ECONOMIC ACTIVITY 

DURING THE GOVERNMENT 

SHUTDOWN AND DEBT 

LIMIT BRINKSMANSHIP

Council of Economic Advisers 

October 2013
1

Introduction

The government shutdown and debt limit brinksmanship have had a substantial negative 

impact on the economy. The shutdown directly affected the economy by withdrawing 

government services for a sixteen day period, which not only had direct impacts but also had a 

range of indirect effects on the private sector. For example the travel industry was hurt by the 

closing of national parks, businesses in oil and gas and other industries were hurt by the 

cessation of permits for oil and gas drilling, the housing industry was hurt by the cessation of 

IRS verifications for mortgage applications, and small businesses were hurt by the shutdown of 

Small Business Administration loan guarantees. In addition, a reduction in consumer confidence 

and an increase in uncertainty associated not just with the shutdown but also the 

brinksmanship over the debt limit affected consumer spending, investment and hiring as well.

A number of private sector analyses have estimated that the shutdown reduced the annualized 

growth rate of GDP in the fourth quarter by anywhere from 0.2 percentage point (as estimated 

by JP Morgan) to 0.6 percentage point (as estimated by Standard and Poor’s), with 

intermediate estimates of 0.2 percentage point and 0.5 percentage point from Macroeconomic 

Advisers and Goldman Sachs respectively. Most of the private sector analyses are based on 

models that predict the impact of the shutdown based on the reduction in government services 

over that period. Very few of them are based on an actual analysis of economic performance 

during the period of the shutdown and very few take into account the secondary effects on the 

private sector of the cessation of government services or the effects on confidence and 

uncertainty associated with both the shutdown and the debt limit brinksmanship. But we know 

that these effects can be large; for example, the debt limit brinksmanship in the summer of 

2011 had an adverse economic impact even though it was not accompanied by a shutdown nor 

did it lead to an actual default on U.S. government obligations. While useful in understanding 

the costs of the shutdown and brinksmanship, the available private-sector analyses present 

only part of the picture.

This report attempts to estimate the actual impact of the shutdown and default brinksmanship 

on economic activity as measured by eight different daily or weekly economic indicators. 

Overall it finds that a range of eight economic indicators combined in what this report calls a 

“Weekly Economic Index” are consistent with a 0.25 percentage point reduction in the 

annualized GDP growth rate in the fourth quarter and a reduction of about 120,000 privatesector jobs in the first two weeks of October (estimates use indicators available through 

October 12th.) 

These estimates could understate the full economic effects of the episode to the degree it 

continues to have an effect past October 12th.
2

Summary of Economic Data for the First Half of October and the Weekly 

Economic Index

The attempt to create an immediate estimate of economic impact is frustrated by the fact that 

most economic data are reported with a long lag (for example, most October data will be 

released in mid-to-late November), are reported on a monthly rather than a weekly basis and 

the weekly and daily data have substantial volatility. These limitations were compounded 

during the shutdown as virtually all government data, with the exception of weekly 

unemployment insurance claims, were halted. By combining a range of indictors that are 

individually noisy it is possible to gather evidence about the trajectory of the economy over 

shorter periods, including during the first half of October. Such short windows are often 

uninteresting given the noise in the data and the generally slow shifts in major economic 

trends. However, during periods with a sharp break in the economic environment—like the 

recent shutdown and debt limit brinksmanship—such estimates can provide a valuable clue to 

the direction of the economy. 

Table 1 shows eight different measures of economic performance and sentiment in the first half 

of October. All eight indicators deteriorate in the first half of October, with the contractions 

being very sharp in several cases.

Table 1. Weekly Economic Indicators and Index, October 2013

Week ending 

9/28

Week ending 

10/12 Change

Johnson-Redbook Same-Store Sales Index

(y/y % chge) 3.8 3.2 -0.6

ICSC Same-Store Sales Index (y/y % chge) 2.1 1.0 -1.1

New UI Claims (thousands) 308 358 50

Gallup Job Creation Index 19.7 16.8 -2.8

Gallup Economic Confidence Index -21.5 -38.2 -16.7

Rasmussen Consumer Index 99.9 91.8 -8.1

AISI Raw Steel Production (y/y % chge) 4.0 6.2 2.2

MBA Mortgage Applications (y/y % chge) -2.5 -11.5 -9.0

Weekly Economic Index 3.6 2.0 -1.6

To understand what this means for overall economic activity CEA combined these indicators 

into a “Weekly Economic Index” that is scaled to match the overall growth rate of economic 

activity (see Figure 1). This “Weekly Economic Index” is designed to extract the main common 

“signal” from the noise of these different indicators. As discussed in more detail later in this 

Report, this Weekly Economic Index was designed to estimate the co-movements among these 

eight indicators, not to predict any particular monthly data series. Nevertheless, the Weekly 

Economic Index turns out to be highly correlated with standard monthly measures of economic 

activity, notably changes in employment and the growth of industrial production.
3

 Source: Bureau of Labor Statistics; CEA calculations. 

This Weekly Economic Index is calibrated to be consistent with the magnitudes of growth rates 

in GDP and shows a sharp 1.6 percentage point reduction in the economic growth rate in the 

fourth quarter if it were sustained for the full 13 weeks of the fourth quarter. Our focus is on 

the first two weeks of October, and these data suggests that the decline over this period will 

reduce the GDP growth rate by 0.25 percentage point at an annual rate (about two-thirteenths 

of the reduction in the index reflecting the two weeks we are analyzing). When calibrated to 

employment growth the Index suggests 120,000 fewer private-sector jobs were created in the 

first two weeks of October than would have been created without the shutdown and debt limit 

brinksmanship.

The Weekly Data Series

The eight series used to construct the index include two measures of retail sales, two measures 

of consumer confidence, two measures of labor market activity, one measure of production, 

and one measure of housing market activity. The series, their source, and release dates relative 

to the observation][ period are listed in Table 2. Seven of the series are privately produced and 

-1000

-800

-600

-400

-200

0

200

400

600

-6

-4

-2

0

2

4

6

Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14

Figure 1. Weekly Economic Index and Monthly Change in 

Private Employment

Percent, Annual Rate

Week Ended 

Oct. 12

Thousands, Seasonally Adjusted

Weekly Economic Index

(left axis)

Monthly Change in Employment

(right axis)

July-August

2011

May-June 2012
4

are based on privately-reported data, while one series, new claims for unemployment 

insurance (UI), is produced by the Department of Labor. All these series were released during 

the shutdown. Three of the series – the Gallup Economic Confidence Index, the Rasmussen 

Consumer Index, and the Gallup Job Creation Index – are available daily, while the rest are

reported weekly.1

Table 2. Description of Variables

Series Name Frequency Release Date Source

A. Consumer Spending

ICSC Same-Store Retail Sales

(52-week growth, %) Weekly Following Tuesday International Council of Shopping Centers

Redbook Same-Store Retail Sales

(y/y growth, %) Weekly Following Tuesday Johnson Redbook Service

B. Consumer Confidence

Gallup Economic Confidence Daily Next day Gallup

Rasmussen Consumer Index Daily Same day Rasmussen Reports

C. Labor Market

Gallup Job Creation Index Daily Next day Gallup

Unemployment Insurance

(Initial Claims) Weekly Following Thursday Department of Labor

D. Industrial Production

Raw Steel Production

(52-week growth, %) Weekly Following Monday American Iron and Steel Institute

E. Housing Market

Mortgage Purchase Applications

(52-week growth, %) Weekly Following Wednesday Mortgage Bankers Association

The eight indicators are plotted in Figures 2-5. Figure 2 shows the two retail sales indexes, one 

constructed by the International Council of Shopping Centers (ICSC) the other by Johnson 

Redbook Service. These series measure the growth in same-store sales over the past 52 weeks. 

Both series are noisy partly because the sample of stores is small and because sales fluctuate 

1

CEA considered a number of other weekly indicators In addition to these eight but they were not used in the 

index based on considerations including the length of the sample, the presence of strong seasonal patterns that 

could not be adequately addressed using 52-week growth rates, reporting dates more than 6 days after the week 

in question, or data irregularities.
5

considerably depending on dates of holidays and major weather systems. Figure 2 also shows 

the 12-month growth in monthly Real Retail Sales and Food Services, a measure of sales that 

includes a wider segment of the retail sector and is based on a larger sample. Although the two 

weekly series have a long-term trend similar to that of the monthly sales series, at any given 

date the two weekly series diverge from the Census series and from each other. Recently, the 

Redbook series has shown stronger sales growth than the ICSC series.

Figure 2

The two labor market variables, shown in Figure 3, are weekly initial claims for unemployment 

insurance and the daily Gallup Job Creation Index. The Gallup Job Creation Index is based on a 

daily random digit dial telephone survey that asks respondents whether their employer is 

increasing or reducing employment (the job creation index is presented here on an inverted 

scale to make it easier to compare with unemployment insurance claims). These two series 

track each other closely and share many common features, including the spike in the week 

ending October 5.

Figure 3

-20 -10 0 10

-4 -2

0 2

Jan. 2008Jan. 2009Jan. 2010Jan. 2011Jan. 2012Jan. 2013Jan. 2014

Redbook Same-Store Retail Sales (y/y % growth, left axis)

ICSC Same-store Retail Sales (y/y % growth, left axis)

Total Retail and Food Services Sales (right axis)

Retail Sales: year-over-year growth

-30 -20 -10 0 10

300 400 500 600 700 Jan. 2008

Jan. 2009Jan. 2010Jan. 2011Jan. 2012Jan. 2013Jan. 2014

New Unemployment Insurance Claims (left axis)

Gallup Daily Job Creation Index (right axis, inverted)

UI Claims and Gallup Weekly Job Creation Index
6

Figure 4 shows the two daily confidence measures, the daily Gallup Economic Confidence Index 

and daily Rasmussen Consumer Index, which are plotted on a standardized scale to simplify 

comparison. The two daily indexes track each other and also track the monthly University of 

Michigan Index of Consumer Sentiment. The three confidence measures are based on 

independent surveys and use differently worded questions. All three measures show a sharp 

decline in the first two weeks of October.

Figure 4

Figures 5 shows the 52-week growth in the Mortgage Bankers’ Association weekly mortgage 

applications for new purchases and the 52-week growth in the American Iron and Steel 

Institute’s raw steel production series. Although both series slumped sharply during the 

recession, they show different patterns during the recovery: weekly steel production grew 

quickly early in the recovery, then slowed, whereas the mortgage applications for new 

purchases started to recover only later, both because of the delayed recovery of the housing 

market and because of the relatively large fraction of cash purchases early in the recovery. 

Figure 5

50 60 70 80 90

-2 -1

0 1 2 3

Jan. 2008Jan. 2009Jan. 2010Jan. 2011Jan. 2012Jan. 2013Jan. 2014

Gallup Daily Economic Confidence Index (scaled, left axis)

Rasmussen Consumer Index (scaled, left axis)

Michigan Consumer Sentiment Index (right axis)

Economic Confidence Indexes

-100 -50 0 50

-60 -40 -20

0

20

Jan. 2008Jan. 2009Jan. 2010Jan. 2011Jan. 2012Jan. 2013Jan. 2014

MBA Mortgage Apps. - Purchases (52-week % growth, left axis)

Steel production (52-week % growth, right axis)

52-Week Growth of MBA Mortgage Apps. (Purchases) and Steel Production
7

Construction of the Weekly Economic Index

As can be seen in Figures 2-5, these weekly series exhibit considerable noise from week to 

week, so that gleaning broader trends from any one series is difficult. They also, however, 

display a clear cyclical pattern, which suggests that these series might usefully be combined 

into a single index. The Weekly Economic Index is computed from these eight series using the 

method of principal component analysis. The principal component of these eight series 

provides an estimate of a signal about the economy which is common to all eight. The 

mathematics of principal components analysis is summarized in the Appendix.

The resulting Weekly Economic Index is shown in Figure 1. By construction, the Weekly 

Economic Index is a weighted average of the eight series. The weekly index explains 58% of the 

overall variance of the eight component series. The Weekly Economic Index is a measure of 

economic growth, and for ease of interpretation it has been scaled to have the same mean as 

the four-quarter growth in real GDP from 2008 to the present. The units of the Weekly Index 

are therefore the units of GDP growth at an annual rate.2

Relationship of the Weekly Index to Monthly Employment and Industrial 

Production

The weekly index is designed to be a gauge of overall economic activity in the current week and

is not intended to forecast the growth of any specific major economic indicator. Nevertheless, 

as seen in Figure 6, the index tracks the overall pattern of changes in monthly private and total 

employment. When the weekly index is aggregated to the monthly level, the correlation 

between the index and the monthly change in total employment is 0.86 (0.87 excluding Census 

workers), and the correlation between the index and the monthly change in private 

employment is 0.87.

2

Specifically, the mean and standard deviation of the Weekly Economic Index have been adjusted so that they 

match the mean and standard deviation of the four-quarter growth of GDP from 2008 through the second quarter 

of 2011.
8

Figure 6

The weekly index also tracks other major economic indicators. The correlation between the 

index and the twelve-month growth of the Index of Industrial Production is 0.84, and its 

correlation with the twelve-month growth of real manufacturing and trade sales is 0.86.

Regression results and sensitivity checks. 

Figure 6 indicates that the Weekly Economic Index moves together with changes in monthly 

employment. This co-movement can be summarized in a regression of the changes of monthly 

employment on monthly values of the index, and the results of this regression are shown in 

Table 3 for monthly changes in private payroll employment. For the Weekly Economic Index, 

the adjusted R2of this regression is 75%.

We computed a number of other indexes as sensitivity checks. Two of these indexes are 

computed as the principal component of a subset of the eight variables: the 6-variable index 

drops steel production and MBA mortgage applications, and the 5-variable index also drops UI 

claims. An alternative 6-variable index was also computed using state space methods, with the 

index estimated using the Kalman filter as discussed in the appendix. Regressions relating 

changes in private employment to these three additional indexes are reported in Table 3. The 

regressions confirm that the three indexes have very similar predictive content for monthly 

changes in private employment, and the results for total employment (not shown) are similar to 

those in Table 3.

-1000 -500 0 500

Thousands

-6 -4 -2

0 2 4

% growth, annual rate

Jan. 2008Jan. 2009Jan. 2010Jan. 2011Jan. 2012Jan. 2013Jan. 2014

Weekly Economic Index

Monthly Change in Total Employment (right axis, thousands)

Monthly Change in Private Employment (right axis, thousands)

Weekly Economic Index and Monthly Change in Employment
9

Table 3. Monthly Aggregate of the Weekly Index and Private Employment: Regression Results

Sample: monthly, February 2008 – August 2013

Dependent variable: Change in Private Employment

Regressors (1) (2) (3) (4)

Weekly Economic Index

115**

(8)

Sensitivity checks:

5-variable index

114**

(8)

6-variable index

111**

(9)

6-variable index estimated by the

Kalman Filter

120**

(8)

Adjusted R20.75 0.74 0.72 0.77

Standard Error of the Regression 154 157 164 147

Notes: Each column summarizes the results of a regression of monthly change in private payroll 

employment on a weekly index, where the weekly index is aggregated to monthly (data for a 

week overlapping two months are assigned proportionately to the two months). Entries are 

regression coefficients, with heteroskedasticity- and autocorrelation-robust standard errors in 

parentheses. The sample size is shorter for the regressions involving the 9-indicator index 

because that index is available starting February 2009. Coefficients are statistically significant at 

the +10%, *5%, **10% significance level.

Table 3 answers, in the affirmative, the question of whether the index is correlated with 

changes in employment. A separate question is whether the index contains information useful 

for predicting monthly changes in employment going forward, beyond the information 

contained in past values of changes of employment. This question is examined in Table 4. The 

regressions in Table 4 take advantage of the weekly nature of index and use values of the index 

for the four weeks between the two measurement reference weeks for payroll employment. 

Broadly speaking, the Establishment Survey aims to estimate payroll employment on the 12th

day of the month. The weekly data structure permits estimation of the effect of economic 

developments between these reference dates from one month to the next. Accordingly, in 

these regressions, payroll employment in a given month is predicted using data for the first two 

seven-day periods of the month, the third seven-day period of the previous month, and the 

final seven-to-ten day period of the previous month – that is, the weeks between the 

Establishment Survey reference date – along with changes of payroll employment from the 

previous month.
10

Table 4. Weekly Values of the Index as a Predictor of Changes in Employment

Sample: monthly, February 2008 – August 2013

Regressors (1) (2) (3) (4)

Weekly 

Economic 

Index

5-variable 

index

6-variable 

index

6-variable 

index, 

Kalman 

Filter

Current month, week 2 81.1

(33.7)

56.5

(28.2)

62.1

(34.1)

75.3

(48.0)

Current month, week 1 4.9

(48.9)

28.1

(37.8)

28.6

(46.9)

41.6

(67.4)

Last month, week 4 -17.4

(50.9)

-56.7

(35.7)

-41.3

(45.5)

-118.6

(59.6)

Last month, week 3 -65.6

(42.0)

-20.6

(31.9)

-48.5

(40.6)

6.9

(45.0)

first lag of change in empl. 0.61

(0.12)

0.62

(0.12)

0.65

(0.12)

0.61

(0.12)

second lag of change in empl. 0.30 

(0.13)

0.27

(0.12)

0.29

(0.13)

0.30

(0.13)

F-test of that coefficients on 

all four weekly variables = 0

3.82

(0.01)

3.96

(0.01)

3.72

(0.01)

3.27

(0.02)

F-test of equality of 

coefficients on all four weekly 

variables 

4.99

(0.00)

5.27

(0.00)

4.77

(0.00)

4.21

(0.01)

Adjusted R20.90 0.90 0.90 0.89

Standard Error of the 

Regression 99 99 99

100

Predicted Change in Total 

Employment (Thousands) -120 -126 -128 -106

Notes: Each column summarizes the results of a regression of monthly change in private 

employment on weekly values of an index, where the first three weekly values in a month 

correspond to the first through third 7-day period of the month and final weekly value 

corresponds to the final 7-10 day period, where data for calendar weeks overlapping adjacent 7-

day periods are assigned to the two periods proportionately. Entries in the top panel are 

regression coefficients, with standard errors in parentheses, and in the lower panel are Fstatistics with p-values in parentheses. The sample size is shorter for the regressions involving 

the 9-indicator index because that index is available starting February 2009. Coefficients are 

statistically significant at the +10%, *5%, **10% significance level.

Two findings in Table 4 are noteworthy. First, the test of the hypothesis that the coefficients on 

the four weekly values of the index are zero constitutes a test of marginal predictive content (a 

so-called Granger Causality test). For the Weekly Index and the four sensitivity check indexes, 

this hypothesis is rejected at the 5% significance level, indicating that the indexes help to 

forecast employment changes beyond the lagged value of employment. Second, the 

hypothesis that the coefficients on the four weeks are equal is rejected for all the indexes; 
11

instead of equality, the coefficients show a similar pattern across indexes in which the change 

in the index between the last two weeks of the previous month and the first two weeks of the 

current month is a useful predictor of employment. Because these regressions contain lagged 

changes in employment, this pattern is consistent with the interpretation of this regression as 

estimating changes in employment, relative to the previous month.

The final line of Table 4 reports estimates of the change in private employment arising from the 

shutdown. These estimates were computed by comparing the actual value of the index for the 

first two weeks of October to the counterfactual in which those values for the first two weeks 

of October were the same as for the last week in September. The estimate based on the 

Weekly Economic Index is a reduction in job changes in October – the immediate cost of the 

shutdown, measured in terms of jobs – is 120 thousand jobs lost. The estimates based on the 

other indexes range between 106 -128 thousand jobs lost.

Conclusion 

In normal times estimating weekly changes in the economy is likely to detract from the focus on 

the more meaningful longer term trends in the economy which are best measured over a 

monthly, quarterly, or even yearly basis. But when there is a sharp shift in the economic 

environment, analyzing high-frequency changes with only a very short lag since they occurred 

can be very valuable. This paper shows that a range of indicators show that sentiment, job 

creation, consumption, and some elements of production grew more slowly in the first half of 

October than in previous months. Moreover, it combines all of these indicators into a single 

measure termed the Weekly Economic Index which is consistent with a 0.25 percentage point 

reduction in the annualized GDP growth rate in the fourth quarter or a reduction of about 120 

thousand jobs in October, based solely on the indicators available covering the period through 

October 12th. These estimates could understate the full economic effects of the episode to the 

degree it continues to have an effect past October 12th—as it most likely would. This is just a 

first attempt to analyze these data and as updated data and further research becomes available 

it could lead to refinements in these estimates.
12

APPENDIX: Principal Components Estimation of Dynamic Factor Models

A leading framework for the construction of an economic index from multiple time series is the 

so-called dynamic factor model, developed by Geweke (1977). The dynamic factor model 

posits the existence of a small number of unobserved or latent series, called factors, which 

drive the co-movements of the observed economic time series. Application of dynamic factor 

models to estimating economic indexes range from the construction of state-level indexes of 

economic activity (Crone and Clayton-Matthews (2005)) to large-scale indexes of economic 

activity (for example, the Chicago Fed National Activity Index, or CFNAI). Stock and Watson 

(2011) provide a review of the econometric theory of dynamic factor models, including recent 

applications.

The premise of a dynamic factor model is that a small number – in the application of this 

Report, a single – latent factor, ft, drives the co-movements of a vector of N time-series 

variables, Xt. The dynamic factor model posits that the observed series is the sum of the 

dynamic effect of the common factor and an idiosyncratic disturbance, et, which arise from 

measurement error and from special features that are specific to an individual series:

Xt = (L)ft + et (1)

where L is the lag operator. The elements of the N1 vector of lag polynomials  (L) are the 

dynamic factor loadings, and i(L)ftis called the common component of the i

th series. The 

dynamic factor can be rewritten in static form by stacking ft and its lags into single vector Ft, 

which has dimension up to the number of lags in (L):

Xt = Ft + et (2)

where  is a matrix with rows being the coefficients in the lag polynomial (L).

The two primary methods for estimating the unobserved factor ft are by principal components 

and using state space methods, where the factor is estimated by the Kalman filter. Broadly 

speaking, early low-dimensional applications used parametric state-space methods and more 

recent high-dimensional applications tend to use nonparametric principal components or 

variants. The key theoretical result justifying the use of principal components is that the 

principal components estimator of the factor (or, more generally, the space spanned by the 

factors) s is consistent and moreover, if N is sufficiently large, then the factors are estimated 

precisely enough to be treated as data in subsequent regressions.

 

The principal components estimator of Ftis the weighted average 



ˆ

Xt, where 



ˆ

is the matrix 

of eigenvectors of the sample variance matrix of Xt, 

ˆ

 X

= 

1

1

T

t t t

T X X 

  

, associated with the r
13

largest eigenvalues of 

ˆ

 X

, where here r=1. The principal components estimator can be derived 

as the solution to the least squares problem,

1

min ( , )

,..., , F F r T  V F 

, where Vr(,F) = 

1

1

( ) ( )

T

t t t t

t

X F X F

NT 

      , (3)

subject to the normalization N

–1 = Ir

. Consistency of the principal components estimator of 

Ft was first shown for T fixed and N   in the exact static factor model by Connor and 

Korajczyk (1986). Stock and Watson (2002a) proved uniform consistency of the factors under 

weaker conditions along the lines of Chamberlain and Rothschild’s (1983) approximate factor 

model, allowing for weak serial and cross-correlation in the idiosyncratic errors. Stock and 

Watson (2002a) also provided rate conditions on N and T under which 

ˆ

Ft

can be treated as 

data for the purposes of a second stage least squares regression (that is, in which the 

estimation error in 

ˆ

Ft

does not affect the asymptotic distribution of the OLS coefficients with 

ˆ

Ft

as a regressor). Bai (2003) provides limiting distributions for the estimated factors and 

common components Bai and Ng (2006a) provide improved rates, specifically N  , T  , 

and N

2

/T  , under which 

ˆ

Ft

is consistent and can be treated as data in subsequent 

regressions; they also provide results for construction of confidence intervals for common 

components estimated using 

ˆ

Ft

.

The main alternative estimation method is to specify a parametric factor model, to estimate the 

parameters by maximum likelihood, and to estimate the factor using the Kalman filter; for 

initial applications of this approach see Engle and Watson (1981), Sargent (1989), and Stock and 

Watson (1989, 1991). In practice the resulting estimates can be sensitive to the parametric 

specification of the model, so principal components estimation is used in this Report. As a 

sensitivity check, however, we also considered the six-variable index estimated using the 

Kalman filter. Regression results for that index are similar to those reported in Tables 3 and 4.

An alternative approach to using high-frequency data for real-time monitoring (“nowcasting”) is 

to focus on forecasting a specific economic release, such as the monthly change in 

employment, and to construct a model that updates those forecasts as new data comes in. The 

dynamic factor model and its state space implementation is useful for this purpose because a 

single model automatically adapts to new data becoming available to estimate the variable of 

interest. For applications of dynamic factor models to nowcasting, see Giannone, Reichlin and 

Small (2008) and Aruoba, Diebold and Scotti (2009).
14

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