_final_w24.qmd

---
title: "Time Series Homework: Final W24"
subtitle: "Please_put_your_name_here"
format: 
  html:
    embed-resources: true
    toc: true
---

```{r, echo=FALSE, message=FALSE, warning=FALSE}

# Loading R packages

if (!require("pacman")) install.packages("pacman")
pacman::p_load(tsibble, fable,feasts, tsibbledata,
               fable.prophet, patchwork,lubridate, 
               rio,ggplot2,kableExtra,tidyquant, 
               dplyr , plotly,tidymodels, stats,
               multilevelmod,nlme, broom.mixed,
               forecast
)
```

## Data

```{r, message=FALSE}

non_store_ts <- rio::import("data/retail_by_business_type.parquet") |>
  filter(naics == 454) |>
  as_tsibble(index = month)

term_prem10 <- rio::import("https://byuistats.github.io/timeseries/data/term_prem10.csv")


```

## Questions

### Question 1 -  Non- Store Retailers ( points)

Over the last 20 years, the part of the retail world that doesn't use traditional stores, classified under the NAICS code 454, has grown significantly. This includes online shopping, mail orders, vending machines, and home deliveries, etc. Among these, online shopping has become especially important, leading the growth in this sector. The series non-store_ts shows the sales in millions for this category starting in the early 90's. Your client is a start-up company entering the space, and needs a forecast of the category for the next 10 years. The audience will be primarily venture capital firms looking to invest in the start-up. However, your report will also serve as the main source of information for the structuring of the company's supply chain risk assessment and purchasing strategy. Assume that there is at least one person in your audience that will have similar technical expertise than you, but the rest of your audience has an MBA. Your report should contain a discussion of all the features of the series and analysis of the random component of the series in the context of uncertainty in business planning. Your analysis should use all the tools taught in the course that are appropriate for the setting. Please include details of model selection and a discussion of the strengths and limitations of your forecast.

::: {.callout-note title="Answer" icon="false"}
```{r}



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### Question 2 -  ( points)

The term premium refers to the extra yield that investors require to commit to holding a longer-term bond, such as a 10-year Treasury, instead of rolling over shorter-term securities over the same period. Essentially, it compensates investors for the additional risks associated with longer maturities, including interest rate risk, inflation risk, and the uncertainty over the future path of short-term interest rates. Analyzing the term premium provides valuable insights into investor expectations regarding future economic conditions, monetary policy actions, and other factors that influence the yield curve. For you, this exploration offers a practical application of time series analysis techniques to real-world economic and financial data. For this section, pretend you are a financial analyst who has applied to a job with a financial analytics firm. They have provided the term_prem10 series and your task is to explore the term premium's temporal structure. Your discussion should center around which stochastic-process model is the most appropriate for the realizations of the time series. Your audience is a potential employer who is trying to asses your understanding of time series models, such as autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA).


::: {.callout-note title="Answer" icon="false"}
```{r}



```
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:::