Elif Sen

English

Research Papers in Economics

 
State Unemployment Rate Nowcasts
*
 
Elif Sen 
October 2010 
 
For the month of August, the unemployment rates for Pennsylvania, New Jersey, and 
Delaware were 9.2 percent, 9.6 percent, and 8.4 percent, respectively, and the national 
unemployment rate was 9.6 percent. Using national unemployment and other regional data, we 
predict the September unemployment rates for the three states in our District to be 9.2 percent for 
Pennsylvania, 9.6 percent for New Jersey, and 8.4 percent for Delaware with 90 percent 
confidence intervals of [9.1,9.4], [9.5,9.7], and [8.3,8.5], respectively
1. 
Although the national unemployment rate for a given month is usually reported on the 
first Monday of the following month, state unemployment rates are not released until roughly 
three weeks later.  For example, the data on the national unemployment rate for September 2010 
were reported on Friday, October 8, 2010, but the state unemployment rate data for the same 
month will not be reported until Friday, October 22, 2010.  Because of this lag in the release of 
state-level data for a given month, it would be desirable to produce a nowcast — which is what 
economists call a “forecast” or estimate of economic activity that has already occurred — of the 
state unemployment rates in advance of the actual report. 
Our goal is to nowcast the unemployment rates for the current month for the three states 
in our District — Pennsylvania, New Jersey, and Delaware — based on weekly state 
unemployment insurance claims (number of continued claims to receive unemployment benefits 
and the number of employed workers covered by state insurance programs, which are used to 
                                                            
* The views expressed here are those of the author and do not necessarily reflect those of the Federal 
Reserve Bank of Philadelphia or of the Federal Reserve System. Elif Sen is a research associate and can be reached 
at Elif.Sen@phil.frb.org. 
1 Due to rounding of the forecasts and interval bounds to match the traditional format of unemployment rates, the 
nowcasts may not be presented as the midpoint of their respective confidence intervals. 
1 
 calculate an insured unemployment rate), historical state unemployment rates, and the national 
unemployment rate. We also include as an indicator of overall state employment the employment 
index from the Federal Reserve Bank of Philadelphia’s Business Outlook Survey, which reflects 
the difference in the percentage of local manufacturing firms that have increased workers and the 
percentage of firms that have decreased workers. 
The availability of these data varies by data series. As noted above, national employment 
data for a given month are released on the first Friday of the following month. Claims data are 
reported weekly (on Thursdays) on a two- to three-week delay, and the BOS for a given month is 
released on the third Thursday of the same month. State employment data for a given month are 
released roughly around the third week of the following month, so by the time state 
unemployment data are released, the insured unemployment rate (which we calculate using 
claims data), the current employment index from the BOS, and the national unemployment rate 
for the same month are already available. Figure 1 shows the timing of all relevant data releases 
for this month’s nowcasts. Because of the delay in data releases for state claims, the insured 
unemployment rate used in the model represents a partial month (the average of two-three weeks 
of data); for this month’s nowcasts, the insured unemployment rate covers three weeks. 
In order to predict the new unemployment rate for each state, we run a linear regression 
of the one-month change in the state unemployment rate (∆state_urt = state_urt - state_urt-1) 
dependent on the following variables: 
•  ∆state_urt-1, the lagged one-month change in the state unemployment rate 
•  ∆state_iurt, the one-month change in the state insured unemployment rate, defined 
as average weekly continued claims of unemployment insurance divided by 
average weekly covered employment 
•  nect, the current BOS employment index 
as shown below: 
∆state_urt = β0 + β1(∆state_urt-1)+ β2(∆state_iurt)+ β3(nect), 
where state_urt is unknown and what we are estimating at time t.    
Additionally, because national unemployment data for the month for which we are 
nowcasting the state unemployment rates are available at the time of nowcasting, we can also run 
a set of regressions by state using the above model with the addition of the one-month change in 
the national unemployment rate (∆us_urt):  
2 
 ∆state_urt = β0 + β1(∆state_urt-1)+ β2(∆state_iurt)+ β3(nect)+β4(∆us_urt). 
The data we use are monthly and run from January 1990 for Pennsylvania and New 
Jersey and from January 1991 for Delaware
2 through the most recent date available, covering 
more than 240 months. Table 1 lists the coefficients and standard errors we obtain from each 
model (excluding and including the U.S. rate) for each state when nowcasting state 
unemployment rates using data through September 2010 (for a September nowcast). For all three 
states, the inclusion of the change in the national unemployment rate improves the model, and 
the coefficient on the national rate change variable is significantly different from zero at the 5 
percent level. 
In order to get a sense of the consistency of the data and our model, we compare the root 
mean squared errors (RMSEs) obtained from two separate methods: rolling regressions and in-
sample and out-of-sample forecasting. Table 2 lists the RMSEs from each method for each state. 
Rolling regressions, which are linear regressions on moving subsamples of the data, were 
run for each state. These rolling regressions use the same linear models described above (with 
and without the national unemployment rate) but on a moving window of a specified size, which 
in this case is 10 years (120 months). Starting in January 2000, we run the models described 
above on a sample of the past 10 years, calculate a “forecasted” rate for the end month (January 
2000), then shift ahead one month and repeat the process through the most recent month of data. 
We end up with at least 120 individual regression results per state. For the end month of each 
window, we calculate a residual from the actual state unemployment rate and the “forecasted” 
unemployment rate. We are then able to construct an RMSE for the entire set of rolling 
regressions by taking the square root of the sum of the squared end-month residuals and dividing 
by the number of months. So for each state i and end-month j: 
           , 
∑        ,           ,  
 
  
 =    
For all three states, the rolling RMSEs are comparable to the RMSEs from the model and are 
lower when the national rate is included. 
                                                            
2 In September 1990, the Delaware unemployment rate jumped to 5.8 percent from an average rate of 3.2 percent for 
the year leading up to that month and remained around that level for the rest of the year. This jump produced a very 
large, isolated residual so we start the Delaware sample in January 1991. 
3 
 We also look at the RMSEs for an in-sample and out-of-sample of the data, with the hope 
that the two are similar, implying consistent data over time. The in-sample consists of the first 10 
years of data for each state. The model is used to estimate an equation for the in-sample data, 
which is then used to produce “forecasts” for the out-of-sample data. RMSEs are then calculated 
for each sample. If the data are consistent over time, the RMSEs will be similar. While for 
Pennsylvania the RMSEs are very similar when the national rate is included, New Jersey and 
Delaware show more disparity. However, if we compare the actual unemployment rates observed 
since January 2008 through August 2010 with the 90 percent confidence intervals created from 
the out-of-sample forecasts over the same period, we see that the forecasts for Pennsylvania and 
New Jersey have been mostly accurate (see Figure 2). 
In general, the RMSEs from the overall model fall between the RMSEs calculated from 
the in-sample and out-of-sample testing and are similar to the rolling regressions in most. With 
the exception of the in-sample RMSEs, including the U.S. unemployment rate lowers the RMSEs 
and improves the predictive power of our model, so we want to continue to use national data to 
help inform the state nowcasts. Table 3 lists the nowcasted (excluding and including U.S. rate) 
and actual state unemployment rates from our models since January 2010 when we began 
nowcasting state unemployment rates. Figure 3 shows the residuals for the model that includes 
the national rate from this month’s nowcast for each state. Over the entire time span, the 
nowcasts are fairly stable, although Delaware has experienced wider dispersion in recent months.  
  Because of the relative consistency of the nowcasts and the lag in data availability for 
unemployment data at the state level and the other data we use in our model, these nowcasts help 
provide a better sense of the current state of the regional economy before actual data are 
released. The model only produces a one-step forecast for each state, but it provides an accurate 
idea of what the unemployment rate is in the absence of actual data. 
 
 
 
 
 
 
4 
 Table 1. Change in State Unemployment OLS Regression Results and Standard Errors, September 2010 
 Pennsylvania  New  Jersey  Delaware 
∆State URt-1     0.151** 
(0.063) 
  0.107* 
(0.064) 
     0.485*** 
(0.053) 
     0.444*** 
(0.055) 
     0.506*** 
(0.058) 
     0.431*** 
(0.056) 
∆State IURt       0.245*** 
(0.047) 
     0.211*** 
(0.048) 
     0.199*** 
(0.063) 
   0.160** 
(0.063) 
0.039 
(0.038) 
0.038 
(0.035) 
NECt     -0.003*** 
(0.001) 
   -0.003*** 
(0.001) 
   -0.003*** 
(0.001) 
   -0.003*** 
(0.001) 
   -0.001*** 
(0.001) 
    -0.000 
(0.001) 
∆US URt         0.113*** 
(0.041) 
       0.114*** 
(0.041) 
       0.219*** 
(0.038) 
Constant  0.009   
(0.005) 
0.008 
(0.005) 
0.005 
(0.005) 
0.004 
(0.005) 
0.005 
(0.006) 
0.003 
(0.005) 
Adjusted R
2   0.377   0.394   0.613   0.623   0.358  0.438 
 
 
Table 2. Root Mean Squared Errors, September 2010 
 Pennsylvania  New  Jersey  Delaware 
Without US UR      
OLS – overall  0.0845  0.0851  0.0840 
OLS – in sample  0.0775  0.0941  0.0446 
OLS – out sample  0.1042  0.0795  0.1759 
Rolling 0.0802  0.0704  0.1003 
With US UR      
OLS - overall  0.0833  0.0840  0.0787 
OLS – in sample  0.0815  0.0955  0.0627 
OLS – out sample  0.0825  0.0685  0.0899 
Rolling 0.0803  0.0688  0.0897 
 
 
5 
 Table 3. Forecasted and Actual Unemployment Rates, January to August 2010 
 Pennsylvania  New  Jersey Delaware 
 Forecasted 
Actual 
Forecasted 
Actual 
Forecasted 
Actual   W/out 
US Rate 
With 
US Rate 
W/out 
US Rate
With 
US Rate
W/out 
US Rate 
With US 
Rate 
January  8.9  8.8  8.8    10.2    10.2   9.9  9.0  8.9  8.9 
February  8.7  8.7   8.9  9.8  9.8   9.8  8.9  8.9  9.2 
March  8.8  8.9   9.0  9.7  9.7   9.8  9.2  9.2   9.2 
April  8.9   9.0   9.0   9.8   9.8   9.8   9.2   9.2   9.2  
May  9.0   9.1   9.1   9.8   9.8   9.7   8.9   9.0   8.8  
June  9.1 9.1 9.2 9.7 9.7 9.6 8.7  8.8 8.5 
July  9.2 9.2 9.3 9.6 9.6 9.7 8.3  8.4 8.4 
August  9.3 9.3 9.2 9.8 9.8 9.6 8.4  8.4 8.4 
 
 
 
 
 
 
 
 
 
 
 
 
6 
 Figure 1. Schedule of Data Releases for September Nowcasts 
12 September  13  14  15  16 
Sep. BOS; 
State claims 
for week 
ending 8/28 
17  18 
19   20  21 
Aug. state 
urates  
22  23 
State claims 
for week 
ending 9/4 
24  25 
26  27  28  29  30 
State claims 
for week 
ending 9/11 
1 October  2 
3  4  5  6  7 
State claims 
for week 
ending 9/18 
8   
Sep. US 
urate;  
State 
nowcasts run
9 
10  11  12  13  14  15  16 
 
 
17  18  19  20  21  22  
Sep. state 
urates  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 
 Figure 2. Unemployment Rates and 90% Confidence Intervals for Out-of-Sample Forecasts by 
State, January 2008 to August 2010 
4
6
8
1
0
Jan08 Jul08 Jan09 Jul09 Jan10 Jul10
 
Pennsylvania
4
6
8
1
0
Jan08 Jul08 Jan09 Jul09 Jan10 Jul10
 
New Jersey
4
6
8
1
0
Jan08 Jul08 Jan09 Jul09 Jan10 Jul10
 
Delaware
January 2008 to August 2010
Actual Rates and 90% CIs for Out-of-Sample Forecasts
 
 
 
 
 
 
 
 
 
 
 
 
 
8 
 Figure 3. OLS Residuals by State When U.S. Unemployment Rate Is Included, September 2010  
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Jan90 Jan95 Jan00 Jan05 Jan10
 
Pennsylvania
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Jan90 Jan95 Jan00 Jan05 Jan10
 
New Jersey
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Jan90 Jan95 Jan00 Jan05 Jan10
 
Delaware
Residuals by State (US Rate Model), September 2010
 
 
 
 
9 
 

State unemployment rate nowcasts

http://www.philadelphiafed.org/research-and-data/publications/research-rap/2010/state-unemployment-rate-nowcasts.pdf

State unemployment rate nowcasts

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Research Papers in Economics