---
title: "Introduction to bvhar"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to bvhar}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  \newcommand{\R}{\mathbb{R}}
  \newcommand{\B}{\boldsymbol\beta}
  \newcommand{\hb}{\boldsymbol{\hat\beta}}
  \newcommand{\E}{\boldsymbol\epsilon}
  \DeclareMathOperator*{\argmin}{argmin}
  \DeclareMathOperator*{\argmax}{argmax}
  \newcommand{\X}{\mathbf{X}}
  \newcommand{\Y}{\mathbf{Y}}
  \newcommand{\by}{\mathbf{y}}
  \newcommand{\bz}{\mathbf{Z}}
  \newcommand{\ba}{\boldsymbol{\alpha}}
  \newcommand{\bc}{\mathbf{c}}
  \newcommand{\bu}{\mathbf{u}}
  \def\Cov{\mathrm{Cov}}
  \def\Var{\mathrm{Var}}
  \def\Corr{\mathrm{Corr}}
  \def\vec{\mathrm{vec}}
  \newcommand{\defn}{\mathpunct{:}=}
---

```{r rmdsetup, include = FALSE}
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  out.width = "70%",
  fig.align = "center",
  fig.width = 6,
  fig.asp = .618
  )
orig_opts <- options("digits")
options(digits = 3)
```

```{r setup}
library(bvhar)
```

# Data

Looking at VAR and VHAR, you can learn how the models work and how to perform this package.

## ETF Dataset

This package includes some datasets.
Among them, we try CBOE ETF volatility index (`etf_vix`).
Since this is just an example, we arbitrarily extract a small number of variables: *Gold, crude oil, euro currency, and china ETF*.

```{r etfdat}
var_idx <- c("GVZCLS", "OVXCLS", "EVZCLS", "VXFXICLS")
etf <- 
  etf_vix |> 
  dplyr::select(dplyr::all_of(var_idx))
etf
```

## h-step ahead forecasting

For evaluation, split the data.
The last `19` observations will be test set.
`divide_ts()` function splits the time series into train-test set.

In the other vignette, we provide how to perform out-of-sample forecasting.

```{r hstepsplit}
h <- 19
etf_eval <- divide_ts(etf, h) # Try ?divide_ts
etf_train <- etf_eval$train # train
etf_test <- etf_eval$test # test
# dimension---------
m <- ncol(etf)
```

- T: Total number of observation
- p: VAR lag
- m: Dimension of variable
- s = T - p
- k = m * p + 1 if constant term, k = m * p without constant term

# Models

## VAR

This package indentifies VAR(p) model by

$$\Y_t = \bc + \B_1 \Y_{t - 1} + \ldots + \B_p +\Y_{t - p} + \E_t$$

where $\E_t \sim N(\mathbf{0}_k, \Sigma_e)$

```{r varlag}
var_lag <- 5
```

The package perform VAR(p = `r var_lag`) based on

$$Y_0 = X_0 A + Z$$

where

$$
Y_0 = \begin{bmatrix}
  \by_{p + 1}^T \\
  \by_{p + 2}^T \\
  \vdots \\
  \by_n^T
\end{bmatrix}_{s \times m} \equiv Y_{p + 1} \in \R^{s \times m}
$$

by `build_y0()`

and

$$
X_0 = \left[\begin{array}{c|c|c|c}
  \by_p^T & \cdots & \by_1^T & 1 \\
  \by_{p + 1}^T & \cdots & \by_2^T & 1 \\
  \vdots & \vdots & \cdots & \vdots \\
  \by_{T - 1}^T & \cdots & \by_{T - p}^T & 1
\end{array}\right]_{s \times k} = \begin{bmatrix}
  Y_p & Y_{p - 1} & \cdots & \mathbf{1}_{T - p}
\end{bmatrix} \in \R^{s \times k}
$$

by `build_design()`. Coefficient matrix is the form of

$$
A = \begin{bmatrix}
  A_1^T \\
  \vdots \\
  A_p^T \\
  \bc^T
\end{bmatrix} \in \R^{k \times m}
$$

This form also corresponds to the other model.
Use `var_lm(y, p)` to model VAR(p).
You can specify `type = "none"` to get model without constant term.

```{r varfit}
(fit_var <- var_lm(etf_train, var_lag))
```

The package provide `S3` object.

```{r varlist}
# class---------------
class(fit_var)
# inheritance---------
is.varlse(fit_var)
# names---------------
names(fit_var)
```

## VHAR

Consider Vector HAR (VHAR) model.

$$\Y_t = \bc + \Phi^{(d)} + \Y_{t - 1} + \Phi^{(w)} \Y_{t - 1}^{(w)} + \Phi^{(m)} \Y_{t - 1}^{(m)} + \E_t$$

where $\Y_t$ is daily RV and

$$\Y_t^{(w)} = \frac{1}{5} \left( \Y_t + \cdots + \Y_{t - 4} \right)$$

is weekly RV

and

$$\Y_t^{(m)} = \frac{1}{22} \left( \Y_t + \cdots + \Y_{t - 21} \right)$$

is monthly RV. This model can be expressed by

$$Y_0 = X_1 \Phi + Z$$

where

$$
\Phi = \begin{bmatrix}
  \Phi^{(d)T} \\
  \Phi^{(w)T} \\
  \Phi^{(m)T} \\
  \bc^T
\end{bmatrix} \in \R^{(3m + 1) \times m}
$$

Let $T$ be

$$
\mathbb{C}_0 \defn \begin{bmatrix}
  1 & 0 & \cdots & 0 & 0 & \cdots & 0 \\
  1 / 5 & 1 / 5 & \cdots & 1 / 5 & 0 & \cdots & 0 \\
  1 / 22 & 1 / 22 & \cdots & 1 / 22 & 1 / 22 & \cdots & 1 / 22
\end{bmatrix} \otimes I_m \in \R^{3m \times 22m}
$$

and let $\mathbb{C}_{HAR}$ be

$$
\mathbb{C}_{HAR} \defn \left[\begin{array}{c|c}
  T & \mathbf{0}_{3m} \\ \hline
  \mathbf{0}_{3m}^T & 1
\end{array}\right] \in \R^{(3m + 1) \times (22m + 1)}
$$

Then for $X_0$ in VAR(p),

$$
X_1 = X_0 \mathbb{C}_{HAR}^T = \begin{bmatrix}
  \by_{22}^T & \by_{22}^{(w)T} & \by_{22}^{(m)T} & 1 \\
  \by_{23}^T & \by_{23}^{(w)T} & \by_{23}^{(m)T} & 1 \\
  \vdots & \vdots & \vdots & \vdots \\
  \by_{T - 1}^T & \by_{T - 1}^{(w)T} & \by_{T - 1}^{(m)T} & 1
\end{bmatrix} \in \R^{s \times (3m + 1)}
$$

This package fits VHAR by scaling VAR(p) using $\mathbb{C}_{HAR}$ (`scale_har(m, week = 5, month = 22)`).
Use `vhar_lm(y)` to fit VHAR.
You can specify `type = "none"` to get model without constant term.

```{r harfit}
(fit_har <- vhar_lm(etf_train))
```

```{r harlist}
# class----------------
class(fit_har)
# inheritance----------
is.varlse(fit_har)
is.vharlse(fit_har)
# complements----------
names(fit_har)
```

## BVAR

This page provides deprecated two functions examples.
Both `bvar_minnesota()` and `bvar_flat()` will be integrated into `var_bayes()` and removed in the next version.

### Minnesota prior

- Litterman (1986) and Bańbura et al. (2010)
- All the equations are centered around the random walk with drift.
- *Prior mean*: Recent lags provide more reliable information than the more distant ones.
- *Prior variance*: Own lags explain more of the variation of a given variable than the lags of other variables in the equation.

First specify the prior using `set_bvar(sigma, lambda, delta, eps = 1e-04)`.

```{r minnesotaset}
bvar_lag <- 5
sig <- apply(etf_train, 2, sd) # sigma vector
lam <- .2 # lambda
delta <- rep(0, m) # delta vector (0 vector since RV stationary)
eps <- 1e-04 # very small number
(bvar_spec <- set_bvar(sig, lam, delta, eps))
```

In turn, `bvar_minnesota(y, p, bayes_spec, include_mean = TRUE)` fits BVAR(p).

- `y`: Multivariate time series data. It should be data frame or matrix, which means that every column is numeric. Each column indicates variable, i.e. it sould be wide format.
- `p`: Order of BVAR
- `bayes_spec`: Output of `set_bvar()`
- `include_mean = TRUE`: By default, you include the constant term in the model.

```{r bvarfit}
(fit_bvar <- bvar_minnesota(etf_train, bvar_lag, num_iter = 10, bayes_spec = bvar_spec))
```

It is `bvarmn` class. For Bayes computation, it also has other class such as `normaliw` and `bvharmod`.

```{r bvarlist}
# class---------------
class(fit_bvar)
# inheritance---------
is.bvarmn(fit_bvar)
# names---------------
names(fit_bvar)
```

### Flat prior

Ghosh et al. (2018) provides flat prior for covariance matrix, i.e. non-informative.
Use `set_bvar_flat(U)`.

```{r flatspec}
(flat_spec <- set_bvar_flat(U = 5000 * diag(m * bvar_lag + 1))) # c * I
```

Then `bvar_flat(y, p, bayes_spec, include_mean = TRUE)`:

```{r flatfit}
(fit_ghosh <- bvar_flat(etf_train, bvar_lag, num_iter = 10, bayes_spec = flat_spec))
```

```{r flatlist}
# class---------------
class(fit_ghosh)
# inheritance---------
is.bvarflat(fit_ghosh)
# names---------------
names(fit_ghosh)
```

## BVHAR

Consider the VAR(22) form of VHAR.

$$
\begin{aligned}
  \Y_t = \bc & + \left( \Phi^{(d)} + \frac{1}{5} \Phi^{(w)} + \frac{1}{22} \Phi^{(m)} \right) \Y_{t - 1} \\
  & + \left( \frac{1}{5} \Phi^{(w)} + \frac{1}{22} \Phi^{(m)} \right) \Y_{t - 2} + \cdots \left( \frac{1}{5} \Phi^{(w)} + \frac{1}{22} \Phi^{(m)} \right) \Y_{t - 5} \\
  & + \frac{1}{22} \Phi^{(m)} \Y_{t - 6} + \cdots + \frac{1}{22} \Phi^{(m)} \Y_{t - 22}
\end{aligned}
$$

What does Minnesota prior mean in VHAR model?

- All the equations are centered around $\Y_t + \bc + \Phi^{(d)} \Y_{t - 1} + \E_t$
- RW form: shrink diagonal elements of $\Phi^{(d)}$ toward one
    - $\Phi^{(w)}$ and $\Phi^{(m)}$ to zero
- WN form: $\delta_i = 0$

For more simplicity, write coefficient matrices by $\Phi^{(1)}, \Phi^{(2)}, \Phi^{(3)}$.
If we apply the prior in the same way, Minnesota moment becomes

$$
E \left[ (\Phi^{(l)})_{ij} \right] = \begin{cases}
  \delta_i & j = i, \; l = 1 \\
  0 & o/w
\end{cases} \quad \Var \left[ (\Phi^{(l)})_{ij} \right] = \begin{cases}
  \frac{\lambda^2}{l^2} & j = i \\
  \nu \frac{\lambda^2}{l^2} \frac{\sigma_i^2}{\sigma_j^2} & o/w
\end{cases}
$$

We call this VAR-type Minnesota prior or BVHAR-S.

### BVHAR-S

`set_bvhar(sigma, lambda, delta, eps = 1e-04)` specifies VAR-type Minnesota prior.

```{r bvharvarspec}
(bvhar_spec_v1 <- set_bvhar(sig, lam, delta, eps))
```

`bvhar_minnesota(y, har = c(5, 22), bayes_spec, include_mean = TRUE)` can fit BVHAR with this prior.
This is the default prior setting.
Similar to above functions, this function will be also integrated into `vhar_bayes()` and removed in the next version.

```{r}
(fit_bvhar_v1 <- bvhar_minnesota(etf_train, num_iter = 10, bayes_spec = bvhar_spec_v1))
```

This model is `bvharmn` class.

```{r bvharlist}
# class---------------
class(fit_bvhar_v1)
# inheritance---------
is.bvharmn(fit_bvhar_v1)
# names---------------
names(fit_bvhar_v1)
```

### BVHAR-L

Set $\delta_i$ for weekly and monthly coefficient matrices in above Minnesota moments:

$$
E \left[ (\Phi^{(l)})_{ij} \right] = \begin{cases}
  d_i & j = i, \; l = 1 \\
  w_i & j = i, \; l = 2 \\
  m_i & j = i, \; l = 3
\end{cases}
$$

i.e. instead of one `delta` vector, set three vector

- `daily`
- `weekly`
- `monthly`

This is called VHAR-type Minnesota prior or BVHAR-L.

`set_weight_bvhar(sigma, lambda, eps, daily, weekly, monthly)` defines BVHAR-L.

```{r}
daily <- rep(.1, m)
weekly <- rep(.1, m)
monthly <- rep(.1, m)
(bvhar_spec_v2 <- set_weight_bvhar(sig, lam, eps, daily, weekly, monthly))
```

`bayes_spec` option of `bvhar_minnesota()` gets this value, so you can use this prior intuitively.

```{r}
fit_bvhar_v2 <- bvhar_minnesota(
  etf_train,
  num_iter = 10,
  bayes_spec = bvhar_spec_v2
)
fit_bvhar_v2
```


```{r resetopts, include=FALSE}
options(orig_opts)
```

