BEEM012代写、代写R程序语言

日期：2023-03-24 09:31

BEEM012 – Empirical Assignment Brief

Julian Dyer

Assignment Overview

The goal of this assignment is to use the tools you have learned so far in your R

assignments and apply them to an independent project on time series data of your

choice. I will be providing a few sample datasets that are easy for you to use from

which you can choose which one relates to a research question you find interesting.

You cannot use exactly the same data as I use as an example in

tutorials! I will primarily be using UK quarterly GDP growth as my Yt

variable and UK quarterly unemployment as my Xt.

Remember that you can always subtract one time series from another if

you are interested in the difference between two outcomes. For example,

we considered the term spread, the difference between long and short run

interest rates, in some of our R assignments as a predictor of GDP growth.

You can also use this as an outcome, and look at the difference between

profits in two different sectors as your Yt or Xt or differences in outcomes for

men and women as your Yt or Xt, etc.

You are welcome to seek out your own data and explore an independent

research project if you wish to go above and beyond the assignment. You will,

however, need to complete the same analysis tasks listed in the assignment.

The grading scheme will be consistent for those using data I provide and for

those who find their own.

If you want to use this empirical work as the basis for your dissertation that

would be an excellent use of your effort. You should be aware, however, that

you cannot submit the exact same report for your dissertation as you submit

for this module, and your dissertation would need to contain substantively

additional content.

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The first task is choosing an outcome variable that will be your Yt for your

analysis, and a primary Xt that will be the main explanatory variable you explore.

Once you have chosen some data of interest, the first part of this assignment

will involve using the tools we learned in the first part of the module (up to our

work with Dynamic Causal Effects) in order to explore what we can learn about

your outcome Yt as an Autoregressive process. You will complete the analytical

tasks outlined below by adapting the code provided in R tutorials and write up

an explanation of the task and the results. You will also use the tools of Volatility

Analysis we will cover later in the course to test whether the volatility or variance

of a time series is serially correlated.

The next step is to consider an additional explanatory variable, and estimate

the Dynamic Causal Effects of this explanatory variable on your outcome of inter-

est. You will complete the analytical tasks outlined below by adapting the code

provided in R tutorials and write up an explanation of the task and the results.

Where we have learned a manual tool to complete a task, you should

use this in your assignment. You are, however, free to use the automatic tools

to check your work.

You will then test two variables for Cointegration, in a formal test of whether

they move together and receive the same shocks. This can be the same as the

variables you have used previously, but you can also choose different variables.

Finally, you will estimate a model testing for Volatility Clustering in your time

series Yt using the Autoregressive Conditional Heteroskedasticity (ARCH) and

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models.

Grading Criteria

Your assignment will be assigned a grade as follows:

(Weight: 50%) Interpretation and Understanding of Econometric

Tools Part of your grade will be based on whether you correctly use and

interpret the tools of Time Series Econometrics that we learned. This means

that you use the appropriate models for the given task, that you interpret

results correctly, using the proper critical values for inference as well as

interpreting null hypotheses correctly. This also depends on whether you

explain why you use different tools, and the problems these are selected to

deal with.

(Weight: 25%) Programming and R Code Part of your grade will

depend on correctly using R to implement the tasks you are assigned and

whether your R code correctly implements the work that you describe in the

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write-up of your assignmnet. Marks will be given for R code that is correct,

and with comments to clarify you understand the tools you are using.

(Weight: 25%) Economic Analysis and Discussion This part of your

grade will depend on the economic analysis of your results and the depth

of your discussion. Marks will be given for the economic content of your

analysis and your interpretation of the economic reasoning of your results.

Assignment Outputs to Submit

A PDF write-up of the results of your analysis, including graphs and tables.

See the outline of the analysis tasks to complete below for details on exactly

what tables & graphs you need to complete. Include your R code at the

end of the document. You can write this document in Word or you can use

LATEX. There are additional videos on the assignment page about exporting

tables and using LATEX.

Word Count: Maximum 2,500 words, excluding R code, tables and

figures.

1 Analysis Tasks to Complete

1.1 Descriptive Analysis – Week 1 Exercises

Before running regressions, we will first examine our data and use some simple

tools to look at the time series.

1.1.1 Data Description

First, write a very brief (just a few sentences) description of the outcome variable

you are interested in analysing. Next write a brief description of your primary

explanatory variable, and the rough research question.

1.1.2 Time Series Plots

Next, plot your Yt time series., and give a few sentences of description. Do there

appear to be significant outliers in this time series? Either exclude them if they

are near the beginning or end of your time series, and if not, make note of this

outlier and make sure to take it into account in your analysis.

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1.2 Autoregression Analysis of a Time Series

1.2.1 Estimate an Autoregression Model

First, run an AR(1) regression of your outcome variable. Then use the Bayes

Information Criterion to select the appropriate lag length for your model,

setting a maximum of four lags. Write down the four values of the BIC(p)

you calculate, and explain which model length you end up selecting. Now,

estimate this model. (See Week 1 & 2 Exercises)

Next, test for violations of our key Time Series Assumptions: (See Week 3

Exercises)

– Use the appropriate model to test for a unit root process. Does economic

theory suggest that your time series should exhibit a roughly linear time

trend? Justify your answer briefly, and explain what this means for the

model you use for this test and the hypotheses you test. Write a brief

explanation of the result, and what this means for your time series. If

you conclude your time series has a unit root, perform the necessary

transformation, use the BIC again on your transformed time series and

add this revised AR(p) model to your table.

– Use the appropriate test for a break in your time series where you don’t

know the exact date of the break. Write a brief explanation of the

result, and what this means for your time series. If you conclude your

time series has a break and you identify the likely break date, make the

necessary adjustment to your model and add this model to your table.

Report estimated coefficients from both the AR(1) and AR(p) models in a

table, along with the coefficients from your modified model in the case that

your time series either has a break or a unit root.

Is the coefficient on Yt?1 in your AR(1) significant? Write a brief explanation

of whether it is statistically significant, and an additional brief interpreta-

tion of the economics of this result. How about the coefficient Yt?1 in your

AR(p) model - is it similar? Discuss the implication of these results, and the

persistence of shocks. If you correct for a trend or a break, discuss how your

analysis of the non-transformed time series might be misleading.

1.2.2 Estimate an Autoregressive Distributed Lag Model - Week 2

Exercises

Now we are going to introduce a second variable Xt. First, estimate an

ADL(1,1) model. If you found your Yt had a unit root above, then continue

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to use the transformed data in this section.

Repeat the exercise you conducted above using the BIC to select the length

of lag, but now you will select a lag p to use for your ADL(p,p) model. For

simplicity, consider again up to p = 4 and use the same lag length for Yt and

Xt.

Use a Granger causality test to test whether the lags of your explanatory

variable Xt are jointly significant predictors of Yt. Report the test statistic

in the text (no need to add it to a table).

Produce a table with your coefficient estimates from the ADL(1,1) model as

well as the ADL(p,p) model.

Interpret the results from the above. Are the lags of Xt jointly predictive

of Yt in a model where we also include lags of Yt? Discuss the economic

significance of this result.

1.2.3 Check Out-Of-Sample Forecast Performance – Week 4 Exercises

Using the Pseudo Out-Of-Sample forecasting method, with your ADL(1,1)

model and with the final 25% of your sample as your excluded sample, and

compare the within-sample SER (from the regression including none of your

excluded observations) and the out-of sample fit using your estimate of the

Root Mean Squared Forecast Error.

Compare the size of the SER to the size of your RMSFE. Which is larger?

Does this suggest your forecast errors are larger, smaller, or the same as your

within-sample errors? Is your model capable of predicting out-of-sample?

1.3 Dynamic Causal Effects – Week 5 Exercises

Use GLS to estimate the dynamic multipliers for a distributed lag model

regressing Yt on Xt and lags. For simplicity, use a Distributed Lag model

where r = 3, which means you will include Xt as well as the lag Xt?1 and

Xt?2, and an AR(1) error term, meaning that you model the error term just

as in lecture using φ1. Now, estimate these dynamic multipliers using the

Cohcrane-Orcutt method (not the Iterated Cochrane-Orcutt!).

Discuss the results above, beginning with a short discussion of whether it is

reasonable to assume strict exogeneity or exogeneity and give an example of

something that would mean we can only assume exogeneity but not strict

exogeneity. For example, in lecture we considered crop prices as our outcome

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Yt and climate shocks as our Xt. If people potentially stockpile crops today

based on anticipated climate shocks tomorrow then this would violate strict

exogeneity. Give an example of the issues with assuming strict exogeneity

in your setting. The important thing here is showing you understand the

conditions, so you can use a slightly unrealistic example here, as long as

you show you understand how to think of the exogeneity conditions in your

context. Next, discuss the implications of the dynamic multipliers you esti-

mate. Which dynamic multiplier of Xt is strongest? Does the effect increase,

decrease, or stay the same over time?

1.4 Multiperiod Forecasts - Week 7 Exercises

Using both methods that we have learned for multiperiod forecasting, forecast the

next ten periods of your time series past the end of your data using your ADL(p,p)

model above. What is the forecasted value in ten periods time? How do the two

forecasts differ?

1.5 Cointegration – Week 8 Exercises

Use the two-stage test for cointegration to test if your Yt and Xt are cointe-

grated. You can use the same Xt as above, or you can choose a different Xt

if you think they are more likely to be cointegrated and, therefore, a more

interesting exercise. (Note: even if it isn’t sensible to test for cointegration

here, conduct the test anyways, and interpret the result accordingly, and

explain why it isn’t appropriate to test for cointegration of these time series)

Discuss the results from the above analysis. First of all, discuss whether it

makes sense in this case to test for cointegration.

1.6 Volatility Clustering Analysis – Week 9 Exercises

Next, analyse whether the volatility of your time series is clustered, that is,

whether your time series exhibits greater variance at some times than others.

Estimate an ARMA(1,1)-GARCH(1,1) model on your data.

Report estimated coefficients from this model in a table. Are any of the

coefficients significant? What does this mean about whether your time series

display conditional heteroskedasticity? Give a very brief interpretation of the

economics of this result: will a period of high volatility tend to be long-lived,

or will it be brief?

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1.7 Conclusion

Finally, write a brief paragraph summarizing your findings. Again, this should just

be a brief summary of any of the results that give additional economic insights

relating to your outcome variable Yt or the relationship between Yt and Xt.