Datasets

To use this package, the time series object with missing values should be coercible to either a numeric vector or numeric matrix (e.g., zoo or xts) with missing values denoted by NA. For convenience, the package contains two time series objects with missing values:

• ts_AR1_Gaussian is a list containing a time series with missing values y_missing generated from an AR(1) model with Gaussian distributed innovations, and the parameters of the model phi0, phi1, and sigma2;
• ts_AR1_t is a list containing a time series with missing values y_missing generated from an AR(1) model with Student’s \(t\) distributed innovations, and the parameters of the model phi0, phi1, sigma2, and nu.

library(imputeFin)
data(ts_AR1_Gaussian)
data(ts_AR1_t)
names(ts_AR1_t)
#> [1] "y_missing" "phi0"      "phi1"      "sigma2"    "nu"

Fitting a Gaussian AR(1) model

We start with the function fit_AR1_Gaussian() to fit a univariate Gaussian AR(1) model and estimate the parameters:

y_missing <- ts_AR1_Gaussian$y_missing[, 2]
fitted <- fit_AR1_Gaussian(y_missing)
fitted
#> $phi0
#> [1] 1.063603
#> 
#> $phi1
#> [1] 0.4674481
#> 
#> $sigma2
#> [1] 0.009669752

If instead we want to fit a random walk model, which means that phi1 = 1, then we can set the argument random_walk = TRUE (similarly, if we want to force a zero mean, then we can set zero_mean = TRUE):

fitted <- fit_AR1_Gaussian(y_missing, random_walk = TRUE)
fitted
#> $phi0
#> [1] 0.001318797
#> 
#> $phi1
#> [1] 1
#> 
#> $sigma2
#> [1] 0.01263339

For multivariate time series, the function fit_AR1_Gaussian() can still be used but it simply works on each univariate time series individually (thus no multivariate fitting, just univariate fitting). In the following example, the object y_missing contains three different time series named ‘a’, ‘b’, and ‘c’. The function fit_AR1_Gaussian() fits each time series separately and the returned value is a list consisting of the estimation results for each time series and additional elements that combine the estimated values in a convenient vector form:

y_missing <- ts_AR1_Gaussian$y_missing
fitted <- fit_AR1_Gaussian(y_missing)
names(fitted)
#> [1] "a"          "b"          "c"          "phi0_vct"   "phi1_vct"  
#> [6] "sigma2_vct"
fitted$a
#> $phi0
#> [1] 1.034113
#> 
#> $phi1
#> [1] 0.4815927
#> 
#> $sigma2
#> [1] 0.009586921
fitted$phi0_vct
#>        a        b        c 
#> 1.034113 1.063603 1.077366

Fitting a Student’s t AR(1) model

The function fit_AR1_t() works similarly to fit_AR1_Gaussian() but assuming that the residuals follow a Student’s \(t\) distribution:

y_missing <- ts_AR1_t$y_missing[, 2]
fitted <- fit_AR1_t(y_missing)
fitted
#> $phi0
#> [1] 0.04725166
#> 
#> $phi1
#> [1] 0.9827364
#> 
#> $sigma2
#> [1] 0.007565987
#> 
#> $nu
#> [1] 1.764065

It is important to note the argument fast_and_heuristic, which indicates whether a heuristic but fast method is to be used to estimate the parameters (by default, it is TRUE).

Imputation of missing values from Gaussian AR(1) model

We now show how to use the function impute_AR1_Gaussian() to impute the missing values in the time series based on the Gaussian AR(1) model, and how to conveniently plot the imputed time series with the function plot_imputed():

y_missing <- ts_AR1_Gaussian$y_missing[, 1]
y_imputed <- impute_AR1_Gaussian(y_missing)
plot_imputed(y_imputed)

The function impute_AR1_Gaussian() first fits the Gaussian AR(1) model to the incomplete time series data with missing values, and then imputes the missing values by drawing samples from the conditional distribution of the missing values given the observed data based on the estimated Gaussian AR(1) model. By default, the number of imputations is 1 (n_samples = 1), and the function impute_AR1_Gaussian() returns an imputed time series of the same class and dimensions as the input data but with one new attribute recording the locations of the missing values (the function plot_imputed() makes use of such information to indicate the imputed values).

If multiple imputations are desired, simply set the argument n_samples to the desired number. Then the function will return a list consisting of each imputed time series:

res <- impute_AR1_Gaussian(y_missing, n_samples = 3)
names(res)
#> [1] "y_imputed.1" "y_imputed.2" "y_imputed.3"

In addition to the imputed time series, the function can return the estimated parameters of the model by setting the argument return_estimates = TRUE (by default, it is FALSE):

res <- impute_AR1_Gaussian(y_missing, n_samples = 3, return_estimates = TRUE)
names(res)
#> [1] "y_imputed.1" "y_imputed.2" "y_imputed.3" "phi0"        "phi1"       
#> [6] "sigma2"

Imputation of missing values from Student’s t AR(1) model

The function impute_AR1_t() works similarly to impute_AR1_Gaussian() but assuming that the residuals follow a Student’s \(t\) distribution:

y_missing <- ts_AR1_t$y_missing[, 1]
res <- impute_AR1_t(y_missing, n_samples = 3, return_estimates = TRUE)
names(res)
#> [1] "y_imputed.1" "y_imputed.2" "y_imputed.3" "phi0"        "phi1"       
#> [6] "sigma2"      "nu"
y_imputed1 <- res$y_imputed.1
plot_imputed(y_imputed1)

Parameter estimation

The parameter estimation for the AR(1) models with Gaussian and Student’s \(t\) distributed innovations are based on the maximum likelihood estimation (MLE) given the observed data. Suppose we have a univariate time series \(y_{1}\), \(y_{2}\),\(\ldots\), \(y_{T}\) from the Gaussian AR(\(1\)) model \[ \begin{equation} y_{t}=\varphi_{0}+\varphi_{1}y_{t-1}+\varepsilon_{t},\label{eq:ar(1) model} \end{equation} \] where \(\varepsilon_{t}\overset{i.i.d.}{\sim}\mathcal{N}\left(0,\sigma^{2}\right)\). Some values are missing during the collection, and we denote the missing values by \(\mathbf{y}_{\mathsf{miss}}\). Then MLE problem for the parameters of the Gaussian AR(1) model takes the form:

\[ \begin{equation} \begin{aligned}\mathsf{\underset{\varphi_{0},\varphi_{1},\sigma^{2}}{maximize}} & \thinspace\thinspace\thinspace\log\left(\int\prod_{t=2}^{T}f_{G}\left(y_{t};\varphi_{0}+\varphi_{1}y_{t-1},\sigma^{2}\right)\mathsf{d}\mathbf{y}_{\mathsf{miss}}\right),\end{aligned} \end{equation} \] where \(f_{G}\left(\cdot\right)\) denotes the probability density function (pdf) of a Gaussian distribution.

For Student’s \(t\) AR(1) model with \(\varepsilon_{t}\overset{i.i.d.}{\sim}t\left(0,\sigma^{2},\nu\right)\), the MLE problem for the parameters takes the form: \[ \begin{equation} \begin{aligned}\mathsf{\underset{\varphi_{0},\varphi_{1},\sigma^{2},\nu>0}{maximize}} & \thinspace\thinspace\thinspace\log\left(\int\prod_{t=2}^{T}f_{t}\left(y_{t};\varphi_{0}+\varphi_{1}y_{t-1},\sigma^{2},\nu\right)\mathsf{d}\mathbf{y}_{\mathsf{miss}}\right),\end{aligned} \label{eq:problem formulation-2} \end{equation} \] where \(f_{t}\left(\cdot\right)\) denotes the probability density function (pdf) of a Gaussian distribution.

The objective functions in the above optimization problems are very complicated, and there are no closed-form solutions for them. Thus, it is necessary to resort to the expectation-maximization (EM) framework to derive efficient iterative algorithms to solve these MLE problems. An EM agorithm was developed to estimate the parameters for the Gaussian case [1]. The stochastic version of the EM algorithm has been derived to deal with the Student’s \(t\) case [2].

Imputation

Given the conditional distribution \(p\left(\mathbf{y}_{\mathsf{miss}}|\mathbf{y}_{\mathsf{obs}}\right)\) with \(\mathbf{y}_{\mathsf{obs}}\) being the observed values, it is trivial to impute the missing values by randomly drawing realizations from \(p\left(\mathbf{y}_{\mathsf{miss}}|\mathbf{y}_{\mathsf{obs}}\right)\). However, in our case, we do not have the conditional distribution \(p\left(\mathbf{y}_{\mathsf{miss}}|\mathbf{y}_{\mathsf{obs}}\right)\) in closed form. An improper way of imputing (which is acceptable in many cases with small percentage of missing values) is with \(p\left(\mathbf{y}_{\mathsf{miss}}|\mathbf{y}_{\mathsf{obs}},\boldsymbol{\theta}^{\mathsf{ML}}\right)\), where \(\boldsymbol{\theta}^{\mathsf{ML}}\) is the MLE result for the model parameter. Due to the complexity of the conditional distribution \(p\left(\mathbf{y}_{\mathsf{miss}}|\mathbf{y}_{\mathsf{obs}},\boldsymbol{\theta}^{\mathsf{ML}}\right)\), we cannot sample from it direcly, and a Gibbs sampling scheme is designed to draw realizations.