代写ECON3183: Time Series Data Analysis代写C/C++语言

ECON3183: Time Series Data Analysis

Individual Project

This document provides questions and requirements for the Individual Project of ECON3183, which accounts for 40% of the total marks.

A statement regarding academic honesty and the stance on using generative AI tools in this course:

To achieve the desired learning outcomes for this course, students must complete individual assignments, a test, and a term project that involves conducting empirical investigations/studies related to economics or finance. Students are expected to obtain data from a reliable source, perform. exploratory data analysis, propose a causal inference strategy with justification, conduct empirical studies, and interpret the results independently.

To ensure that students meet the intended learning outcomes for this course, generative AI tools are not allowed for any submissions (including drafts or final versions) unless otherwise specified in the assessment instructions. All work (including assignment reports, test answers, the term project report, and Stata codes) must be the student’s own or adequately attributed to its source. Using ChatGPT or other AI tools for CA is considered equivalent to receiving help from someone else. It raises concerns that the work is not the student’s own unless the instructor has provided specific instructions to the contrary.

Penalties for unacceptable AI use may include resubmitting the work, partial mark deduction, or receiving zero marks for the corresponding CA component. Turnitin’s ‘Similarity Check’ and ‘AI detector’ features will be used to monitor the use of AI tools in this course.

Deadline: by Dec. 21, 2025.

Submission Method:

a) Submit your typing assignment report in a single PDF file to Turnitin ‘Submission Link: Individual Project - Report’ via iSpace. The file name of your PDF submissions should have the following format: ECON3183_Individual Project_Student ID_Name in Pinyin (e.g., ECON3183_Individual Project_190000001_Mi Lin) and

b) Save your data and .do file(s) in a zip file. Name your zip file as ECON3183_Individual Project_Programme_Student ID_Name in Pinyin. Then, upload your file to ‘Submission Link: Individual Project - Stata Data and Program’ via iSpace.

Assignment Guidelines:

Please note that this is not an essay-type assignment. Answer each of the following questions one by one. Modify the STATA example programs from the Lectures on iSpace as needed to address each question. Name your STATA programs (.do files) as “Q1”, “Q2_PartA”, “Q2_PartB”, etc., so that it is easy to match each program with its corresponding answer.

Question 1 (20%): Interpret an Existing Study

Referring to the paper by Chow and Wang (2010), “The Empirics of Inflation in China”, on the first page, the last paragraph, the authors report that “Let ut  = log(pt ) — log(p*t) be the estimated trend deviation of the log price level or the error correction term. We then regress dlog(pt ) on dlog(M2t /yt ), dlog(pt-1 ), and ut-1  where dlog(xt) is defined  as log(xt) — log(xt-1),  …”. What is  “trend” referred to in the first sentence, and for what purpose should it be estimated? Replicate the last two regression results reported at the end of this paper. Illustrate your understanding of this empirical approach. What is the specific purpose of these regressions? How should we interpret the estimated coefficients in this model? Illustrate your understanding that the coefficient on ut-1  is negative.

Question 2 (80%): Mini Independent Empirical Study

Select an economy that interests you and download data for it from reliable source(s) for the following empirical investigations outlined in Part A and Part B. Each economy can be chosen by only one student. A data sign-up sheet is available on iSpace from 9 pm on November 28, 2025, where you can indicate your choice of economy by November 30, 2025. We will adhere to a ‘first-come, first-served’ principle.

It is strongly advised that you verify data availability before finalizing your choice of economy using the data sign-up sheet, just as you would for your Final Year Project (FYP). To ensure fairness, once you register on the sign-up sheet, your chosen economy will be considered final and cannot be changed.

Examples of reliable data sources include, but are not limited to:

- IMF Data Portal: https://data.imf.org/ or https://data.imf.org/IFS,

- World Bank Open Data: https://data.worldbank.org/,

- Bank for International Settlements (BIS): https://data.bis.org/topics/EER,

- Official Website of the Government Statistics Bureau (e.g., https://www.stats.gov.cn/),

- Renowned University/Research Institution’s Website, e.g., UBC Sauder School of Business (maintained by Prof. Werner Antweiler) on exchange rates: https://fx.sauder.ubc.ca/, etc.

which are all publicly available databases supplied by experts or organizations with a good reputation in the field, and that provide trustworthy, accurate, credible, and up-to-date information.

Using data from non-authority sources could lead to failure in this assessment component.

Part A (50%):

Download the following variables for an economy of your choice (Domestic) and the United States (Foreign) from reliable data source(s), starting from January 1970 (or as early as possible) to December 2024 (or as recent as possible).

• Exchange Rate: Monthly average and daily of the Nominal Exchange Rate (St), defined as units of Domestic Currency per 1 unit of U.S. Dollar.

• Domestic Prices: Monthly Consumer Price Index (CPI) for your chosen economy.

• Foreign Prices: Monthly Consumer Price Index (CPI) for the United States.

Use monthly data for Questions 1 to 3, and daily data for Questions 4 and 5. Provide comments on the results in the context of the economy you chose.

1. Data Preparation and Description (5%): Introduce  your  data  source.  Convert seasonally unadjusted CPI data to seasonally adjusted data if the raw data is not already adjusted (Note: Exchange rates typically do not require seasonal adjustment, but verify this for your specific currency). Let pt  be the Domestic CPI and pt* bethe US CPI.  Calculate the  quarterly  log-difference for both to derive inflation rates: πt  = Δln(pt ) and πt* = Δln(pt*). Plot the log-difference of the exchange rate, Δst = Δln(st ), and  the  inflation  differential, ( πt  — πt* ). Comment on the summary  statistics, stationarity, and the visual correlation between the two series.

2. ADL Model Estimation (10%): Relative   Purchasing   Power   Parity   suggests  a relationship between changes in the exchange rate and inflation differentials. Let yt  = Δst    (depreciation   rate)   and   xt  = (πt  — πt*)   (inflation   differential).   Estimate   an ADL(p, r) model ofyt  on xt:

Choose values for p and r as you see fit and provide justifications. Interpret the coefficients. Does a rise in the inflation differential lead to a depreciation of the domestic currency in the short run? What about in the long run?

3. Pseudo Out-of-Sample Forecasting (25%): Using the model you estimated in (2), perform. pseudo-out-of-sample forecasting for the period from January 2020 to December 2024 (or the most recent 5 years), employing a rolling-window approach with loops. Calculate the forecast errors. Plot these errors along with a confidence band (such as RMSE) to create a graph similar to the example discussed in the lecture notes. (Tip: In Stata, combining “graph twoway rarea” with line plots is useful here.) Comment on the forecasting performance. Does the PPP-based model predict exchange rate movements more accurately than a random walk?

4. GARCH Modelling (5%): Financial  time  series,  particularly  exchange  rates, often exhibit  volatility  clustering.  Using  daily  data  on  the  exchange  rate  returns  Δst , estimate an AR(p) model with GARCH(2,2) errors:

Choose  a  value  for  p  as  you  see  fit  with  justification.  Interpret  the  ARCH  term and GARCH term coefficients. What do they imply about the persistence of volatility shocks in this currency market?

5.    Volatility  Analysis   (5%): Based  on  the   regression  results  in   (4),  recover  the conditional variance series σt(2) . Plot the depreciation rate from its mean along with conditional standard deviation bands (±t ). Do you observe any sharp increases in volatility?  Provide  an  economic  interpretation  of  these  volatility  spikes  for  your specific economy.

Part B (30%):

Download from reliable data source(s) the following monthly variables for the economy of your choice from 1970m1 to 2024m12 (or available sample period):

• Exchange Rate (st ): Monthly average nominal exchange rate (Domestic per USD).

• Domestic Price Level (pt ): Monthly Domestic CPI.

• Foreign Price Level (pt(*)): Monthly US CPI.

Theoretical Framework: In international macroeconomics, the interactions between prices and exchange rates are central to the transmission of monetary shocks. We are analyzing a dynamic system involving three variables. Let:

Nominal exchange rate appreciation/depreciation:    et   = Δln(st ),

Domestic Inflation:    πt   = Δln(pt ),

Foreign Inflation: πt(*)  = Δln(pt(*)).

We can construct a vector autoregression (VAR) system to analyze the dynamic feedback loops between these variables:

zt  = A0  + A1zt-1  + A2zt-2  + … + Apzt-p  + ut

where zt  = [et, πt, πt(*)],.

1.    VAR Estimation and Granger Causality (10%): Estimate a VAR(4) for the system zt  = [et, πt, πt(*)],. Perform Granger Causality tests based on the estimated results of the VAR(4) model:

o Does Domestic Inflation (πt ) Granger-cause Exchange Rate changes (et )?

o Does the Exchange Rate (et ) Granger-cause Domestic Inflation (πt ) (Exchange Rate   Pass-Through)?   Repeat   the   exercise   to   investigate   the   relationship between Foreign Inflation (πt(*)) and the Exchange Rate.

Explain and comment on your results.

2.    Lag Length Selection (10%) Should the VAR model estimated in (1) include more or fewer than four lags? Compute information criteria for lag lengths p = 1 to p = 12. Based on these statistics and considerations of autocorrelation in the residuals, justify your choice of the optimal lag length.

3.    Impulse Response Analysis (10%): Now estimate the three-variable VAR(p) model using  the  lag  length  selected  in  (2).  Conduct  Impulse  Response  Functions  (IRF) analysis based on this VAR model.

o Plot the response of Domestic Inflation (πt ) to a shock in the Exchange Rate (et ). Is there evidence of “pass-through” inflation?

o Plot the response of the Exchange Rate (et ) to a shock in Domestic Inflation (πt ). Provide  comments  on  the  magnitude,  direction,  and  persistence  of  these responses in the context of the economy you chose.

o Plot the response of the Exchange Rate (et ) to a shock in Foreign Inflation (πt(*)). Provide  comments  on  the  magnitude,  direction,  and  persistence  of  these responses in the context of the economy you chose.