Models for Growth at Risk – Quantile Regressions – Economics Assignment Help

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Assignment Task :

Part 1: Empirical 

As studied in the lecture material, the paper of Adrian et al. (2019) looks at models for “Growth-at-Risk” (GaR) by relating future real GDP growth to current GDP growth and a nancial conditions index, both for one quarter ahead and one year ahead predictions. In Section 1 of the paper, the authors provide a variety of analysis using Ordinary Least Squares (OLS) and quantile regressions. 

The dataset “USData.csv” (attached to this assignment) contains quarterly data for the US real 2 

GDP growth rate and the Chicago Fed National Financial Conditions Index, taken from FRED. These are the same data series used in Adrian et al. (2019), extended from 2016Q3 to the time period 2020Q3. The dataset also contains the rst and fourth lags of both series, which are used in the one quarter ahead and one year ahead models. 

Produce a report which attempts to replicate some of the ndings of Adrian et al. (2019) and assess whether they are robust to the inclusion of the additional data up until 2020Q3. There is no xed format for your report, but as a guideline you should at least include the following: 

A brief introduction to the replication exercise 

Some discussion of the how the updated data series compare to those of Adrian et al. (2019) and the likely implications for the models and results (see Figure 2 and discussion) Analysis of the OLS regression coecients of future real GDP growth on current GDP growth and nancial conditions in the updated dataset, compared to those in the paper (see Figures 3 and 4 and discussion)  

Analysis of quantile regression coecients at dierent levels in the updated dataset, compared to those in the paper (see Figure 4 and discussion) 

Discussion of the likely suitability of GaR forecasts considering the recent data periods after those used by Adrian et al. (2019) 

 

Part 2: Simulation 

“In the presence of outlier(s), estimators like the Least Absolute Deviation (LAD) estimator are more robust in nite samples than estimators which use squared error loss as their objective function.” 

Design a Monte Carlo simulation study to support the statement above. As a guideline, your report should at least include: 

An introduction including a full description of the data generating processes (DGPs) and estimators used in the study 

Analysis of the results over a range of parameter values in the DGP(s) and across dierent sample sizes 

Discussion of the results and the limitations of the study 

 

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