Introduction to Time-Varying VAR and the Problem at Hand

In time series analysis, a vector autoregression (VAR) model is used to study the relationships between multiple time series. A time-varying VAR (TVVAR) model extends this idea by allowing the parameters of the model to change over time. In this article, we will delve into how to create two important objects in a TVVAR model: beepvar and dayvar. These objects represent the number of observations on a day and the days of observation, respectively.

What is Time-Varying VAR?

A TVVAR model is an extension of the traditional VAR model. In a traditional VAR model, the parameters are assumed to be constant over time. However, in many real-world applications, the relationships between variables can change over time due to various factors such as seasonal fluctuations, exogenous shocks, or changes in economic conditions.

The TVVAR model allows these changing relationships to be incorporated into the model. The parameters of the model are typically estimated using a combination of statistical techniques such as maximum likelihood estimation and cross-validation.

Creating beepvar and dayvar

To create beepvar and dayvar, we need to first understand what they represent. beepvar is the number of observations on a day, while dayvar represents the days of observation.

In the given R code, tvvar_obj is the TVVAR object created using the tvmvar() function. However, the parameters for beepvar and dayvar are not specified. To create these objects, we need to modify the code to extract the relevant information from the data.

Extracting beepvar

To create beepvar, we can use the time variable in the data frame. This variable represents the date and time of each observation. We can extract the day of the week by converting this date to a factor using the dayofweek() function from the lubridate package.

library(lubridate)
beepvar <- nrow(stress_data[, "I feel stressed"])
days_of_week <- as.factor(as.Date(stress_data$Survey Creation Date, "%m/%d/%Y"))
n_days <- length(days_of_week[!is.na(days_of_week)])

In this code, nrow(stress_data[, "I feel stressed"]) extracts the number of observations on a day. The days_of_week variable represents the days of the week in which observations were made. Finally, n_days calculates the total number of days.

Extracting dayvar

To create dayvar, we can use the time variable to extract the days of observation. We can convert this date to a numeric value using the ymd() function from the lubridate package.

dayvar <- length(unique(stress_data$Survey Creation Date))

In this code, length(unique(stress_data$Survey Creation Date)) extracts the unique days of observation.

Modifying the TVVAR Object

Now that we have created beepvar and dayvar, we can modify the TVVAR object to include these parameters. We will use the data.frame() function to create a new data frame with the extracted variables.

tvvar_data <- data.frame(
  type = rep("g", 12),
  level = rep(1, 12),
  lambdaSel = "CV",
  estpoints = seq(0, 1, length = 20),
  bandwidth = ### need bandwidth,
  lags = 1,
  beepvar = beepvar,
  dayvar = dayvar
)

In this code, tvvar_data is the new data frame with the extracted variables.

Conclusion

In conclusion, creating beepvar and dayvar involves extracting relevant information from the data using various functions. By modifying the TVVAR object to include these parameters, we can create a more accurate model that captures the changing relationships between variables over time.

Here is the complete code:

library(lubridate)
library(tvmvar)

# Load the data
stress_data <- read.csv("stress_data.csv")

# Extract beepvar and dayvar
beepvar <- nrow(stress_data[, "I feel stressed"])
days_of_week <- as.factor(as.Date(stress_data$Survey Creation Date, "%m/%d/%Y"))
n_days <- length(days_of_week[!is.na(days_of_week)])

dayvar <- length(unique(stress_data$Survey Creation Date))

# Create tvvar data
tvvar_data <- data.frame(
  type = rep("g", 12),
  level = rep(1, 12),
  lambdaSel = "CV",
  estpoints = seq(0, 1, length = 20),
  bandwidth = ### need bandwidth,
  lags = 1,
  beepvar = beepvar,
  dayvar = dayvar
)

# Create tvvar object
tvvar_obj <- tvmvar(data = stress_data,
                     type = rep("g", 12),
                     level = rep(1, 12),
                     lambdaSel = "CV",
                     timepoints = NULL,
                     estpoints = seq(0, 1, length = 20),
                     bandwidth = ### need bandwidth,
                     lags = 1,
                     beepvar = beepvar,
                     dayvar = dayvar,
                     scale = TRUE,
                     pbar = TRUE)

Note that you will need to replace ### need bandwidth with the actual bandwidth value.

Last modified on 2024-03-19