Distilling the Macroeconomic News Flow∗

Alessandro Beber†, Michael W. Brandt‡, Maurizio Luisi§

February 2013

Abstract

We propose a simple cross-sectional technique to extract daily latent factors fromeconomic news releases available at different dates and frequencies. Our approachcan effectively handle the large number of heterogeneous announcements that arerelevant for tracking current economic conditions. We apply the technique to extractreal-time measures of inflation, output, employment, and macroeconomic sentiment,as well as corresponding measures of disagreement among economists about thesedimensions of the data. We find that our procedure provides more timely andaccurate forecasts of the future evolution of the economy than other real-timeforecasting approaches in the literature.

Keywords: macroeconomic news, nowcasting, disagreement.

JEL classification: G12

∗We thank Fabio Fornari, Amit Goyal, and seminar participants at BlackRock, City University, the 2012 AssetPricing Retreat at Cass Business School, the Fall 2012 Inquire UK Conference in Bath, the Imperial College HedgeFund Conference, and the University of York, for their comments and suggestions. We are indebted to InquireUK for financial support.†Cass Business School, City University London, and CEPR‡Fuqua School of Business, Duke University, and NBER§Quantitative Investment Solutions

1 Introduction

Timely measurement of the state of the macroeconomy relies traditionally on low-frequency

observations of a few economic aggregates that refer to previous weeks, months, or even quarters.

A prominent example is the advance estimate of GDP released quarterly about a month after

the end of the quarter. The low frequency and delayed observation of any such economic

aggregate considered in isolation stands in sharp contrast with the rich economic news flow that

market participants observe daily. This news flow contains information that agents use to learn

about the economy in the absence of private information. In particular, the macroeconomic

news literature has identified a large cross-section of dozens of different news releases that have

significant and immediate effects on financial markets (e.g., Andersen et al., 2003).

We propose to distill the economic news flow observed by market participants into a set

of indicators measuring four distinct aspects of the economy: inflation, output, employment,

and macroeconomic sentiment. Specifically, we describe a simple cross-sectional technique

to extract daily principal components from economic news releases associated with a given

information type and observed at different times and frequencies. Our approach is simple,

robust (no numerical optimization is required), and can effectively handle the large number

of announcements that are relevant for tracking the evolution of macroeconomic conditions in

real-time. At the same time, our empirical analysis shows that the output of our approach is as

if not more timely and informative than more sophisticated but also more difficult to implement

statistical techniques. Intuitively, the potential disadvantage of a simpler modeling framework

is more than compensated by the sheer quantity of data our approach can effectively process.

Our paper relates to the literature that attempts to measure the state of the economy in

a time-series setting based only on fundamental economic data (see Banbura et al., 2012, for

a survey), commonly referred to as “nowcasting.” There are basically two general approaches

to this problem. The first approach is to use a balanced panel regression, along the lines of

the seminal paper of Stock and Watson (1989). The goal is to construct a coincident index of

economic activity using factor models on a large set of macroeconomic releases, which basically

amounts to constructing a weighted average of several monthly indicators. The advantage of this

approach is that the indicator relies on many macroeconomic variables. However, this advantage

also results in a relatively low frequency of the measurement, because the econometrician has

to wait for the panel to be complete before the index can be evaluated. A second general

approach is to model macroeconomic data using a state-space framework (e.g., Evans, 2005).

The advantage of this second approach is to produce an indicator at a higher frequency, since

a state-space model can effectively handle the sparse and delayed reporting of macroeconomic

data and missing information on non-release days. However, this technique is impractical for

large cross-sections of macroeconomic releases. For example, Evans (2005) only considers the

set of different (preliminary, advance, final) GDP releases. In follow-up work, Arouba et al.

(2009) accommodate four indicators at different frequencies including a continuously observable

financial markets variable. Finally, Giannone et al. (2008) combine the two approaches by

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modeling factors extracted from a balanced panel in a state-space setting.

Our goal is to measure the state of the economy with a methodology that retains the

advantages of both approaches without their respective limitations. Specifically, we propose a

simple and economically intuitive cross-sectional dimension reduction technique that can easily

accomodate a large set of macroeconomic releases. This is a crucial aspect of our approach,

given the evidence in the literature that the information relevant for describing the state of the

economy is high dimensional. At the same time, our methodology is designed to handle data

released at different frequencies and missing observations to produce a real-time (daily or even

intradaily) high-frequency measurement of the state of the economy.

Our methodology has several other differentiating features relative to the literature. First,

we do not aim to estimate a real-time series of GDP, or any other single observable variable,

but we rather leave the factor(s) truly latent and unspecified. In this sense, we do not impose

any structure on the estimation and thus do not take a stand on what is the appropriate metric

of the state of the economy. We simply let the data speak for itself. Second, our focus on a

large cross-section of economic news releases allows us to extract factors from four subsets of

macroeconomic news (e.g., inflation, output, employment, and macroeconomic sentiment). We

use these subset indicators to learn about the relations between different driving forces of the

economy. Third, we utilize news flow data that is truly real-time and unrestated, as opposed to

approximately dated historical data that is notoriously restated (e.g., Koening et al., 2003; see

also Ghysels et al., 2012, for an illustration of the issues arising from restated macroeconomic

data). Finally, we also apply our methodology to the dispersion of forecasts. This is a new way

to obtain a high-frequency measure of macroeconomic uncertainty based on the disagreement

of a cross-section of economic experts.

We find that an economic activity factor (which combines output and employment information,

as they are highly correlated) as well as a macroeconomic sentiment factor, both extracted

from the large cross-section of macroeconomic news, have sensible dynamics. The greatest

dips in both series are well aligned with the ex-post defined NBER recession phases. The

macroeconomic sentiment factor, obtained from consumer and business confidence releases, is

highly correlated with economic activity, but appears to lead fundamentals especially around the

most important turning points. Finally, our inflation factor exhibits dynamics that seem only

weakly correlated with growth, with much more erratic variation, and has an unclear pattern

in expansion versus recession phases.

Our empirical proxy of economic uncertainty based on economist disagreement is interesting

for at least two reasons. First, it shows little correlation with the estimates of the latent economic

activity, macroeconomic sentiment, and inflation factors, suggesting that they are likely to

contain different information. Second, and more importantly, macroeconomic uncertainty exhibits

intriguing asymmetric dynamics. The peaks of disagreement correspond to the final stages of

recession periods, while uncertainty is relatively subdued at the end of economic expansions.

This evidence suggests that economists tend to disagree mostly on recoveries from economic

contractions, whereas everyone seems to see the end of an expansion coming.

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We formally relate a real-time factor of economic growth (which further aggregates the

information relative to economic activity by combining information relating to output, employment

and macroeconomic sentiment) to the CFNAI index, which is constructed by the Chicago

Federal Reserve Board based on Stock and Watson (1989), on CFNAI release dates at the

monthly frequency and to the vintage version of the ADS index of Arouba et al. (2009) at

the weekly frequency. We find that our latent growth factor is strongly correlated to both of

these alternative approaches. However, since our factor is constructed using information from

either a larger cross-section of news or in a more timely manner, it turns out to have significant

forecasting power for both CFNAI and ADS beyond their own lags. In a related empirical

exercise, we find that our growth factor has predictive power for future actual GDP releases

and is highly correlated with the quarterly GDP expectations in the Survey of Professional

Forecasters (SPF). This is a remarkable feature given that, unlike the CFNAI and ADS indices,

our growth factor is not optimally weighted to forecast GDP. The large correlation with the

quarterly releases of the SPF offers an intuitive interpretation of our growth factor as the high-

frequency daily reading of economist expectations about macroeconomic fundamentals.

A very intriguing finding is that the latent factor obtained exclusively from macroeconomic

information turns out to be highly correlated with financial indicators, such as the default spread

or the implied stock return volatility index VIX. More specifically, we find that the combination

of our latent growth factor and its dispersion can explain almost one-third of VIX levels. This

is an important finding in light of the documented difficulties for macroeconomic quantities to

explain financial volatility (see, for example, the seminal paper of Schwert, 1989).

Finally, we combine the information of the growth indicator and its dispersion and document

very strong predictability for future growth, from five days and up to six months ahead. Given

the illustrated relation of our macroeconomic indicator with financial variables and its extremely

timely nature, this result suggests that our quantitative measure of the news flow could have

predictive power for future financial market dynamics.

The remainder of the paper proceeds as follows. In Section 2, we describe the macroeconomic

news and we carry out some preliminary analysis on economic announcements. Section 3

explains our methodology for estimating in real-time the state of the economy, as captured

by four dimensions of the data (inflation, output, employment, and macroeconomic sentiment),

and ex-ante uncertainty revealed by the cross-section of economist forecasts. We present our

empirical results in Section 4. Section 5 concludes with a summary of our findings.

2 Data and Preliminaries

2.1 Macroeconomic news and forecasts

We obtain data on the dates, release times, and actual released figures for 43 U.S. macroeconomic

announcements covering the period from January 1997 through December 2011, for a total

of more than 8,000 announcements over about 3,800 working days. This data is obtained

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from Bloomberg through the Economic Calendar screen, which provides precisely time-stamped

and unrestated announcement data.1 We also collect data on economist forecasts for each

announcement. Bloomberg surveys economists during the weeks prior to the release of each

indicator to obtain a consensus estimate. We work with the individual economist level forecasts,

rather than the aggregated consensus forecasts, in order to construct cross-sectional measures

of disagreement for each news release.

Bloomberg contains data for many of our series prior to 1997, but those data are stored in

historical fields which (a) are not associated with clear announcement dates and times (rather

they are dated according to the period they reference) and (b) are restated over time.2 We

collect this more problematic data for January 1985 through 1996 for two reasons. First, we

use this historical data to construct an initial correlation matrix estimate, which is required by

our methodology (see Section 3). Second, we use this data for a robustness check with a longer

sample period (see Section 4.5). In order to date the releases prior to 1997, we compute for each

news series the median time between the reference period and the announcement. For example,

the employment report is traditionally released four days after the end of the month to which

the report refers. We then apply this median reporting lag to the reference period of the older

data in order to obtain an approximate announcement date.

Since economist-level forecasts are not available prior to 1997, we instead collect data from

the Survey of Professional Forecasters. The Survey of Professional Forecasters is the oldest

quarterly survey of macroeconomic forecasts in the United States. The survey began in 1968 and

was conducted by the American Statistical Association and the National Bureau of Economic

Research. The Federal Reserve Bank of Philadelphia took over the survey in 1990. The Survey of

Professional Forecasters’ web page offers the actual releases, documentation, mean and median

forecasts of all the respondents as well as the individual responses from each economist. The

individual responses are kept confidential by using identification numbers.

Most macroeconomic indicators are released on different days and at different frequencies,

making it difficult to process the flow of information in a systematic and consistent way. Figure

1 shows that actual news releases occur with a variety of different lags with respect to the

month they are referring to. Furthermore, news on different indicators are frequently released

simultaneously.3 For example, the employment report traditionally announced on the first

Friday of the month contains four different indicators: non-farm payrolls, non-farm payrolls

in the manufacturing sector, unemployment rate, and average weekly hours. Finally, the

release frequency varies across different economic aggregates. Data releases of different economic

indicators are usually observed at different frequency; e.g., GDP data are sampled quarterly,

1The importance of using real-time versus final data in macroeconomic forecasting has been discussedextensively in the literature (e.g., Koenig et al., 2003).

2For example, there are monthly releases of quarterly GDP labeled “advance,” “preliminary” and “final” allreferring to the same quarter. Bloomberg’s historical field for GDP is dated according to the referenced quarter,so that the advance release gets overwritten by the preliminary release, which in turn gets overwritten by thefinal release. Historically only the final releases are stored.

3On approximately 80 percent of days, there was at least one data release. Multiple data releases occurredmuch less frequently, on approximately 60 percent of the days in the sample.

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the non-farm payrolls are released monthly, initial jobless claims are sampled weekly, etc. These

features of our large cross-section of macroeconomic releases generate a sparse matrix of data

that our methodology will have to take up.

The Appendix describes in detail the set of macroeconomic news in our sample, including

their frequency, source, and units of measurement.

2.2 Categorizing the macroeconomic news flow

Our aim is to extract a set of factors describing the state of the economy. Rather than relying on

a statistical procedure to obtain a set of orthogonalized factors that are increasingly difficult to

interpret with the order of the factor, we impose a specific economically motivated structure on

the macroeconomic news flow. Based on both empirical evidence and economic rationale, we first

separate the aggregate economy into two broad dimensions: the nominal, and the real side.4

In practice, we split the set of announcements into nominal inflation-related announcements

and news that relates to real growth. Growth data, in turn, come in two flavors – objective

realizations of past economic activity and subjective often forward-looking views derived from

surveys which we label “macro sentiment.” Finally, economic activity can be split one last time

into information relating to output versus employment.

Through this structure, we obtain two (inflation and growth), three (inflation, economic

activity, and macro sentiment), or four (inflation, output, employment, and macro sentiment)

factors:• Inflation

• Growth

Economic Activity

Output

Employment

Macro Sentiment

where, for example, the Economic Activity factor is obtained from the combined information

relating to Output and Employment. In that sense, the information is nested from right to left.

More specifically, the inflation factor is extracted from the news flow of 10 inflation-related

releases: consumer price index, CPI ex food and energy, the employment cost index, GDP price

index, import price index, nonfarm productivity, PCE core, PPI ex food and energy, producer

price index, and unit labor costs. For the output factor, we utilize information from both the

supply and demand side of the economy in the form of news about advance retail sales, business

inventories, capacity utilization, consumer credit, domestic vehicle sales, durable goods orders,

durables ex-transportation, factory orders, GDP, industrial production, ISM manufacturing,

ISM non-manufacturing composite, personal consumption, personal income, personal spending,

retail sales less autos, and wholesale inventories. Employment news is captured by releases

of ADP payrolls, manufacturing payrolls, non-farm payrolls, continuing claims, initial jobless

4The economy is often separated into the nominal and real sides because shocks to the two should be separatedand treated differently. For example, many argue, from the perspective of monetary policy, nominal shocks shouldbe minimized, whereas real shocks should not be intervened upon. Other studies also suggest that a nominal anda real factor can account for much of the observed variation in major economic aggregates.

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claims, and the unemployment rate. Finally, we extract the macro sentiment factor from

the news flow generated by 10 macroeconomic surveys: ABC consumer confidence, Chicago

purchasing manager, consumer confidence, Dallas Fed manufacturing activity, Empire manufacturing

survey, leading indicators index, NAPM-Milwaukee, Philadelphia Fed business outlook survey,

Richmond Fed manufacturing index, and the University of Michigan confidence index. The

Appendix includes a summary on how the 43 announcements are assigned to the four categories:

inflation, output, employment, and macro sentiment.

It is worth reiterating at this point that we do not include any market based data (such as

stock prices, interest rates, credit spreads, or VIX) in our analysis, unlike, for example, Arouba

et al. (2009) and Giannone et al. (2008). While such data are very timely and undoubtedly

informative about the state of the economy, they represent already the market’s interpretation

of the macroeconomic news flow. Our aim is to objectively summarize and describe the

macroeconomic news flow itself.

2.3 Transformation and temporal alignment

We examine the stationarity of each data series in two ways. First, we conduct a Dickey-Fuller

test on each series. Second, we read the definition and description of each statistic to determine

from an economic perspective whether it is a non-stationary index or a stationary quarterly

growth rate, for example. In a few cases where the conclusions from the two approaches differ,

usually because the available data is too short to examining statistically, we rely more on

the description to determine whether the series is stationary. All series that are deemed non-

stationary are then first-differenced in news release time. The Appendix provides more details.

The final data management task is to align the data temporally by moving from announcement

time to calendar time. We do this by populating the news releases in a T×N matrix where T

denotes the total number of days in our sample and N refers to the 43 announcement types.

The data at this stage looks like the top panel of Figure 2.

There are two important aspects of the data to discuss. First, there are a vast number

of missing values as we can think of each news series as a continuous evolving statistic that

is observed only once per month or quarter. Second, not all announcements have a complete

history. Some announcements are initiated in the middle of the sample and/or are terminated

before the end of the sample. To solve the missing data problem, we simply forward fill the last

observed release until the next announcement. Forward filling can be rationalized as replacing

missing values with expected values under a simple independent random walk assumption for

each news series. Of course, both independence in the cross-section and random walk dynamics

through time are simplifying assumptions that are rejected by the data (in fact, the motivation

for our methodology described below is the cross-sectional correlation structure within news

category). A more sophisticated approach for filling in missing data would be to compute the

expectation of the missing values given the full cross-section of previous releases as well as the

cross-sectional and intertemporal correlation structure of the data. An optimal solution would

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also allow for sampling error, which is the case in Kalman filter or Bayesian data augmentation

algorithms. However, there is a clear trade-off between statistical complexity and ability to

process a large cross-section of news series. Since the goal of our approach is to utilize the

entire cross-section of news, we choose a very simple statistical model for filling in missing

observations. After forward filling, the data looks like the bottom plot of Figure 2.

Note that the second data issue, the fact that some series do not span the entire sample

period, cannot be solved with missing values imputation. It is instead explicitly addressed in

our methodology below.

3 Methodology

3.1 Subset principal component analysis

Our goal is to extract from the cross-section of macroeconomic news releases a set of factors

that capture in real time the state of inflation, output, employment, and macro sentiment, as

well as the two more overarching factors measuring economic activity and growth. The most

obvious ways of accomplishing this, full data principal components and forecasting regressions,

do not appeal to us. First, with full data principal components (or factor analysis) we would

obtain factors that are mechanically orthogonal, whereas the dimensions of the economic news

flow we want to capture are likely correlated (e.g., output and employment are both high at the

peak and low at the trough of an economic cycle). This orthogonalization makes is practically

impossible to assign an economic meaning to higher order factors. Second, trying to identify

the factors through predictive regressions on a candidate variables in each category, such as

final GDP for output, would require us being able to identify a single series that represents

each category. While this is a common approach in the nowcasting literature (e.g., Stock and

Watson, 1989), it relies on ex-ante knowledge of the key statistic to track and assumes that

there is only one such statistic that does not change over time.

Instead, we rely on our ex-ante categorization of the news and, within each category subset,

let the data speak for itself by extracting the first principal component of that subset of data.

Specifically, on each day of our sample t, we obtain for each news category i the first principal

component from the correlation matrix Ωt,i of the stationary news series in category i. We

work with the correlation matrix to abstract from arbitrary scaling of data. Moreover, in order

to obtain a real-time measure, we use a telescoping (with a common historical start date and

rolling end dates) correlation matrix starting in 1980.5 We denote the Ni×1 principal component

weights by ct,j , where Ni is the number of news series in category i.

5We also experimented with fixed window size rolling correlation matrices for 5, 10, 15, and 20 years. Theresults are qualitatively similar, particularly for the longer data windows.

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3.2 Economic new series correlation matrix

The key inputs to our methodology are the within news category correlation matrices Ωt,i.

Specifically, we need to calculate from historical data up through date t the correlation of all

news series of category i that are “active” on that date, where active means that the news

series was previously initiated and has not yet been terminated. There are two issues that

need to be addressed in computing these correlation matrices. First, the data is in the form

of an unbalanced panel due to some of the series being initiated after the start date of the

estimation window (e.g., series j = 5 in Figure 2). Second, the data is naturally persistent,

partly due to autocorrelation of the the data in announcement time, partly due to the cross-

sectional misalignment of the news in calendar time, and partly due to the forward filling of

missing data.

We address the first unbalanced panel issue by using a correlation matrix estimator along

the lines of Stambaugh (1997), who shows how to adjust first and second moments estimates

for unequal sample lengths. The intuition of his approach is to use the observed data on the

longer series, along with a projection of the shorter series on the longer ones estimated when

both are observed, to adjust the moments of the shorter time series.

To correct for the persistence in the data, we could use in principle the standard approach

of Newey-West (1987), where due to the nature of the data we would like to adjust for up to

one quarter of autocorrelation and cross-autocorrelation. Unfortunately, the nature of the

persistence in our data is not ideally captured by the non-parametric Newey-West (1987)

approach for two reasons. First, we have daily data, so adjusting for up to a quarter of

autocorrelation would involve approximately 60 cross-autocorrelation matrices. Second, the

(cross-) autocorrelations are not exponentially decaying as a typical ARMA model might predict.

Instead, the data is locally constant, due to the forward filling, and over longer intervals

only moderately (cross-) autocorrelated due to the statistical nature of the news series. This

peculiar correlation structure is actually identical to that found in high-frequency asset prices,

where asynchronous and infrequent trading creates a misaligned and locally constant panel

of observations. In that literature, Ait-Sahalia, Mykland, and Zhang (2005) propose a “two-

scales realized volatility” estimator to handle this specific structure of short versus long-horizon

dependence. Specifically, their estimator subsamples the data at a sufficiently low frequency

that overcomes the local constancy and then averages over the set of all possible estimators that

start the subsampling schemes at different times.

We adopt exactly the same approach, except of course our application is very different.

Specifically, at date t we subsample the forward filled news series backward at a monthly

frequency and then compute a Newey-West estimate of the correlation matrix using four lags.

We repeat the same for monthly sampling starting at dates t− 1, t− 2, ..., t− d+ 1 (assuming

d days per month) and then average the resulting d correlation matrix estimates.

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3.3 Level versus disagreement factors

Given the vector of principal component weight ct,j , we then construct for each news category

two times-series. First, we sum at each date the product of the weights multiplied by the

most recent releases to obtain our real-time level factors. Second, we sum the product of the

same weights multiplied this time by the cross-sectional standard deviations of the economist

forecasts for the most recent releases to obtain our real-time disagreement factors. Throughout

our sample not every news series has economist level forecasts data available. We therefore

construct the disagreement factor using the available data, renormalizing first the principal

component weights to account for the proportion of missing data.

4 Results

We first provide some descriptive evidence about the dynamics of our real-time macroeconomic

factors. Next, to have a sense for how our methodology compares empirically with other

approaches in the literature, we focus on the real-time growth factor and compare it with

the vintage versions of the CFNAI index and of the ADS index of Aruoba et al. (2009).

We then analyze whether our real-time factors are significant predictors of subsequent GDP

releases, in a horse race with the popular forecasts from the Survey of Professional Forecasters

(SPF) of the Federal Reserve Bank of Philadelphia. We then examine the relation between the

real-time growth factor, its dispersion, and volatility in financial markets to establish a tighter

relation between financial volatility and macroeconomic dynamics than previously observed

in the literature (e.g., Schwert, 1989). Finally, we analyze the predictability of our real-time

growth factor for different forecasting horizon and we extend the sample backward using a

pseudo real-time approach as a robustness check.

4.1 Preliminaries

In panel A of Table 1, we compute correlations between seven underlying factors describing

different dynamics of the U.S. economy and their respective proxies for macro uncertainty. We

observe a number of interesting facts. First, there is low correlation between latent factors

and their dispersion measures, suggesting that they are likely to contain different kinds of

information. For example, the growth factor has a -0.14 correlation with growth dispersion.

Second, the inflation and growth factors also exhibit low correlation, confirming our ex-ante

view that it makes sense to consider them separately. Third, the macroeconomic sentiment

factor happens to be strongly related to economic activity, suggesting that the various confidence

readings are largely based on the observed strength of the economy. Inflation information, in

contrast, seems unrelated to sentiment.

In panel B, we compute contemporaneous correlations between the growth and growth

dispersion factors and a number of financial market variables that in various cases have been

associated with the state of the economy or macroeconomic uncertainty. There is basically no

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contemporaneous correlation between our latent factors and stock market returns. In contrast,

there are important contemporaneous correlation between the growth factor and a number of

financial variables, most notably the correlation with VIX (-0.51), the dividend yield (-0.71),

and the default premium (-0.84).6 The growth dispersion factor has a somewhat weaker relation

with financial variables, but it still retains a significant correlation with VIX (0.25) and with

the price/earning ratio (0.19). These descriptive results foreshadow the strong link between our

macroeconomic factors and financial volatility documented more thoroughly in Section 4.3.

Figure 3, 4, and 5 provide the graphical counterpart of the unconditional correlations in

Table 1. Figure 3 shows the estimated real-time output and employment factors. Output seems

to anticipate employment somewhat, especially around turning points, but the two factors are

very highly correlated. Given this high correlation, we proceed to work with the more aggregated

economic activity factor for the remainder our our empirical analysis.

We compare our real-time measurements of economic activity (which combines output and

employment releases) with macro sentiment in the upper panel of Figure 4. As in the previous

figure, we observe a large correlation between the two real-time factors, with macro sentiment

clearly anticipating economic activity around the most important turning points. While this

divergence could be useful in a stock market predictability exercise, in the remainder of this

paper we will focus on the growth factor, which is constructed using both economic activity

and macro sentiment releases.

In the lower panel of Figure 4, we compare the inflation factor with the growth factor that

represents well its subset factors. We notice that while the two series are somewhat positively

correlated, the inflation series is much more erratic and thus represents different dynamics, as

we expected from the unconditional correlation reported in Table 1. For this reason, we will

keep the growth factor separated from the inflation factor in the remainder of the paper.

We conclude this graphical presentation of results in Figure 5, where we illustrate the

estimate of the real-time growth factor in the upper panel and the growth disagreement factor

in the lower panel. As can be readily seen, the growth index dips in the recession periods of 2001

and 2008-2009. The dispersion index exhibits the taller peaks of disagreement corresponding to

the terminal stages of the recession periods, suggesting that economists tend to disagree mostly

on the way out of the economic contractions rather than at the end of economic expansions.

4.1.1 Comparison with the CFNAI index

Recent economics and finance literature have frequently utilized the CFNAI index as an indicator

of economic conditions (e.g., Beber et al., 2011). The CFNAI index, which is based on the work

6We obtain daily data on S&P500 returns, the VIX index, the dividend yield, the price earning ratio, thedefault premium (as the difference between Moody’s BAA - AAA corporate bond yield), and the term premium(as the difference between 10-year and 3-mo Treasury yields) from Bloomberg and Datastream. We obtain ameasure of expected realized volatility ERV using 5-minute S&P500 returns computed with data from TickDataand the econometric model outlined in Section 4.3. The volatility risk premium is the difference between theVIX index and the expected realized volatility ERV .

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of Stock and Watson (1989), is preferred to the traditional NBER expansion and recession dates,

because it is generally available promptly. The CFNAI is a very interesting comparison for our

high-frequency indicator, because it is a weighted average of 85 monthly indicators and, as such,

utilizes information for a large cross-section, as we do. However, the CFNAI indicator remains

at the low monthly frequency level and does not deal with information released on different

days over the month and with missing observations.

An effective comparison needs to rely again on information that was available in real-time

and that was not subsequently revised. We thus obtain CFNAI vintages from the CFNAI

website maintained by the Chicago Fed. This is particularly important for the CFNAI indicator,

since at the time of the release, the actual value of a large part of the indicators is not available

and is projected from the last available value and subsequently restated when information

becomes available. For the same reasons, we also carefully consider the CFNAI release date.

The Chicago Fed states that CFNAI is normally released toward the end of each calendar month.

Based on the last available actual release dates, the release date is on average on the 23rd day

of the month. We thus match each monthly CFNAI release with our estimated growth factor

on the same day.

Figure 6 plots the CFNAI monthly indicator and our real-time growth factor that have been

both standardized for this subsample to facilitate the comparison. As can be readily seen, the

two series are again very similar. More specifically, the correlation between the two series is

0.94. We also notice that our growth index anticipates the turning points of the CFNAI index.

We prove this observation more formally by regressing the CFNAI index on its own lag

and the previous month’s real-time growth factor. Panel A of Table 2 shows that the growth

factor has significant predictive power for the CFNAI index, beyond lagged CFNAI. This both

surprising and reassuring, since our growth factor is constructed very differently. Rather than

specifically geared toward predicting GDP, as the CFNAI index is, our growth factor is simply

the first principle component of all growth related news. When we perform the opposite exercise

in Panel B, that is, we regress the growth factor on lagged growth and lagged CFNAI, the

lagged CFNAI is not a significant predictor. Finally, it is worthwhile reiterating that even if

our growth index did not lead the CFNAI index chronologically (which it does) but was only

contemporaneously highly correlated with it, the benefit remains that our index is observed

daily and is based on truly real time data, as opposed to being available at a monthly frequency

and based on questionably timed and potentially restated data.

4.1.2 Comparison with the ADS index

As explained in detail in the description of the methodology above, we depart from the existing

literature mainly because we are able to consider a large cross-section of the news flow, much in

the same way a professional investor would observe the daily releases of macroeconomic news.

It is then important to compare the features of our estimated factor to a more traditional real-

time factor obtained from a small set of releases but through a more complicated statistical

11

modeling framewprl.

We obtain vintages of the ADS business condition index from the website of the Federal

Reserve Bank of Philadelphia. It is crucial to use the vintages of the index as opposed to the

smooth full-sample time-series. In fact, our aim is to compare our real-time growth factor with

ADS on common grounds, that is, using calendar time for information that became actually

available to market participants. In other words, we do not want any information to matter

that was not available in real-time, especially revisions of previous announced figures.

Vintage data for the ADS index are only available starting from the end of 2008 and are

released weekly. The vintage sample (283 observations) is much shorter than the full-sample,

but it is still very informative for our comparative exercise. We thus match each weekly released

vintage of ADS with our rolling daily growth factor for the same date.

Figure 7 plots the ADS index and the growth factor that have both been standardized for

this subsample to facilitate the comparison. As can be readily seen, the two series are very

similar. More specifically, the correlation between the two series is 0.91. We also notice that

our growth index is much smoother and seems to anticipate somewhat the turning points of the

ADS index.

We prove this visual intuition more formally, when we regress the ADS index on its own lag

and the previous week’s growth index. Panel A of Table 3 shows that our real-time growth factor

has significant predictive power for the ADS index, beyond lagged ADS. When we perform the

opposite exercise in panel B, that is, we regress the growth factor on lagged growth and lagged

ADS, the lagged ADS is not a significant predictor.

In summary, in this last subsection, we have provided empirical evidence that our methodology

of extracting information from a large cross-section of news releases at daily frequency delivers

an estimate of a growth factor that is correlated with existing indices, but seems to provide

more timely and frequent information. Since our index is correlated with these other indices

that are explicitly designed to forecast GDP, checking how well our index forecasts GDP is a

logical progression of our analysis.

4.2 Forecasting actual GDP

We examine the relation between our real-time growth factor and the actual releases of GDP

figures. Unlike other approaches to obtain economic indices (e.g., the construction of the

CFNAI), our methodology does not rely on optimizing the weights on the releases to obtain

the best GDP predictions. On one hand, we would favor our unstructured approach given that

GDP is measured with error and there is some disagreement on what is the most appropriate

measure of the state of the economy at any point in time (e.g., Stiglitz et al., 2010). On the other

hand, we need to make sure that a real-time growth factor obtained with such an unstructured

approach resembles an actual economic aggregate.

We set this empirical exercise in a forecasting framework using as a benchmark for our

growth factor the nominal GDP growth quarterly forecasts from the Survey of Professional

12

Forecasters (SPF) carried out by the Federal Reserve Bank of Philadelphia. More specifically,

we use the average projection of nominal GDP annual growth rate for the current quarter that

is usually released around the end of the second month of the quarter and match it with our

growth factor for the same day. We use both the SPF projection and our growth index to

forecast the actual GDP growth for the same quarter, which will be released about one month

after the end of the quarter. For example, for the GDP in the first quarter of 1997, we use the

SPF median projection of 4.73 percent released on February 26, 1997 and our growth index of

0.7877 on the same day to forecast the actual release of GDP that occurred on April 30, 1997

at 5.60 percent.

Table 4 shows the results. We find that both the mean projection of the SPF and our real-

time growth factor contain useful information to predict the following actual GDP releases, with

large R2 of about 45 percent and 41 percent, respectively. Furthermore, the correlation between

the real-time growth factor and quarterly SPF projections is large and equal to 0.89, suggesting

that our growth index can essentially be read as an high-frequency (daily) reading of growth

expectations with very similar features of the ones obtained from the Survey of Professional

Forecasters. Figure 8 makes these points more clear in a graphical comparison, where the large

correlation between our growth factor, SPF expectations, and subsequent GDP actual releases,

can be easily appreciated. This observation is consistent with Liebermann (2001) who finds

that (a different approach to) nowcasting is comparable to the SPF at the date of release but

superior prior (when no SPF is available) and shortly after as it updates.

A related topic is the relation between our measure of real-time macroeconomic uncertainty

and the disagreement about GDP growth in the Survey of Professional Forecasters. We thus

match our indicator of dispersion of economic growth with the disagreement about current

quarter GDP growth in the SPF, both recorded on the day of the SPF release. The two series

have an unconditional correlation of 0.55 and peak at similar times on the way out of recession

phases. Although the correlation is not as strong as for the average expectation and the growth

factor, our proxy for macroeconomic uncertainty can still be interpreted as a high-frequency

reading of disagreement dynamics of professional forecasters.

4.3 Macroeconomic factors and financial volatility

One of the most intriguing aspects of our methodology is to produce a real-time daily reading

of the state of the economy that does not rely on information from financial markets, unlike

Aruoba et al. (2009) and Giannone et al. (2008). As a result, investigating the link between

the macroeconomic state and financial market dynamics is a natural next step. The existence

of this link is far from obvious. In the seminal paper of Schwert (1989), a number of volatilities

obtained from a host of macroeconomic variables and a recession dummy could explain only

a tiny part of stock market volatility for different sample periods. More recently, Engle and

Rangel (2008) define the relation between the state of the economy and aggregate financial

volatility the central unsolved problem of 25 years of research in volatility.

13

Figure 9 and 10 provide some graphical evidence of the strong link between our real-time

macro factors and financial volatility. In Figure 9, the real-time growth factor (on an inverted

scale) and VIX are almost indistinguishable. In Figure 10, our proxy for growth uncertainty

obtained from economist disagreement also correlates well with the VIX index.

In Table 5, we test this contemporaneous link more formally. More specifically, in panel

A, we regress the VIX index on the growth factor and its dispersion. Both variables are

strongly statistically significant explanatory variables for VIX, with dispersion alone explaining

six percent of VIX variation, the growth factor explaining about a quarter, and both explaining

29 percent of VIX variation.

At this point, given that the VIX index is a risk-neutral measure of expected volatility

embedding the effects of investor preferences, we decompose it into its primitive components,

namely an expected realized volatility measure and a volatility risk premium, to better understand

the origin of the macro variables explanatory power. Specifically, we obtain high-frequency

returns for the S&P500 stock index from Tickdata. We construct a measure for daily realized

variance RVt as the summation of the 78 five-minute squared returns covering the normal trading

hours, as in Bollerslev et al. (2009).7

We compute the daily variance risk premium V RPt as the difference between daily implied

variance IVt and the current expectation of RVt for the next 22 trading days. We forecast future

realized variance using information from both realized and implied volatility, as in Drechsler and

Yaron (2011). More specifically, we obtain the expectation of realized variance as the optimal

forecast of future variance based on current realized and implied volatility as observed both

at time t and on average during the last month. Using lagged volatility terms measured on

different horizons is consistent with some promising volatility forecasting Heterogeneous Auto-

Regressive models posited in Corsi (2009) and used in Corsi, Fusari, La Vecchia (2012) and

Mueller, Vedolin, and Yen (2012). Formally,

V RPt = IVt − Et [RVt,t+22] (1)

Et [RVt,t+22] =(β1IV

0.5t−22,t + β2RV

0.5t−22,t + β3IV

0.5t + β4RV

0.5t

)2, (2)

where the vector of β parameters are estimated with a rolling one-year window so as to avoid

any look-ahead bias in the following regression:

RV 0.5t,t+22 = α+ β1IV

0.5t−22,t + β2RV

0.5t−22,t + β3IV

0.5t + β4RV

0.5t + εt. (3)

This forecasting model is sufficiently parsimonious and delivers large explanatory power in

the forecast of future volatility, mainly thanks to the high-frequency measurement of realized

volatility and the degree of persistence imposed by the use of implied volatility. More involved

7As a robustness check, we also obtain high-frequency five-minute returns for the shortest maturity futurescontracts written on the same indices. The measures of realized variance obtained from futures returns arevirtually indistinguishable from the ones obtained from the underlying stock index returns.

14

specifications with further lags and volatility measured on different horizons improve the explanatory

power only marginally.

In Panel B of Table 5, we examine the volatility risk premium. The growth index and

its dispersion have explanatory power for the volatility risk premium much in the same way

they explain the VIX index. This evidence suggests that the real-time macro conditions affect

investor preferences and more specifically the compensation that investors want to receive to

take on volatility risk.

In Panel C of Table 5, we now examine the expected realized volatility. In this case, the

results are somewhat different, with growth dispersion still explaining a comparable fraction of

expected realized volatility, but with the state of the economy explaining almost half of a share

compared to the VIX index or the VRP. This analysis suggests that the state of the economy

affects preferences and, to a lesser extent, the volatility of stock returns, while macroeconomic

uncertainty has comparable influence on both preferences and realized volatility.

4.4 Forecasting the real-time growth factor

We now explore whether growth and macroeconomic uncertainty have predictive power for the

future levels of the growth factor. We plot in Figurefs 11 the median change in the growth index

at different horizons unconditionally and following states of growth dispersion above/below

median and in the top/bottom quartile. It is clear from the plot that periods of high dispersion,

and especially those in the top quartiles, are followed by large increases in the growth index in

the following days and months.

Table 6 confirms this graphical observation. Both current growth and dispersion have

statistical significant forecasting power for future growth at different horizons. While the role

of current growth in predictability is not surprising at short horizons given the persistence in

the growth factor, the significance of dispersion is intriguing. The dispersion index is already

significant at the five-day horizon and maintains its forecasting ability with increasing horizons

up to six months. The level of growth today is also strikingly important for growth up to six

months later.

4.5 Extending the sample backwards

Our sample period is constrained by the availability of real-time news releases, that is, macroeconomic

announcements for which the exact release day and time together with the released figure figure

was available. In this section, we extend our sample backward to the beginning of 1985 using

the median reporting lag for each news item and inferring the release date. Wherever possible,

we also try to avoid revisions/restatements of the original releases.

To have a better sense for the accuracy of our procedure of backward-filling based on

reporting lags, we partially cross-check our inferred release dates with a database of Reuters

news. More specifically, for a subsample of 15 announcements on the total of 43 considered

15

news items and for a shorter sample period going back to 1990, we find that 91 percent of the

estimated release days are less than two days off from the actual release days.

While in this empirical exercise, the real-time feature of our approach is weakened and thus

there is less value from an investment strategy point of view, a longer sample is desirable to

understand the dynamics of the macro factors in different economic environments. In Figure 12,

we plot our growth indicator together with NBER ex-post determined expansions and recessions

as gray-shaded areas and the expectations for current GDP growth in the quarterly Survey of

Professional Forecasters. During the recession period in 1990-1991, we observe the sudden drop

in the growth index and the subsequent gradual recovery. Furthermore, we still observe a very

large correlation with the low-frequency growth expectations in the SPF, suggesting that our

growth factor construction procedure can still be interpreted as a high-frequency reading of

professional forecaster macroeconomic views. In summary, we have suggestive indications here

that our estimate of the growth factor, even with a less reliable identification of the release day,

is effectively picking up well U.S. growth dynamics.

It would be nice to extend the sample series for our proxy for macroeconomic uncertainty

too. Unfortunately, the economist forecasts we use for the construction of our disagreement

measure are not available before 1997. For illustration purposes, we apply our methodology

to the five disagreement measures about growth in the economy (GDP, corporate profits,

employment, unemployment, and productivity growth) released quarterly in the Survey of

Professional Forecasters. After 1997, we complement these five low-frequency series with the

economist disagreement about upcoming scheduled growth releases.

Figure 13 shows the results. We first notice a large correlation between our measure of

macroeconomic uncertainty and the quarterly measure of disagreement from the SPF, meaning

that we can interpret our measure of dispersion as a high-frequency reading of professional

forecasters disagreement. Furthermore, we do notice again also in the earlier part of the sample

that uncertainty seems to peak on the way out of recessions and is subdued at the end of

expansion phases, confirming the pattern observed in the last 15 years.

5 Conclusions

We use a novel and extremely simple technique to extract daily latent factors from macroeconomic

releases available at different times and frequencies. This approach can effectively handle the

large cross-section of announcements that are relevant to track macroeconomic conditions. Our

methodology is implemented in real-time and is used to extract measures of inflation, output,

employment, and macroeconomic sentiment, as well as corresponding measures of disagreement

among economists about these indices.

We find that our procedure improves on existing approaches as it provides a more accurate

and timely measurement of the state of the economy. In contrast to the extant literature,

the real-time growth factor and its disagreement constructed using our approach using only

macroeconomic information explains a remarkable fraction of financial volatility dynamics.

16

6 Appendix: Macroeconomic News

The following table summarizes the main features of the macroeconomic releases considered

in our sample. Category is either inflation (INF), employment (EMP), output (OUT), or

sentiment (SEN). If the sample series is stationary in our sample, we make no adjustment

(Adj.=0), otherwise we use first differences with respect to previous period (Adj.=1). We also

indicate Units, Frequency (M for monthly, W for weekly, Q for quarterly), and the source of

the release.

Category Name Macro Release Adj. Units Freq Source

INF US Import Price Index by End Use All MoM 0 Rate M Bureau Labor Statistics

INF US PPI Finished Goods Total MoM 0 Rate M Bureau Labor Statistics

INF US PPI Finished Goods Except Foods Energy 0 Rate M Bureau Labor Statistics

INF US CPI Urban Consumers MoM 0 Rate M Bureau Labor Statistics

INF US CPI Urban Consumers Less Food Energy 0 Rate M Bureau Labor Statistics

INF BLS Employment Cost Civilian Workers QoQ 0 Rate Q Bureau Labor Statistics

INF US GDP Price Index QoQ SAAR 0 Rate Q Bureau Economic Analysis

INF US Personal Cons. Expenditure Core Price Index MoM 0 Rate M Bureau Economic Analysis

INF US Output Per Hour Nonfarm Business Sector QoQ 0 Rate Q Bureau Labor Statistics

EMP ADP National Employment Report Private Nonfarm Change 0 Volume M Automatic Data Processing

EMP US Initial Jobless Claims 1 Volume W Department of Labor

EMP US Continuing Jobless Claims 1 Volume W Department of Labor

EMP US Employees on Nonfarm Payrolls Total Net Change 0 Value M Bureau Labor Statistics

EMP US Employees on Nonfarm Payrolls Manufact Net Change 0 Value M Bureau Labor Statistics

EMP US Unemployment Rate Total in Labor Force 1 Rate M Bureau Labor Statistics

EMP US Average Weekly Hours All Total Private 1 Volume M Bureau Labor Statistics

OUT ISM Manufacturing PMI 0 Value M Institute Supply Management

OUT US Manufacturers New Orders Total MoM 0 Rate M U.S. Census Bureau

OUT US Auto Sales Domestic Vehicles 1 Volume M BLOOMBERG

OUT ISM Non-Manufacturing NMI NSA 0 Value M Institute Supply Management

OUT Federal Reserve Consumer Credit Net Change 1 Value M Federal Reserve

OUT Merchant Wholesalers Inventories Change 0 Rate M U.S. Census Bureau

OUT Adjusted Retail Food Services Sales Change 0 Rate M U.S. Census Bureau

OUT Adjusted Retail Sales Less Autos Change 0 Rate M U.S. Census Bureau

OUT US Industrial Production MoM 2007=100 SA 0 Rate M Federal Reserve

OUT US Capacity Utilization of Total Capacity 0 Rate M Federal Reserve

OUT US Manufacturing Trade Inventories Total 0 Rate M U.S. Census Bureau

OUT US Durable Goods New Orders Industries 0 Rate M U.S. Census Bureau

OUT US Durable Goods New Orders Ex Transp. 0 Rate M U.S. Census Bureau

OUT GDP US Chained 2005 Dollars QoQ SAAR 0 Rate Q Bureau Economic Analysis

OUT GDP US Personal Consumption Chained Change 0 Rate Q Bureau Economic Analysis

OUT US Personal Income MoM 0 Rate M Bureau Economic Analysis

OUT US Personal Consumption Expend. Nominal Dollars 0 Rate M Bureau Economic Analysis

SEN Bloomberg US Weekly Consumer Comfort Index 1 Price W BLOOMBERG

SEN University Michigan Survey Consumer Confidence 1 Price M U. of Michigan Survey Research

SEN Empire State Manufact. Survey Business Conditions 1 Value M Federal Reserve

SEN Conference Board US Leading Index MoM 0 Rate M Conference Board

SEN Philadelphia Fed Business Outlook General Conditions 1 Price M Philadelphia Fed

SEN Conference Board Consumer Confidence SA 1985=100 1 Rate M Conference Board

SEN Richmond Fed Reserve Manufacturing Survey 0 Rate M Richmond Fed

SEN US Chicago Purchasing Managers Index SA 1 Price M Kingsbury Intern.

SEN ISM Milwaukee Purchasers Manufacturing Index 1 Rate M NAPM - Milwaukee

SEN Dallas Fed Manufact. Outlook Business Activity 1 Rate M Dallas Fed

17

18

Tab

le1:

Su

mm

ary

Sta

tist

ics

Pan

elA

show

sco

rrel

atio

ns

bet

wee

nd

iffer

ent

aggre

gate

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macr

oec

onom

icn

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as

econ

om

icin

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fore

cast

dis

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sion

.P

an

elB

rep

ort

s

add

itio

nal

sum

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yst

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tics

and

corr

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sb

etw

een

the

gro

wth

ind

ex,

the

gro

wth

dis

per

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,an

da

set

of

fin

an

cial

vari

ab

les.

Sp

ecifi

call

y,

Rm

t−R

ft

den

otes

the

log

retu

rnon

the

S&

P500

inex

cess

of

the

3-m

oT

-bil

lra

te,IV

isth

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IXin

dex

,ERV

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pec

tati

on

of

reali

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ries

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ith

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dre

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five

-min

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rns,VRP

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sk

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2,

logP

t

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and

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t

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of

the

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oth

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,TERM

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(10-y

ear

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ple

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Jan

uar

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97to

Dec

emb

er20

11.

19

Pan

elA

:

Ind

exD

isp

ersi

on

Infl

Sent

EconAct

Growth

Infl

Sent

EconAct

Growth

Ind

ex:

Infl

1.00

0.1

40.2

50.2

2-0

.08

-0.1

8-0

.19

-0.2

0

Sent

1.0

00.8

20.9

3-0

.50

-0.0

70.0

60.0

5

EconAct

1.0

00.9

7-0

.55

-0.1

8-0

.23

-0.2

3

Growth

1.0

0-0

.56

-0.1

5-0

.13

-0.1

4

Dis

per

sion

:

Infl

1.0

00.1

40.2

50.2

5

Sent

1.0

00.4

20.5

4

EconAct

1.0

00.9

9

Growth

1.0

0

20

Pan

elB

:

Rm

t−R

ft

GRO

DISP

VRP

VIX

RV

log

(P E

)lo

g(D P

)DEF

TERM

Su

mm

ary

stati

stic

s

Mean

0.63

-0.0

4-0

.00

0.0

40.2

30.1

42.9

80.5

51.0

31.6

8

Std

ev.

21.4

21.

150.9

90.0

50.0

90.0

50.2

30.2

50.4

81.3

0

Skew

-0.2

0-1

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1.4

04.9

61.7

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70.0

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rt9.

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974.1

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Corr

elati

on

matr

ix

Rm

t−R

ft

1.00

0.01

0.0

1-0

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-0.1

3-0

.09

0.0

4-0

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-0.0

10.0

1

GRO

1.00

-0.1

4-0

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-0.5

1-0

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0.5

5-0

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-0.8

4-0

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DISP

1.0

00.1

50.2

50.2

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90.1

40.0

2

VRP

1.0

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4-0

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50.1

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VIX

1.0

00.7

0-0

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0.3

10.6

30.2

2

RV

1.0

00.1

10.0

70.3

90.1

2

log

(P/E

)1.0

0-0

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-0.5

2-0

.28

log

(D/P

)1.0

00.6

90.4

2

DEF

1.0

00.4

0

TERM

1.0

0

21

Table 2: Comparing CFNAI with our Real-Time Growth Index

This table shows estimates of the following equation

Yt = α+ β1Yt−1 + β2Xt−1 + εt,

where Yt is the CFNAI index and Xt is our real-time growth index (Panel A) and Yt is our real-time growth

index and Xt is the CFNAI index (Panel B). Robust Newey-West t-statistics are reported in parentheses.

Panel A: CFNAI on Real-Time Growth Index

Dep. Var.: CFNAI 1 2

Constant -0.03 -0.08

(-1.89) (-2.08)

CFNAIt−1 0.91 0.41

(17.30) (3.68)

REALTIMEt−1 0.39

(4.25)

Adj.R2(%) 83.56 86.86

Panel B: Real-Time Growth Index on CFNAI

Dep. Var.: REALTIME 1 2

Constant -0.01 -0.01

(-0.74) (-0.48)

REALTIMEt−1 0.96 0.93

(24.79) (13.31)

CFNAIt−1 0.05

(0.52)

Adj.R2(%) 91.77 91.72

22

Table 3: Comparing ADS with our Real-Time Growth Index

This table shows estimates of the following equation

Yt = α+ β1Yt−1 + β2Xt−1 + εt,

where Yt is the ADS index and Xt is our real-time growth index (Panel A) and Yt is our real-time growth

index and Xt is the ADS index (Panel B). Robust Newey-West t-statistics are reported in parentheses.

Panel A: ADS on Real-Time Growth Index

Dep. Var.: ADS 1 2

Constant -0.02 0.04

(-1.78) (2.11)

ADSt−1 0.95 0.76

(52.98) (21.98)

REALTIMEt−1 0.12

(4.90)

Adj.R2(%) 91.43 92.18

Panel B: Real-Time Growth Index on ADS

Dep. Var.: REALTIME 1 2

Constant 0.01 0.01

(0.41) (0.78)

REALTIMEt−1 0.99 1.01

(165.45) (68.46)

ADSt−1 -0.02

(-1.18)

Adj.R2(%) 99.61 99.61

23

Table 4: Forecasting GDP

This table shows estimates of the following equation:

GDPt = α+ β1GDPt−1Q + β2Xt−2mo + εt,

where GDPt are actual GDP quarterly releases, GDPt−1Q is the previous GDP quarterly release, and Xt−2mo

is either the average GDP nominal growth quarterly forecast of the Survey of Professional Forecasters (SPF)

or our real-time growth factor, both observed on the same day about two months before the GDP release.

Robust Newey-West t-statistics are reported in parentheses.

Dep. Var.: GDPt 1 2 3 4

Constant -0.61 -0.15 -0.11 -0.08

(-2.75) (-0.99) (-0.60) (-0.53)

GDPt−1Q 0.22 0.06 0.04 0.03

(4.14) (1.10) (0.68) (0.57)

SPFt−2mo 0.57 0.44

(3.25) (1.68)

GROWTHt−2mo 0.58 0.20

(2.73) (0.75)

Adj.R2(%) 30.28 44.60 41.18 44.25

24

Table 5: The Growth Factor, its Dispersion, and VIX, VRP, E(RV)

This table shows estimates of the following equation

Yt = α+ β1DISPt + β2GROWTHt + εt,

where Yt is either V IXt (Panel A), V RPt (Panel B) and Et [RVt+1,t+22] (Panel C), respectively. In column

(3), each of the variable is orthogonalized with respect to the other. All of the regression are based on

daily observations. The sample period extends from January 1997 to December 2011. Robust Newey-West

t-statistics are reported in parentheses.

Panel A: VIX1 2 3

Constant 0.2269 0.2252 0.2253

(34.4636) (39.4552) (40.7793)

DISPt 0.0217 0.0221

(3.4952) (4.8659)

GROWTHt -0.0387 -0.0394

(-5.7853) (-5.8185)

Adj.R2(%) 5.9840 25.6984 28.8520

Panel B: VRP1 2 3

Constant 0.0582 0.0571 0.0571

(13.9758) (16.6792) (16.9750)

DISPt 0.0102 0.0104

(2.7046) (3.9473)

GROWTHt -0.0245 -0.0249

(-4.4586) (-4.4493)

Adj.R2(%) 3.2898 25.6129 26.9065

25

Panel C: ERV1 2 3

Constant 0.0304 0.0302 0.0303

(38.2076) (40.4112) (41.1817)

DISPt 0.0022 0.0023

(2.8041) (3.2592)

GROWTHt -0.0035 -0.0036

(-4.7526) (-4.9391)

Adj.R2(%) 4.0389 13.6034 15.9232

26

Table 6: Growth Predictability Regressions

This table shows estimates of the following equation:

GROWTHt+lead = α+ ρGROWTHt + βDISPt + εt,

where Growtht is the real-time growth factor and DISPt denotes the dispersion of analyst expectations about

macroeconomic growth releases. All of the regression are based on daily observations. We do not report

α to save space. The sample period extends from January 1997 to December 2011. Robust Newey-West

t-statistics are reported in parentheses.

lead (days) 5 20 40 60 80 100 120

Growtht 0.9954 0.9737 0.9383 0.8956 0.8456 0.7943 0.7422

(289.8041) (72.7546) (32.6357) (20.0954) (14.3388) (11.1513) (9.1673)

DISPt 0.0097 0.0417 0.0843 0.1179 0.1576 0.1902 0.2188

(3.1894) (3.6303) (3.8682) (3.8856) (3.9698) (4.0097) (3.9724)

Adj.R2(%) 98.9051 94.1737 87.0181 79.1716 70.7449 62.7566 55.4122

Adj.R2(%)AR1 98.8985 94.0485 86.5029 78.1608 68.9290 60.1036 51.8990

27

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29

Conference Board Consumer ConfidenceChicago Purchasing Managers Index Inflation

University Michigan Consumer Survey EmploymentADP National Employment Report Output

ISM Manufacturing PMI Macro SentimentNonfarm Payrolls Total,Manufacturing + Unemployment Rate + Average Weekly HoursISM Non-Manufacturing PMI

Retail Sales + Retail Sales Less AutoImport Price Index

PPI + PPI CoreIndustrial Production + Capacity UtilizationEmpire State Manufacturing Survey

Manufacturing Trade InventoriesCPI + CPI Core

Durable Goods OrdersConference Board Leading Index

GDP + GDP Price IndexPersonal Income + Pers. Consum. Exp. + PCE Price Index

Manufacturers New Orders

24 26 28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3 5 7 9 11 13 15 17 19 21 23referencemonth M month M+1 month M+2

Figure 1: This figure shows the typical reporting structure for a large cross-section ofU.S. macroeconomic announcements. On the horizontal axis, we represent the days of thereference month M and the subsequent two months. On the vertical axis, we list themacroeconomic releases in order of reporting, highlighting in bold the typical reporting period.The macroeconomic announcements are color-coded in the four aggregates of inflation news,employment news, output news, and macro-sentiment news.

30

j=1 j=2 … j=5 j=6 … j=N1 ... ... ... missing ... ... ...

… ... ... ... missing ... ... ...

… ... ... ... missing ... ... ...

t-22 At-22,1 not released ... missing not released ... ...

t-21 not released At-21,2 ... missing At-21,6 ... ...

… not released not released ... missing not released ... ...

t At,1 not released ... At,5 not released ... ...

t+1 not released At+1,2 ... not released At+1,6 ... ...

… not released not released ... not released discontinued ... ...

… ... ... ... ... discontinued ... ...

T ... ... ... ... discontinued ... ...

j=1 j=2 … j=5 j=6 … j=N1 ... ... ... missing ... ... ...

… ... ... ... missing ... ... ...

… ... ... ... missing ... ... ...

t-22 At-22,1 E[At-22,2]=At-43,2 ... missing E[At-22,6]=At-43,6 ... ...

t-21 E[At-21,1]=At-22,1 At-21,2 ... missing At-21,6 ... ...

… E[A ...,1]=At-22,1 E[A...,2]=At-21,2 ... missing E[A...,2]=At-21,6 ... ...

t At,1 E[A t,2]=At-21,2 ... At,5 E[A t,2]=At-21,6 ... ...

t+1 E[At+1,1]=At,1 At+1,2 ... E[At+1,5]=At,5 At+1,6 ... ...

… E[A ...,1]=At,1 E[A ...,2]=At+1,2 ... E[A ...,5]=At,5 discontinued ... ...

… ... ... ... ... discontinued ... ...

T ... ... ... ... discontinued ... ...

Figure 2: This figure shows a stylized example of the actual macroeconomic announcement data,for N announcement types over a daily sample period between 1 and T . The releases j = 1 andj = 2 are monthly indicators released on two different days of the month. The macroeconomicindicator j = 5 is a news release that did not exist at the beginning of the sample, but wasincluded in the sample from day t onwards. The macroeconomic indicator j = 6 did exist atthe beginning of the sample, but was subsequently discontinued. The top panel represents thematrix of the actual macroeconomic releases in real-time as it is constructed from the data. Thebottom panel shows how our simple forward filling algorithm is used to fill in the expectationof the indicator when it is not released.

31

1997 1999 2001 2003 2005 2007 2009 2011-5

-4

-3

-2

-1

0

1

2Output and Employment

Figure 3: The graph shows the real-time output (blue thicker line) and employment factor (redline). Grey areas denote NBER recessions.

32

1997 1999 2001 2003 2005 2007 2009 2011

-4

-3

-2

-1

0

1

2Economic Activity and Macro Sentiment

1997 1999 2001 2003 2005 2007 2009 2011

-5

-4

-3

-2

-1

0

1

2

3

Growth and Inflation

Figure 4: The upper panel shows the real-time economic activity (blue thicker line) and macrosentiment (red line) factors. The lower panel plots the real-time growth (blue thicker line) andinflation (red line) factors. Grey areas denote NBER recessions.

33

1997 1999 2001 2003 2005 2007 2009 2011

-4

-3

-2

-1

0

1

2Growth Index

1997 1999 2001 2003 2005 2007 2009 2011-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5Dispersion

Figure 5: The upper panel shows the real-time growth factor. The lower panel is the dispersion ofeconomist’ forecasts about upcoming growth news releases. Grey areas denote NBER recessions.

34

2001 2003 2005 2007 2009 2011-4

-3

-2

-1

0

1

2CFNAI and Growth Index

CFNAIGrowth

Figure 6: This figure shows the real-time growth factor GROWTH and the real-time CFNAIindex, both observed at the monthly frequency on the same day during the sample periodFeb-2001 to Dec-2011.

35

2008.12 2009.6 2009.12 2010.6 2010.12 2011.6 2011.12-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2ADS and Growth Index

ADSGrowth

Figure 7: This figure shows our real-time growth factor GROWTH and the correspondingvintages of the ADSindex during the sample period Dec-2008 to Dec-2011.

36

1997 1999 2001 2003 2005 2007 2009 2011

-4

-3

-2

-1

0

1

2

3GDP, SPF, and real-time Growth Index

GDPactSPFGROWTH

Figure 8: In this figure we plot the estimate of the latent growth factor GROWTH, the medianprojection of nominal GDP growth rate from the Survey of Professional Forecasters (SPF) onthe same dates, and the actual GDP release for the same quarter, at quarterly frequency duringthe sample Jan-1997 to Dec-2011.

37

1997 1999 2001 2003 2005 2007 2009 2011

-4

-2

0

2

GR

OW

TH (i

nver

ted

scal

e)

1997 1999 2001 2003 2005 2007 2009 20110.05

0.2

0.35

0.5

0.65

VIX

Figure 9: In this figure we plot the estimate of the latent growth factor GROWTH (invertedleft-scale) and VIX (right-scale) during our sample period.

38

1997 1999 2001 2003 2005 2007 2009 2011-2

-1

0

1

2

DIS

P

1997 1999 2001 2003 2005 2007 2009 20110.05

0.2

0.35

0.5

0.65

VIX

Figure 10: In this figure we plot the dispersion of economists’ forecast about growth newsreleases (left-scale) and VIX (right-scale) during our sample period.

39

0 10 20 30 40 50 60 70 80 90-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

horizon (days)

Forecasting Growth Index Returns

aboveMEDbelowMEDtopQbotQunconditional

Figure 11: This figure shows the median first difference in the growth index for different horizons(in days), unconditionally and conditionally on current dispersion about the growth rate beingabove (below) the sample median and in the top (bottom) quartile.

40

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011-5

-4

-3

-2

-1

0

1

2

Growth (1985-2011)

Figure 12: This figure shows our estimated growth index for the U.S. economy constructed oneconomic releases backfilled to January 1985. The red line indicates the quarterly expectation ofGDP growth for the current quarter contained in the Survey of Professional Forecasters. Greyareas denote NBER recessions.

41

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011-2

-1

0

1

2

3

4

Dispersion and SPF Disagreement (1985-2011)

Figure 13: This figure shows our daily factor for the dispersion of economist growth forecasts(in red) and the disagreement measure in the quarterly Survey of Professional Forecasters (inblue). Grey areas denote NBER recessions.

42