Factor de inversión de patrones estacionales y de inversión estacional

Question

Estoy intentando crear una variable patrón que tome la media de un mismo mes (lag 12, 24... 240) de los últimos 20 años y la media de los demás meses lag (1-11, 13-23, 25-35... 229-239). (Reproduciendo este trabajo: ¿Las estacionalidades de los rendimientos se deben al riesgo o a una valoración errónea? por Matti Keloharju et al. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3276334 )

Debería empezar a calcularse cuando se disponga de al menos 5 años de datos. (60 meses de observaciones que no son na). <- se requieren al menos 5 años para empezar. Y puede utilizar hasta 20 años de datos pasados. Así que parece una ventana móvil que utiliza un mínimo de 60 meses y un máximo de 240 meses. La inversión hace lo contrario, toma la media de todos los demás meses que no sean el actual.

Estoy intentando crear un nuevo objeto xts llamado seasonal a partir de estos datos xts (dput) de abajo:

               299916 299918 299921     299922 299923     299925 299926      299927 299932
1926-01-15 0.00000000     NA     NA 0.00000000     NA 0.10714286     NA  0.03238868     NA
1926-02-15 0.02040818     NA     NA 0.00000000     NA 0.00000000     NA  0.00000000     NA
1926-03-15 0.00000000     NA     NA 0.00000000     NA 0.00000000     NA  0.00000000     NA
1926-04-15 0.00000000     NA     NA 0.00000000     NA 0.02990030     NA  0.00000000     NA
1926-05-15 0.00000000     NA     NA 0.00000000     NA 0.00000000     NA  0.00000000     NA
1926-06-15 0.00000000     NA     NA 0.02564103     NA 0.00000000     NA -0.03921569     NA
1926-07-15 0.00000000     NA     NA 0.00000000     NA 0.00000000     NA  0.03375532     NA
1926-08-15 0.02040820     NA     NA 0.00000000     NA 0.00000000     NA  0.00000000     NA
1926-09-15 0.00000000     NA     NA 0.00000000     NA 0.00000000     NA  0.00000000     NA
1926-10-15 0.00000000     NA     NA 0.00000000     NA 0.02990033     NA  0.00000000     NA
1926-11-15 0.00000000     NA     NA 0.00000000     NA 0.00000000     NA  0.00000000     NA
1926-12-15 0.00000000     NA     NA 0.05193955     NA 0.00000000     NA  0.00000000     NA

La función debe tomar la media de cada mes diferente de los últimos 240 meses, lo que requiere al menos 60 meses de datos. He probado a hacerlo así, y después a poner cada valor que no tenga 60 observaciones anteriores a NA

reversal <- rollapplyr(data, FUN= function(x) { i <- month(end(x)); xx <- x[month(time(x)) != i]; if (sum(!is.na(xx)) < 60) NA else mean(xx, na.rm = TRUE) },width=60)

¿Cómo podría cambiar esta función para hacer lo que quería o podría especificar cómo lo haría de otra manera, se puede hacer en xts o dataframe / tabla?

Un ejemplo representativo de mis datos se puede crear con el dput de abajo, contiene 254 meses para 9 series. Mi conjunto de datos completo contiene 20.000 series con 2.400 meses.

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Answer 1

Como ya escribió Chris en sus comentarios: su descripción no es completa. Pero yo sugeriría escribir un simple bucle sobre su matriz de datos. No es necesario necesidad de trabajar con zoo / xts mientras se realizan estos cálculos.

Utilizo su conjunto de datos de muestra y lo llamo data0 .

library("xts")
library("zoo")
time0 <- index(data0)
assets <- colnames(data0)
data0 <- coredata(data0)

Por su descripción, parece que trabaja con datos mensuales. Así que primero mapeo sus datos a una matriz de serie temporal espaciados regularmente en el tiempo data .

time <- seq(min(time0), max(time0), by = "1 month")
data <- array(NA_real_, dim = c(length(time), length(assets)))
data[match(time0, time), ] <- data0

Sus series no sólo tienen valores iniciales perdidos, sino también dentro de la serie. Para ver la fracción de valores perdidos en una determinada columna, digamos

apply(data, 2, function(x) sum(is.na(x))/length(x))
## [1] 0.8327 0.0778 1.0000 0.1051 0.3346 0.3152 0.4669 0.0623 1.0000

No está claro en su descripción cómo quiere manejar esos valores que faltan valores que faltan. Esto parece relevante, ya que hay series con ningún valor (otras tienen muchos valores cero; si éstas son especiales necesita manejar lo especial también). Para el código de ejemplo aquí, asumo que usted quiere requerir un número mínimo de valores no ausentes:

initial.skip <- 60
min.number.same <- 1
min.number.other <- 1
min.nonmissing.window <- 60  ## set to 0 to ignore

Le sugiero encarecidamente que juegue con estos parámetros (véase más abajo) y vea qué efectos tienen en el cálculo de las medias.

Los retrasos.

same.months <- seq(from = 12, to = 240, by = 12)
other.months <- 1:240
other.months <- setdiff(other.months, same.months)

Los resultados se almacenarán en dos matrices que tienen la misma dimensión que los datos.

same.means <- array(NA_real_, dim = dim(data))
other.means <- array(NA_real_, dim = dim(data))

Queda por llenar las matrices. Para ser más estrictos con NA valores, aumentar min.number.same y min.number.other . Para no permitir ningún valor perdido, establezca na.rm en los cálculos de la media a FALSE .

for (j in seq_along(assets)) {
    for (t in seq_along(time)) {

        if (t <= initial.skip || t <= min.nonmissing.window)
            next

        if (min.nonmissing.window > 0)
            if (any(is.na(data[t - seq_len(min.nonmissing.window), j])))
                next

        lags <- same.months
        lags <- lags[t > lags]
        tmp <- data[t - lags, j]

        if (sum(!is.na(tmp)) >= min.number.same)
            same.means[t, j] <- mean(tmp, na.rm = TRUE)

        lags <- other.months
        lags <- lags[t > lags]
        tmp <- data[t- lags, j]
        if (sum(!is.na(tmp)) >= min.number.other)
            other.means[t, j] <- mean(tmp, na.rm = TRUE)
    }
}

Si es necesario, puede volver a transformar estas matrices en zoo / xts .

---

Comprobación de la influencia de NA valores: los parámetros initial.skip , min.number.same , min.number.other y min.nonmissing.window determinar cuándo se intenta un cómputo (de lo contrario, la media se convierte en NA ). Para comprobar los efectos de estos parámetros, lo mejor es crear un conjunto de datos sencillo como el siguiente.

assets <- letters[1:2]
time <- 1:500
data <- array(c(time, time), dim = c(length(time), 2))
data[300, 1] <- NA
same.means <- array(NA_real_, dim = dim(data))
other.means <- array(NA_real_, dim = dim(data))

Ahora ejecuta de nuevo los bucles anidados y mira same.means y other.means .