data set consists of weekly initial jobless claims1 aggregated to monthly data and the monthly unemployment rate in the US.2 Jobless claims
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Problem 1: Forecasting Unemployment
The data set consists of weekly initial jobless claims1 aggregated to monthly data and the monthly unemployment rate in the US.2 Jobless claims are very valuable for policymaking because they are released more often and earlier than unemployment numbers, as a result, they are often used to predict the current level of unemployment during recessions. Make sure to load the dynamic package before opening the dataset.
i. Initial claims number to measure how many individuals filed for unemployment in a given week/month while unemployment numbers measures the total numbers who are unemployed at a given point in time. We will thus try to predict changes in unemployment using the claims number rather than the level of unemployment itself. Explain why this is a good strategy using the notion of flow and stock variables from macroeconomics?
ii. We will consider ADL models of the form:
∆Ut = β0 + β1∆Ut−1 + • • • + βp∆Ut−p + δ1Xt + • • • + δqXt−q−1 + et,
where ∆Ut = Ut Ut−1 is the change in the unemployment rate from t 1 to t and Xt are the jobless claims. Estimate the ADL(p,q) model with p = 0 and q = 1, 2, 3 as well as p = 1 and q = 1, 2, 3. Report the OLS estimates.
Hint: to estimate the ADL model described above, you can use the following command from the dynlm package: dynlm( diff(Unempl)~ L(diff(Unempl),0:p)+ L(Claims,0:q), data= data )https://fred.stlouisfed.org/series/ICSA 2https://fred.stlouisfed.org/series/UNRATE
iii. Using the window command, create two subsets of data. The first consists of observations from January 1990 to December 2005 and the second will consist of observations from January 2006 to March 2020. To create the first subset, you could use data1 = window(data,start
= "Jan 1990", end = "Dec 2005").
Estimate the regression models in ii. on the first subset of data and compute the forecasted values on the second subset. The forecasts are computed manually using forecast1 = model1$coef[1] + model1$coef[2]*lag(data2$Claims,0) for an ADL(0,1) model for instance.
iv. Compute the RMSFE for the specifications you estimated above and choose the best model for out-of-sample forecasting.
v. Using the preferred model from the previous question, predict the change in the unemployment rate in April 2020 and, using the March 2020 number, the level of unemployment in April 2020.3
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