0 \\ z_{1,2} & \underline{z_{2,2}} & 0 \\ z_{1,3} & z_{2,3} & \underline{z_{3,3}} \\ z_{1,4} & z_{2,3} & z_{3,4} \\ z_{1,5} & z_{2,5} & z_{3,5} \end{bmatrix} \]

Diagonal of \(\mathbf{Z}\) is positive: \(z_{i,j} > 0\) if \(i = j\)

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{w}_t \sim \text{MVN}(\mathbf{0}, \mathbf{Q}) \]

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{w}_t \sim \text{MVN}(\mathbf{0}, \mathbf{Q}) \]

We will set \(\mathbf{Q}\) equal to the Identity matrix \(\mathbf{I}\)

For example, if \(m = 4\)

\[ \mathbf{Q} = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \]

This allows our random walks to have a lot of flexibility

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \underline{\mathbf{D} \mathbf{d}_t} + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\(\mathbf{d}_t\) is a \(p \times 1\) vector of covariates at time \(t\)

\(\mathbf{D}\) is an \(n \times p\) matrix of covariate effects

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \underline{\mathbf{D}} \mathbf{d}_t + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

Careful thought must be given a priori as to the form for \(\mathbf{D}\)

Should the effect(s) vary by site, species, etc?

For example, given 2 covariates, \(\text{Temp}\) and \(\text{Salinity}\)

\[ \mathbf{D} = \underbrace{ \begin{bmatrix} d_{\text{Temp}} & d_{\text{Salinity}} \\ d_{\text{Temp}} & d_{\text{Salinity}} \\ \vdots & \vdots \\ d_{\text{Temp}} & d_{\text{Salinity}} \\ \end{bmatrix} }_{\text{effects same by site/species}} ~~~ \text{or} ~~~ \mathbf{D} = \underbrace{ \begin{bmatrix} d_{\text{Temp}, 1} & d_{\text{Salinity}, 1} \\ d_{\text{Temp}, 2} & d_{\text{Salinity}, 2} \\ \vdots & \vdots \\ d_{\text{Temp}, n} & d_{\text{Salinity}, n} \\ \end{bmatrix} }_{\text{effects differ by site/species}} \]

Earlier we saw that we could use model selection criteria to help us choose among the different forms for \(\mathbf{Z}\)

However, caution must be given when comparing models with and without covariates, and varying numbers of states

Think about the DFA model form

\[ \mathbf{y}_t = \mathbf{Z} \underline{\mathbf{x}_t} + \mathbf{a} + \mathbf{D} \underline{\mathbf{d}_t} + \mathbf{v}_t \\ \]

\(\mathbf{x}_t\) are undetermined random walks

\(\mathbf{d}_t\) are predetermined covariates

Model 1 has 2 trends and no covariates

\[ \begin{bmatrix} y_1 \\ y_2 \\ y_3 \end{bmatrix}_t = \begin{bmatrix} z_{1,1} & z_{2,1} \\ z_{1,2} & z_{2,2} \\ z_{1,3} & z_{2,3} \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \end{bmatrix}_t + \begin{bmatrix} v_1 \\ v_2 \\ v_3 \end{bmatrix}_t \]

Model 2 has 1 trend and 1 covariate

\[ \begin{bmatrix} y_1 \\ y_2 \\ y_3 \end{bmatrix}_t = \begin{bmatrix} z_1 \\ z_2 \\ z_3 \end{bmatrix} \begin{bmatrix} x \end{bmatrix}_t + \begin{bmatrix} D_1 \\ D_2 \\ D_3 \end{bmatrix} \begin{bmatrix} d \end{bmatrix}_t + \begin{bmatrix} v_1 \\ v_2 \\ v_3 \end{bmatrix}_t \]

Model 1 has 2 trends and no covariates

\[ \begin{bmatrix} y_1 \\ y_2 \\ y_3 \end{bmatrix}_t = \begin{bmatrix} z_{1,1} & z_{2,1} \\ z_{1,2} & z_{2,2} \\ z_{1,3} & z_{2,3} \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \end{bmatrix}_t + \begin{bmatrix} v_1 \\ v_2 \\ v_3 \end{bmatrix}_t \]

Model 2 has 1 trend and 1 covariate

\[ \begin{bmatrix} y_1 \\ y_2 \\ y_3 \end{bmatrix}_t = \begin{bmatrix} z_1 \\ z_2 \\ z_3 \end{bmatrix} \begin{bmatrix} x \end{bmatrix}_t + \begin{bmatrix} D_1 \\ D_2 \\ D_3 \end{bmatrix} \begin{bmatrix} d \end{bmatrix}_t + \begin{bmatrix} v_1 \\ v_2 \\ v_3 \end{bmatrix}_t \]

Unless \(\mathbf{d}\) is highly correlated with \(\mathbf{y}\), Model 1 will be favored

• fit the most complex model you can envision based on all of your possible covariates and random factors (states)

• keep the covariates fixed and choose the number of trends (states) using AICc

• keep the covariates & states fixed and choose the form for \(\mathbf{R}\)

• sort out the covariates while keeping the states & \(\mathbf{R}\) fixed

Recall that we had to constrain the form of \(\mathbf{Z}\) to fit the model

\[ \mathbf{Z} = \begin{bmatrix} z_{1,1} & 0 & \dots & 0 \\ z_{1,2} & z_{2,2} & \ddots & 0 \\ \vdots & \vdots & \ddots & 0 \\ \vdots & \vdots & \vdots & z_{m,m} \\ \vdots & \vdots & \vdots & \vdots \\ z_{1,n} & z_{2,n} & z_{3,n} & z_{m,n} \end{bmatrix} \]

So, the 1st common factor is determined by the 1st variate, the 2nd common factor by the first two variates, etc.

To help with this, we can use a basis rotation to maximize the loadings on a few factors

If \(\mathbf{H}\) is an \(m \times m\) non-singular matrix, these 2 DFA models are equivalent

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{x}_t + \mathbf{a} + \mathbf{D} \mathbf{d}_t + \mathbf{v}_t \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \mathbf{w}_t \]

\[ \mathbf{y}_t = \mathbf{Z} \mathbf{H}^{-1} \mathbf{x}_t + \mathbf{a} + \mathbf{D} \mathbf{d}_t + \mathbf{v}_t \\ \mathbf{H} \mathbf{x}_t = \mathbf{H} \mathbf{x}_{t-1} + \mathbf{H} \mathbf{w}_t \]

How should we choose \(\mathbf{H}\)?

A varimax rotation will maximize the variance of the loadings in \(\mathbf{Z}\) along a few of the factors

Deterministic vs stochastic elements

Regression with autocorrelated errors

Regression with temporal random effects

Dynamic Factor Analysis (DFA)

• Forms of covariance matrix

• Constraints for model fitting

• Interpretation of results