---
title: "Replicating Rossi (2006)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Replicating Rossi (2006)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 4
)
```

This article replicates the **out-of-sample Diebold-Mariano panel of Table 1** in Rossi (2006), *"Are exchange rates really random walks? Some evidence robust to parameter instability"* (*Macroeconomic Dynamics*, 10(1), 20-38), using `dm_test()` from **forecastdom** and the bundled `rossi2006` dataset.

The exercise is the classical Meese-Rogoff (1983) question: at the monthly horizon, can a small linear AR model of exchange-rate returns beat a driftless random walk out of sample?

```{r setup, message = FALSE}
library(forecastdom)
library(ggplot2)

data(rossi2006)
```

## The data

Five bilateral nominal exchange rates against the U.S. dollar (Canada, France, Germany, Italy, Japan), monthly from March 1973 to December 1998, 310 observations per country.

```{r plot-fx}
ggplot(rossi2006, aes(date, log(fx))) +
  geom_line(colour = "#47A5C5") +
  facet_wrap(~ country, scales = "free_y") +
  labs(x = NULL, y = "log(FX)",
       title = "Log nominal exchange rates vs. USD") +
  theme_minimal()
```

## Forecasting setup

Let $e_t$ denote the log exchange rate and $\Delta e_t = e_t - e_{t-1}$ its monthly return. We compare two one-step-ahead forecasts of $\Delta e_{t+1}$:

* **Benchmark** (driftless random walk): $\widehat{\Delta e}^{RW}_{t+1} = 0$.
* **Alternative** (AR($p$)): $\widehat{\Delta e}^{AR}_{t+1} = \hat\alpha_t + \sum_{k=1}^{p} \hat\beta_{k,t}\, \Delta e_{t-k+1}$, with coefficients re-estimated each period.

Following Rossi, AR(1) and AR(2) share the same usable sample of $n_{obs} = 307$ returns. The first $R = \lceil n_{obs}/2 \rceil = 154$ observations fit the initial model, and the remaining $P = 153$ are evaluated out of sample. Three estimation schemes are considered:

* **Split**: coefficients estimated once on $1{:}R$ and held fixed.
* **Recursive**: coefficients re-estimated each period using all available data.
* **Rolling**: coefficients re-estimated each period using the most recent $R$ observations.

```{r forecast-fn}
forecast_oos <- function(log_fx, p, scheme = c("split", "recursive", "rolling")) {

  scheme <- match.arg(scheme)

  T_full <- length(log_fx)
  dy <- diff(log_fx)
  Y  <- dy[3:(T_full - 1)]
  L1 <- dy[2:(T_full - 2)]
  L2 <- dy[1:(T_full - 3)]
  Xm <- if (p == 1) matrix(L1, ncol = 1) else cbind(L1, L2)

  n_obs <- length(Y)
  R     <- as.integer(ceiling(n_obs / 2))
  P_oos <- n_obs - R

  e_alt   <- numeric(P_oos)
  e_bench <- numeric(P_oos)

  for (j in seq_len(P_oos)) {

    idx <- switch(scheme,
      split     = seq_len(R),
      recursive = seq_len(R + j - 1),
      rolling   = j:(R + j - 1)
    )

    Z <- cbind(1, Xm[idx, , drop = FALSE])
    b <- as.numeric(solve(crossprod(Z), crossprod(Z, Y[idx])))
    pred <- as.numeric(c(1, Xm[R + j, ]) %*% b)

    e_alt[j]   <- Y[R + j] - pred
    e_bench[j] <- Y[R + j]

  }

  list(e_bench = e_bench, e_alt = e_alt, P = P_oos)

}
```

## Replicating the OOS DM panel of Table 1

We call `dm_test()` with `correction = FALSE` to match Rossi's asymptotic $\chi^2(1)$ reference distribution. Rossi defines the loss differential as $f_t = u^{AR}_t - u^{RW}_t$, so a *positive* DM statistic means the random walk has the lower MSFE. To reproduce her sign convention we pass the AR errors as `e1` and the random-walk errors as `e2`.

```{r run-table1}
countries <- levels(rossi2006$country)
schemes   <- c("split", "recursive", "rolling")

run_panel <- function(p) {

  out <- expand.grid(country = countries, scheme = schemes,
                     stringsAsFactors = FALSE)
  out$DM   <- NA_real_
  out$DM_p <- NA_real_

  for (i in seq_len(nrow(out))) {

    log_fx <- log(subset(rossi2006, country == out$country[i])$fx)
    fc     <- forecast_oos(log_fx, p = p, scheme = out$scheme[i])
    res    <- dm_test(fc$e_alt, fc$e_bench,
                      alternative = "two.sided", correction = FALSE)
    out$DM[i]   <- res$statistic
    out$DM_p[i] <- res$pvalue

  }

  out$cell <- sprintf("%.2f (%.2f)", out$DM, out$DM_p)
  wide <- reshape(out[, c("country", "scheme", "cell")],
                  idvar = "scheme", timevar = "country", direction = "wide")
  names(wide) <- gsub("^cell\\.", "", names(wide))

  wide

}
```

### AR(1)

```{r tab-ar1}
knitr::kable(run_panel(1), row.names = FALSE,
             caption = "$DM_T$ statistic ($p$-value), AR(1) vs. RW")
```

### AR(2)

```{r tab-ar2}
knitr::kable(run_panel(2), row.names = FALSE,
             caption = "$DM_T$ statistic ($p$-value), AR(2) vs. RW")
```

These cells reproduce the OOS DM panel of Rossi (2006) Table 1 exactly. The statistics are typically positive (the random walk has the lower MSFE), and the two-sided $p$-values rarely fall below 5%. This is the classical Meese-Rogoff finding.

## Single-country deep dive: Japan, AR(1), recursive

```{r japan}
log_fx_jp <- log(subset(rossi2006, country == "Japan")$fx)
fc_jp     <- forecast_oos(log_fx_jp, p = 1, scheme = "recursive")

dm_test(fc_jp$e_alt, fc_jp$e_bench, alternative = "two.sided", correction = FALSE)
```

The cumulative squared-error differential, using Rossi's sign convention so that positive values mean the *random walk* is winning, shows that the AR's edge is concentrated in narrow sub-samples rather than spread uniformly across the OOS period.

```{r cum-loss}
loss_diff <- fc_jp$e_alt^2 - fc_jp$e_bench^2
oos_dates <- tail(unique(rossi2006$date), fc_jp$P)

ggplot(data.frame(date = oos_dates, cum = cumsum(loss_diff)),
       aes(date, cum)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_line(colour = "#47A5C5", linewidth = 0.8) +
  labs(x = NULL,
       y = "Cumulative SE loss (AR(1) - RW)",
       title = "Cumulative squared-error loss differential, Japan",
       subtitle = "Above zero = RW doing better; below = AR(1) doing better") +
  theme_minimal()
```

## References

- Diebold, F. X. and Mariano, R. S. (1995). Comparing predictive
  accuracy. *Journal of Business & Economic Statistics*, 13(3), 253-263.
- Meese, R. A. and Rogoff, K. (1983). Empirical exchange rate models
  of the seventies: Do they fit out of sample? *Journal of
  International Economics*, 14(1-2), 3-24.
- Rossi, B. (2006). Are exchange rates really random walks? Some
  evidence robust to parameter instability. *Macroeconomic Dynamics*,
  10(1), 20-38.