\linespread{1.2}
\chapter{Do Shocks to Animal Spirits Cause Output Fluctuations?}


\section{Introduction}\label{intro}
The notion that confidence on the part of economic agents, be they consumers or firms, is an important source of economic fluctuations is an old one in economics.  In \textit{The General Theory of Employment, Interest, and Money}, Keynes (1936) writes, ``Most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as a result of animal spirits - of a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities."  Despite the venerable nature of ``animal spirits", however, it is not easy to precisely pinpoint their role in the economy's dynamics.  Largely, this is because they are obviously unobservable and deeply entangled with other factors that are likely also to play a strong role, such as the arrival of news about expected future events.

Recently, the macroeconomic literature has revisited this question in both empirical and theoretical studies.  Much of the theoretical literature has attempted to formalize animal spirits in the context of otherwise fairly standard models.  The transmission mechanisms in these models differ, but they all seek to describe how agents' decision making can be influenced by forces orthogonal to economic fundamentals.  These decisions, in turn, generate fluctuations in economic aggregates that may have appeared intuitive to Keynes.  Alternatively, it has still proven difficult empirically to convincingly identify the effects of these innovations in animal spirits.

In this paper, we attempt to quantify the importance of animal spirits for changes in economic activity.  Using monthly data on economic activity and consumer confidence at the level of U.S. states, we will formally identify shocks to animal spirits that are orthogonal to shocks to economic fundamentals.  We will do so in the context of a structural vector autoregression (SVAR), making use of long run and sign restrictions, just as in the seminal work of Blanchard and Quah (1989). Our SVAR will include consumer confidence and a state-level measure of output, and we will impose that innovations in animal spirits cannot permanently affect output and that, contemporaneously, their effect ought to be positive..  We demonstrate that animal spirits shocks do have a statistically significant effect on output  and consumption (proxied by retail sales) in the medium term, but that the bulk of fluctuations is still accounted for by innovations in fundamentals (which we will refer to as news shocks).

Our paper is among the first to empirically evaluate the effects of animal spirits shocks on real macroeconomic variables.  An important earlier work is that of Barsky and Sims (2012), who also use VAR analysis to identify shocks to consumer confidence.  They employ short run restrictions in a VAR that includes output and consumption as well as consumer confidence.  By incorporating both news shocks and animal spirits shocks in a standard New Keynesian dynamic stochastic general equilibrium (DSGE) model, they conclude that the innovations they identify in the data behave more like fundamental shocks.\footnote{Interestingly, Bachmann and Sims (2012) find that consumer confidence is an important transmission mechanism of shocks to government purchases, although only in recessions.}

Still, the literature that seeks to develop a formal theoretical link between changes in confidence or sentiment and fluctuations in real variables is rapidly expanding.  We discuss a couple of recent examples. Aangeletos and La'O (2013) develop a model in which agents engage in pair-wise trading and the parties to a given pairing receive exogenous sunspot-like signals.  An agent's signal contains noisy information about her trading partner's productivity as well as information about her trading partner's signal about her own productivity.  With production decisions made before all information is revealed, such information frictions can then lead to aggregate fluctuations. Aangeletos and La'O (2013) define ``sentiments," which we interpret as being fundamentally equivalent to animal spirits, as the cross-sectional average of expectations of agents about the productivity levels of their trading partners.\footnote{It is possible to extend the model of Aangeletos and La'O (2013) to take account of higher order beliefs of the agents in the economy about their trading partner's productivity levels as well as their beliefs about their trading partners' beliefs about their beliefs and so on.  This is the approach taken by Huo and Takayama (2015a) and Huo and Takayama (2015b).}   In a similar setting (and one that is also reminiscent of the classic island model of Lucas (1972), Benhabib, Wang, and Wen (2015) build an economy in which households receive shocks to their expectations about the aggregate economy (the sentiments shock) and to their preferences for differentiated goods.  Island firms then must consider a signal extraction problem, in which fluctuations in the demand for their output are in part due to households' changing tastes over goods and in part due to their changing views of the economy as a whole.  Like in the case of Aangeletos and La'O (2013), the result is that changes in optimism or pessimism of agents can generate business cycles.\footnote{Other important works include Benhabib, Wang, and Wen (2013), Lorenzoni (2009), Schmitt-Grohe and Uribe (2012), and Blanchard, L'Huillier, and Lorenzoni (2013).}  In neither case is there a departure from rationality required, a common approach taken to formalize animal spirits in the past.  What is interesting is how these model predictions are, in general, not confirmed by the empirical work of Barsky and Sims (2012).

We will provide evidence that affirms a role for animal spirits in real macroeconomic fluctuations.  In particular, we show that a positive innovation in animal spirits causes the level of economic activity to be about three percent higher after two years, and it takes nearly five years for the effect of the animal spirits shock on output to wear off.  In contrast, however, an innovation in fundamental economic activity itself leads to a ten percent increase in the level of output over a similar time horizon.  The effect on consumption, as proxied by retail sales, of a shock to animal spirits is also significantly positive for about 18 months, but is less persistent than the effect of a fundamental shock and is less persistent than the effect of animal spirits on overall activity.  

The rest of this paper proceeds as follows.  Section \ref{data_section} discusses the data that we use for our analysis, in particular our measure of consumer confidence provided by The Conference Board.  Section \ref{methodology} explains our econometric approach.  Section \ref{results_section} reports and interprets the results of our analysis, and Section \ref{conclusion} concludes.

\section{Data}\label{data_section}

Our data consists primarily of three primary variables, which we collect at the U.S. state level.  These are an index of consumer confidence, the state coincident indexes compiled by the Federal Reserve Bank of Philadelphia, and a measure of retail sales at the state level.  We discuss each of these in turn.  

\subsection{Consumer Confidence}\label{consumer_confidence_data}

We obtain data on consumer confidence from the Conference Board, who construct their index using their Consumer Confidence Survey$^{\textregistered}$.  Each month, respondents are asked to assess current business conditions, current employment conditions, expectations for future business conditions (meaning business conditions six months in the future), expectations for future employment conditions (with the future defined in the same way), and expectations for future family income.  For each category, they must evaluate their perspective as positive, negative, or neutral.  Then, the proportion responding positively is divided by the proportion responding positively or negatively for each category, and this ratio is averaged across the five categories to make up the consumer confidence index.  For further details, see The Conference Board (2011).\footnote{Additionally, the Conference Board constructs subindexes based on the appraisals for the first two categories, which make up the ``Present Situation" index and on the answers for the last three categories, which form the ``Expectations" index.  We focus on the main Consumer Confidence Index which derives from the answers to all five questions.}  

Importantly for our purposes, the Conference Board does not only construct their consumer confidence index at the level of the United States, but also at the regional level and stratified by age.  Table \ref{regionslist} reports the states that each region comprises.  We have observations on region-level consumer confidence from January 1981 to December 2014.  In order to turn these regional consumer confidence indexes into state consumer confidence indexes, we make use of the separate indexes created for three age groups.  Specifically, the Conference Board builds indexes for households where the household head is under the age of 35, is between the ages of 35 and 54 , and is 55 and older.  Let $Conf_{i}$ denote the consumer confidence index for age group $i \in \{young,\ middle,\ old\}$, with $young$ referring to the under-35 age group, $middle$ referring to the 35-54 age group, and $old$ referring to the over-54 age group.\footnote{In this exposition, for notational convenience, we suppress the time subscripts.}  We combine these age-specific indexes with information from the U.S. Statistical Abstract on the age profile of each state's population to create a state index that is a weighted average of the age-specific indexes, where the weights are determined by the individual state's age profile.\footnote{We note that information on the age profile of each state is available for the years 1980, 1990, 2000, 2005, and 2010.  We impose that the age profile remains the same each year until an update is available.  That is, the age profile for 2003 is assumed to be the same as it was in 2000, but it updates in 2005.}  That is, the age-based consumer confidence index for state $j$ is:
\begin{equation}\label{state_cci_1}
Conf_{age,j} = \rho_{young} Conf_{young} + \rho_{middle} Conf_{middle} + \rho_{old} Conf_{old}\ ,
\end{equation}
where the $\rho$'s are the share of state $j$'s population belonging to each age group.  We then take a population-weighted average of these state-level, age-based consumer confidence indexes by region.  If there are $J$ states in region $k$, this measure is:
\begin{equation}\label{state_cci_2}
Conf_{age,k} = \sum_{j=1}^{J} \beta_j Conf_{age,j}\ .
\end{equation}
Let $Conf_{CB,k}$ denote the published regional level consumer confidence index produced by the Conference Board.  We construct a scaling factor, $\theta_k$, such that:
\begin{equation}\label{state_cci_3}
\theta_k = \frac{Conf_{CB,k}}{Conf_{age,k}}\ ,
\end{equation}
and the final state-level consumer confidence index for state $j$ is
\begin{equation}\label{state_cci_4}
Conf_{j} = \theta_k Conf_{age,j}\ .
\end{equation}
This is the measure that we will be employing in our analysis.\footnote{In practice, the state-level consumer confidence indexes generally do not differ that much from the coincident region-level consumer confidence index.  This is because, within a region, differences in states' age profiles tend to be fairly modest.}  Figure \ref{cci_graphs} produces the computed state-level consumer confidence index for nine states, one from each of the regions for which the Conference Board produces a consumer confidence index, with each region represented by the state listed first alphabetically.  Table \ref{summstat} contains the summary statistics for our consumer confidence measure.

\subsection{State Coincident Indexes}\label{cei_data}

Our chosen measure of economic activity at the level of U.S. states is the Coincident Economic Index compiled by the Federal Reserve Bank of Philadelphia.\footnote{We will use the term ``economic activity" index also to refer to this measure.}  The interested reader should consult the Philadelphia Fed's website for details, but this measure applies the latent dynamic factor model of Stock and Watson (1989) to state-level economic data.  Such an application was popularized in Crone and Clayton-Matthews (2005).  The model assumes that the state of economic activity in a given U.S. state can be described by a latent factor that summarizes the comovement of four variables, which are nonfarm payroll employment, the unemployment rate, average manufacturing labor hours, and real wages and salaries.  Clearly, this index is dominated by labor market variables,\footnote{Using a modified form of this technique, Arias, Gascon, and Rapach (2015) construct similar monthly indexes for large metropolitan areas in the United States, and they make use of a wider range of indicators than merely labor market figures.} but these indexes have a number of attractive features for us.  In particular, they are collected at monthly frequency (matching the frequency of our consumer confidence data) and the data is available from January of 1979, encompassing the time dimension of our confidence data.  Also, labor markets variables tend to be highly correlated with general economic activity and are of great interest to policymakers.  For all of these reasons, we believe that this is a good measure to use for our state-level analysis.

Figure \ref{cei_graphs} plots the annualized growth rate in this index for the same nine states for which we plotted the consumer confidence index.  This figure helps demonstrate the advantages inherent in exploiting data at the U.S. state level, as opposed to only considering aggregate U.S. macroeconomic data.  The figures reveal substantial variation in annualized growth in economic activity cross-sectionally.  For example, while most of the states displayed in the figure experienced a very deep recession along with the rest of the United States in the early 1980s, Alaska doesn't fall into recession until the mid-1980s (when oil prices began to fall).  In some states, the recent recession associated with the global financial crisis was the most severe in the sample period, but in others, it is eclipsed by the early 1980s recessions.  Arizona witnesses a tremendous spike in growth in the mid-1980s, while, in the early 1990s, growth in Arkansas does not turn negative as it does in many other states.  Such patterns have also been noted by, among others, Owyang, Piger, and Wall (2005) and Owyang, Rapach, and Wall (2009).

Summary statistics for the annualized growth rate of the state coincident economic index are also found in Table \ref{summstat}.  Figure \ref{connecticut_both} simultaneously displays the consumer confidence index and annualized growth in the coincident economic index for one state, Connecticut.  The two series apparently track each other fairly closely, although consumer confidence lags economic activity somewhat.            

\subsection{Retail Sales}\label{retailsales_data}

We also collect data on retail sales at the state level and at quarterly frequency.  We consider retail sales as a useful proxy for consumption, especially since consumption data is not readily available for states at such frequency.\footnote{We note that the Bureau of Economic Analysis has recently introduced data on Personal Consumption Expenditures for U.S. states, but these data are at annual frequency, which makes them an imperfect fit for our analysis, which attempts to exploit relatively high frequency variation in consumer confidence and real economic variables.}  In order to construct series on retail sales for U.S. states, we follow the work of Garrett, Hernandez-Murillo, and Owyang (2004), who use quarterly data on state sales tax revenue, which comes from the U.S. Census Bureau.  We also collect data on state sales tax rates from a variety of sources, including the Census Bureau, the Book of the States, and the Tax Foundation.  We compute state retail sales as the sales tax revenue divided by the sales tax rate for a given state in a given quarter.  Our retail sales data extends from 1994:Q1 to 2014:Q4, and we have data for 42 states.\footnote{As noted by Garrett, Hernandez-Murillo, and Owyang (2004), there are five states that do not have a sales tax, so we cannot compute an estimate of state retail sales for these.  They include Alaska, Delaware, Montana, New Hampshire, and Oregon.  Further, data for Nevada, Utah, and Wyoming was not of high quality, so we drop these states as well.}  We deflate the retail sales series by the national-level consumer price index.  Garrett, Hernandez-Murillo, and Owyang (2004) show that a when the inferred state retail sales data are summed up cross-sectionally, they have a correlation coefficient of 0.975 with national retail sales data collected by the U.S. Census. 

Summary statistics for annualized growth in retail sales can be found in the third line of Table \ref{summstat}, and Figure \ref{retail_graphs} contains plots of the time series of annualized retail sales growth for those states for which we have data on retail sales and for which we have already produced plots for the consumer confidence and state coincident economic index series.

\section{Econometric Methodology: Structural VAR}\label{methodology}

Our analysis begins with the identification of shocks to animal spirits and to fundamental economic variables, which we will refer to as ``news."  This is motivated by the work of Barsky and Sims (2012), who also distinguish between innovations to expectations about future economic activity (``news") and innovations to consumer confidence that are otherwise unrelated to real economic variables.  Unlike Barsky and Sims (2012), who use VARs with short-run restrictions to inform their DSGE model, we will make use of long-run restrictions in our VAR, following the model of Blanchard and Quah (1989).  Specifically, we will restrict the animal sprits innovations to have only transitory effects on the level of output.

Our structural VAR will include two variables, the state-level consumer confidence index that we modify from the region-level confidence index collected by the Conference Board (with $Conf_{i,t}$ denoting the consumer confidence index in state $i$ at time $t$) and the annualized growth rate in the state coincident economic index constructed by the Federal Reserve Bank of Philadelphia (with $\Delta y_{i,t}$ denoting the annualized growth rate in the economic activity index in state $i$ at time $t$), each observed at a monthly frequency.  Consider the following reduced form VAR representation of these two variables:\footnote{Much of the notation to follow is borrowed from Enders (2010).} 
\begin{equation}\label{lrvar}
\begin{bmatrix}
Conf_{i,t} \\
\Delta y_{i,t}
\end{bmatrix}
=
\begin{bmatrix}
A_{11}(L) & A_{12}(L) \\
A_{21}(L) & A_{22}(L)
\end{bmatrix}
\begin{bmatrix}
Conf_{i,t-1} \\
\Delta y_{i,t-1}
\end{bmatrix}
+
\begin{bmatrix}
e_{1,i,t} \\
e_{2,i,t}
\end{bmatrix}
\end{equation}
We assume that the reduced form residuals $[e_{1,i,t},e_{2,i,t}]'$ are in fact linear combinations of two underlying structural shocks, which we interpret as shocks to animal spirits and to economic fundamentals or expectations about economic fundamentals.  We call this latter innovation ``news."  Our objective is to uncover from these reduced form VAR residuals the structural innovations in animal spirits and in news.  To do so, we will apply the following critical identification assumption:

\textbf{Assumption 1}:  The structural innovation in animal spirits must not have an effect on the level of economic activity in the long run.  That is, fluctuations in economic activity caused by animal spirits shocks must be transitory in nature.

The motivation for this assumption draws heavily from Blanchard and Quah (1989).\footnote{See also Basu, Fernald, and Kimball (2006) and Christiano, Eichenbaum, and Vigfusson (2004).}  In that paper, the authors assumed that, in the long run, shocks to aggregate demand could not have an impact on the level of output.  Only shocks altering the productive capacity of the economy could do that.  Similarly, we assert that shocks to animal spirits, the ``spontaneous urge to action," in the parlance of Keynes (1936), cannot permanently change the long run level of output.  Econometrically, it is more or less trivial to identify innovations that have only transitory effects on output and call them ``animal spirits."  Intuitively, it may be worth delving a little deeper into this assumption.

We argue that such flights of optimism that we call animal spirits are likely to wear off with time and as information about the true state of the economy is revealed.  That is, if a particular household suddenly (irrationally) believed that the level of output would be higher in the future than their given information set would imply, then they ought to eventually revise their belief as new information showed it to be ill-formed.  Consider even the model of Aangeletos and La'O (2013), which relies on communicational frictions to provide a role for animal spirits in economic fluctuations.  While the sunspot-like extrinsic shock of their model generates boom-and-bust cycles, output eventually returns to its long run level as information about the true productivity levels of the various island economies was revealed.\footnote{See Figure 1 in Aangeletos and La'O (2013).}

It might be argued that such animal spirits could have a long run effect on the level of output if they spurred firms to invest in greater quantities, thus raising aggregate supply and subsequently output.  Here,we would assert that our econometric approach to identifying shocks to animal spirits does not allow for this possibility.  For one, we use a measure of consumer (as opposed to business or CEO) confidence to identify animal spirits.  Thus, the shocks we identify are far more likely to impact consumption in the short run than investment.  Secondly, if consumer confidence were to rise in such a way as to generally reflect a broader optimism about future productivity levels in the economy, this sort of innovation would be captured in our identified news shock.  Such increases in confidence can impact the economy in the long run, because they may be associated with expectations for faster future consumption growth due to higher output.

More broadly, it may seem ambitious to suppose that these economies that we consider are driven by only two shocks.  It is certainly the case that we are assuming that a very large number of disparate types of innovations that each affect the economy in very different ways (i.e., some can have permanent effects and some may have only transitory effects) can be rolled up into just one ``fundamental" shock, with only the effects of animal spirits shocks excluded and separately identified.  Also, we note the critique of Blanchard, L'Huillier, and Lorenzoni (2013), who argue that structural vector autoregression can not be used to disentangle noise shocks (which may be considered roughly analogous to the animal spirits shocks that we are trying to identify), because, when consumers face a signal extraction problem, the reduced form VAR is not invertible.\footnote{Fernande-Villaverde et al. (2007) show that when the VAR is not invertible, one cannot interpret the innovations in VARs as being equivalent to the economic shocks posited by theory.}  That is, the econometrician can not observe noise shocks when she has at most the same data available to the consumer.  

In this respect, we note the following.  First, we note that the exercise that we undertake in this paper is subtly different from that described in Blanchard, L'Huillier, and Lorenzoni (2013).  They assume that ``noise" shocks cannot influence productivity, and the problem that they describe is that the econometrician can not use long run VAR restrictions to separate the permanent shock to productivity from the combination of the temporary shock to productivity and the noise shock.  In our paper, we seek to separate the animal spirits shock from any shock that can have a long run effect on output (like their permanent productivity shock).  The risk then is that our animal spirits shock might be an amalgam of the true animal spirits shock and the temporary shock to productivity (again in the framework of Blanchard, L'Huillier, and Lorenzoni (2013)).  We believe that our results are not consistent with this interpretation, which will be discussed further below.  Further, their assumption that noise shocks do not have an effect on productivity is one that we are inherently questioning in this paper.  As in Aangeletos and La'O (2013), Benhabib, Wang, and Wen (2015), and Huo and Takayama (2015a), our model proposes that animal spirits shocks can in fact induce output fluctuations.  

Second, we draw on our use of panel data.  Although we run our VARs state by state, we estimate the impulse responses in a panel setting (as will be discussed further below).  By incorporating data on 50 small, open economies (the 50 U.S. states), we believe that, with the use of time fixed effects, we can control for a substantial number of economic shocks that might plausibly be affecting all states simultaneously, such as monetary policy shocks, fiscal policy shocks, and transitory and permanent shocks to aggregate productivity.  Of course, there could still be idiosyncratic shocks to productivity in each state.  Again, we will argue below that our results are not consistent with the notion that our identified shock is a transitory shock to productivity, but they are consistent with the idea that it is a sentiments shock.  

Blanchard, L'Huillier, and Lorenzoni (2013) demonstrate that when long run restrictions are used to identify shocks that have permanent and transitory effects on productivity in an environment where noise shocks force the consumer to solve a signal extraction problem in order to estimate the permanent component of productivity, long run restrictions in a SVAR, such as those used in Blanchard and Quah (1989) and that we propose to use here, will systematically attribute too much of the variance in output fluctuations to the permanent component and underestimate the contribution of transitory components.  In this sense, then, it is likely that our estimates of the role for animal spirits in generating business cycle fluctuations are biased toward zero.  While this is a problem, we note that the predominant study in the empirical literature (Barsky and Sims (2012)) finds a very small role for animal spirits, so the evidence that we will present for a moderate contribution of animal spirits is fairly novel. 

As Enders (2010) points out, one unappealing aspect of using long run restrictions in a VAR to uncover structural shocks is that the econometrician must impose some restriction on the signs of the contemporaneous effects of the structural innovations on the observed variables in order to identify them.  That leads us to our second key identifying assumption. 

\textbf{Assumption 2}:  The structural innovation in animal spirits must have a positive contemporaneous effect on both consumer confidence and growth in economic activity.

We assume that a shock to animal spirits must be, in part, captured by a positive change in the consumer confidence index.  That is, if households are feeling optimistic about the state of the economy in a fashion that is orthogonal to economic fundamentals, then their answers to the questions posed in the Conference Board's Consumer Confidence Survey must, on balance, be positive.  Similarly, a positive ``spontaneous urge to action" is more likely to lead to an uptick in consumption on the part of the household (and thus higher output) than a decline.  

The imposition of the assumption that animal spirits cannot affect the long run level of economic activity, combined with the contemporaneous sign restriction, can be used to decompose the reduced form VAR residuals into structural innovations in animal spirits and news. Enders (2010), pages 338 to 342, provides a step-by-step guide.

We estimate the structural VAR with long run restrictions individually, state-by-state.  Once we have uncovered the structural innovations in animal spirits and in news, we run the following panel regressions:
\begin{equation}\label{mainreg1}
\Delta y_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}
\end{equation}
\begin{equation}\label{mainreg2}
Conf_{i,t} = \alpha_{2,i} + \delta_{2,t}  + \sum_{j=0}^{T} \beta_{3,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{4,j} \epsilon_{SPIRITS,i,t-j} + v_{2,i,t}\ .
\end{equation}
In these equations, $\epsilon_{NEWS,i,t}$ and $\epsilon_{SPIRITS,i,t}$ denote the structural innovations in news and animal spirits, respectively, for state $i$ at time $t$.  $\alpha_{k,i}$ for $k = 1,2$ represents a set of  state fixed effects in either equation, and $\delta_{k,t}$ for $k=1,2$ is a set of time fixed effects.  What we are interested in are the sequences of coefficients $\{\beta_{1,j}, \beta_{2,j}, \beta_{3,j}, \beta_{4,j}\}$ that make up the impulse responses of state economic activity and consumer confidence to the structural innovations in news and animal spirits.  We set $T$ equal to 60 months, or five years.

In addition to tracking the impulse responses of output (proxied by the state economic activity index) and consumer confidence to innovations in news and animal spirits, we also study the response of consumption, proxied by state-level retail sales.  Our data on retail sales is quarterly, as opposed to the monthly frequency in our consumer confidence and economic activity series.  Therefore, in order to estimate the impulse response of retail sales, we must identify a quarterly innovation in animal spirits and in news.  Thus, we re-estimate the structural VAR with long run and sign restrictions at the quarterly frequency and use the quarterly structural innovations to estimate the impulse response of retail sales in a similar regression as Equations \ref{mainreg1} and \ref{mainreg2}.  

There is a tension in identifying structural innovations in news and animal spirits at the monthly frequency and then aggregated up to the quarterly frequency.  Indeed, it is very likely that quarterly innovations are not nearly as well identified, as they probably contain an element that is already known to agents.  This is a caveat that we bear in mind when examining the impulse responses of retail sales to these quarterly innovations.  
 
\section{Results}\label{results_section}

This section contains our empirical results.  Figure \ref{main_irf} contains the benchmark estimates of the impulse responses of output (top row) and consumer confidence (bottom row) to structural innovations in news (left column) and animal spirits (right column).\footnote{We report results using conventional panel OLS standard errors.  Of course, because our right hand side variables are estimated, and not directly measured, we could potentially have a generated regressor problem.  See Pagan (1984).  We attempt to address this concern by estimating a specification with bootstrapped standard errors.  The results are nearly identical.  We also cluster standard errors at the state and at the region level.  Similarly, the results are barely changed.}   

Consider first the response of consumer confidence to an innovation in animal spirits, in the bottom right hand quadrant of Figure \ref{main_irf}.  This response is a key test of the validity of our approach to identifying shocks to animal spirits.  Intuitively, if our conceptualization of animal spirits is correct, and they are changes in optimism or pessimism orthogonal to economic fundamentals, then they should have a significant effect on consumer confidence in the short run.  Indeed, Barsky and Sims (2012) identify animal spirits as the orthogonalized shock to consumer confidence when it is ordered first in a Cholesky decomposition of reduced form residuals from a VAR with short-run restrictions.  Reassuringly, our identified shock to animal spirits does have a significant contemporaneous positive effect on consumer confidence.  A one standard deviation shock to animal spirits pushes the consumer confidence index up by 6 index points on impact.  The effect quickly reverts to 2 index points, and then it slowly erodes over the next five years, until the consumer confidence index is back to its pre-shock level.

Indeed, it is interesting that an animal spirits shock has such a persistent effect on consumer confidence, even though more than half of the initial response decays quite quickly.  This persistence, as we will see, is also reflected in the response of output to an animal spirits innovation.

In the top left hand quadrant of Figure \ref{main_irf}, one can see the response of output (measured by the state-level coincident economic index) to an innovation to ``news," or economic fundamentals.  Here again, the response is intuitive and helps validate our identification strategy.  The level of output rises slowly in response to a news shock, before it plateaus at a level about 10\% higher than the pre-shock level of output.  Five years after the shock, output is still higher, suggesting that news shocks (or shocks to fundamentals) have a permanent effect on the level of output.  This is consistent with the notion that what we refer to as news shocks carry information to agents reflective of changes in the level of productivity in the economy.

The panel in the bottom left of the figure shows the response of the consumer confidence index to a news shock.  Interestingly, the immediate effect is negative, with the consumer confidence index falling more than two points on impact when a positive news shock hits the economy.  In subsequent periods, consumer confidence recovers strongly and remains about one point above its pre-shock level for the duration of the estimation window.  We can compare this result to those of Basu, Fernald, and Kimball (2006), who famously found that, following a shock to total factor productivity in the economy, employment, hours, and utilization all decline significantly.  While we do not observe the same decline in real activity (which is an aggregate of four labor market variables, including employment and hours worked) after an innovation in news, it is notable that consumer confidence, which is likely to be heavily influenced by labor market variables like employment and hours, exhibits a similar pattern in response to a similar kind of shock.  Again, speaking broadly, we find such a result to be supportive of our interpretation of this innovation as a news shock.

Finally, the top right hand panel in the figure shows the effect of an innovation in animal spirits on real economic activity.  By construction, animal spirits have no long term effect on the level of activity, and, after five years, the influence of the shock has completely eroded.  In the interim, though, animal spirits shocks do have a significant positive effect on output, generating a hump-shaped path for our measure of fundamentals.  The state coincident economic index rises slowly, but steadily, in response to an innovation in animal spirits, peaking after about a year and a half, with the level of output rising 3\% above its pre-shock level (on an annualized basis), after which the effect deteriorates.

If the question is whether or not an innovation in animal spirits that is unconnected to any fundamental change in real driving variables can have a real effect on output, this plot demonstrates that the answer is yes.  Thus, our results do not accord with the results of Barsky and Sims (2012), but are more consistent with the theoretical findings of Angeletos and La'O (2013), Benhabib, Wang and Wen (2015), and Huo and Takayama (2015a).  A change in the annual level of output of about three percentage points in less than two years is a very sizable impact, although as the discussion above shows, the effect of a news shock on the level of output is much greater and actually leads to permanent changes.  Table \ref{var_decomp} reports the variance decomposition for each of the endogenous variables, with each cell of the table displaying the proportion of the overall variance in each of the two endogenous variables attributable to each of the structural innovations under consideration at the given time horizon.     

Table \ref{var_decomp} demonstrates that at short horizons, nearly all of the variation in annualized growth in the state coincident economic index is accounted for by shocks to news, with only a tiny portion due to animal spirits.  While the share of variation made up by animal spirits grows over time, even at fairly long horizons (up to five years), animal spirits can explain less than 15\% of total variation in real economic activity.\footnote{Note that the variance decompositions reported in Table \ref{var_decomp} are for the growth rate, not the level, of the state coincident index.  While the effect of the innovations in animal spirits on the level of output must go to zero, the effect on the growth rate need not go to zero within the same time period.  This is because all of the positive effects that animal spirits have on output must be undone in accordance with the identifying assumption and this manifests in a negative effect on the growth rate of the state coincident index at longer horizons.}  On the other hand, the identified innovation in animal spirits can explain a great deal of the variation in the consumer confidence index, as we might expect.  These shocks explain nearly 90\% of the variance in the consumer confidence index at one month and up to 94\% at the one-year horizon.  The innovation in news, by contrast, explains a much smaller share of the changes in consumer confidence.

It is also worthwhile to compare our results for output to those of Blanchard and Quah (1989), who examine the dynamic responses of aggregate output and the unemployment rate to demand and supply disturbances.  In particular, we note that our estimated responses of the state coincident economic indexes to news, or fundamentals, shocks is very similar to the response of output to an aggregate supply shock in Blanchard and Quah (1989), while the response of the coincident index to an animal spirits shock is has the same hump shape and persistence as the response of output to a demand disturbance in that paper.  In that sense, our identified news shocks have similar effects as supply shocks and our animal spirits shocks act like demand shocks.  

We next revisit the question of whether or not the shock that we identify can in fact be characterized as a shock to animal spirits.  In particular, we discussed in Section \ref{methodology} that there was a risk that the true animal spirits shock we aimed to identify might in practice be conflated with an idiosyncratic transitory shock to productivity.  We argue that our results are more supportive of the idea that the shock we identify is in fact an animal sprits shock.  The fact that the response to output is hump shaped is crucial to this argument.  In Blanchard, L'Huillier, and Lorenzoni (2013), the theoretical response of output to a transitory productivity shock is an immediate jump up on impact, followed by a slow decline back to the pre-shock level.  That is not the response that we observe.  Rather, our estimated response more closely resembles the theoretical response of output to a sentiments shock in Angeletos and La'O (2013). Here, it is important to recall that Blanchard, L'Huillier, and Lorenzoni (2013) do not allow their noise shocks (which might be treated as being more analogous to our animal spirits shocks or the sentiments shocks of Angeletos and La'O (2013)) to have any effect on output at all, an assumption that may be too restrictive.   

In Figures \ref{nonfarm_irf} through \ref{wagespw_irf}, we report the impulse responses for the various state-level labor market variables that make up the state coincident indexes.  We first consider the response of nonfarm payrolls to shocks to news and animal spirits.  The shapes of the impulse responses with respect to both structural shocks roughly mirror the responses of the coincident indexes.  After an innovation in news, the number of employees on nonfarm payrolls slowly rises before plateauing at an increase of six percent relative to the pre-shock level.  The peak in the response of this variable occurs after about three years.  In contrast, after a shock to animal spirits, the number of employees on nonfarm payrolls rises initially, but peaks (after about eighteen months), and then returns to its pre-shock level.  At the peak of the impulse response to an animal spirits innovation, nonfarm payrolls are about two percent higher than they were before the shock.

Figure \ref{unemp_irf} displays the response of the unemployment rate at the state level to each of the innovations.  We see similar patterns in the behavior of the unemployment rate, although the signs are reversed, of course.  After a news shock, the unemployment rate quickly falls, dropping to a level about 0.1\% below its pre-shock level at a horizon of around two years.  Then, the unemployment rate begins to slowly trend back toward its initial level.  This is intuitive.  Considering that we are looking at U.S. states and that there is generally greater labor mobility across states than across countries, we would expect that when a state experiences a fundamental news shock that leads to an increase in employment (as shown in Figure \ref{nonfarm_irf}), agents may be encouraged to enter the labor market in that state, whether they were already state residents but not in the labor force before the news shock or they moved to that state from another state.  The relatively non-persistent response of the unemployment rate to a news shock is consistent with such a story.  After an animal spirits shock, the unemployment rate falls a little more than 0.05 percentage points after about two years, but subsequently quickly reverts to its pre-shock level.

We next consider the response of average weekly hours worked by production workers in manufacturing, which responses can be found in Figure \ref{hours_irf}.  We can observe that in response to neither shock do average weekly hours significantly respond, either statistically or economically.  After a news shock, hours are generally higher, but, especially by the end of the estimation window, the difference relative to the pre-shock amount of hours worked is not statistically significant.  Similarly, after an animal spirits shock, hours actually turn negative, but not significantly so.  They turn positive after about two years, but, again, the response is not significant.  After both shocks, the responses are quite volatile.

In Figures \ref{wages_irf} and \ref{wagespw_irf}, we report the responses of aggregate wages and salaries and average wages and salaries per nonfarm worker, respectively.  As with nonfarm payrolls, aggregate wages and salaries respond significantly positively, and permanently, to a news shock.  The response reaches its peak level after about two and a half years, and the total amount of wages and salaries paid in the state after a positive news shock is five percent higher at the peak than it was before the state was hit by the news shock.  After a positive animal spirits innovation, aggregate wages and salaries rise initially to about 1.5\% higher than the pre-shock level, but they quickly revert back.  When we consider the responses of wages and salaries per nonfarm worker, we see slightly different patterns.  Wages and salaries per worker rise more slowly after a positive news shock than aggregate wages and salaries, and they peak at only about two percent higher than the pre-shock level.  Again, this is not surprising when we recall that the response of the number of workers to a news innovation in a given state was strongly positive.  Thus, some of the rise in aggregate wages and salaries is due to an influx in the number of workers.  In response to an animal spirits shock, the response of wages per worker is roughly flat.      

We next examine the response of state-level retail sales to a structural innovation in news and in animal spirits, and these estimates can be found in Figure \ref{retail_irf}.\footnote{We set the number of lags of the structural innovations in the regression to be 36 quarters.}  Although our estimate of the response of retail sales to a news shock is not very precise, the point estimate is well above zero up to about eight years and is significant at the 95\% confidence level.  At the peak of the response, retail sales are close to five percent higher than before the news innovation.  We would certainly expect that, if output fluctuations can indeed be generated by innovations in animal spirits, that one channel through which they would work would be the consumption channel.  This is what we observe.  The estimates are much noisier than those for the state coincident economic index and its components, but we do see a positive and significant (both statistically and economically) consumption response to a shock to animal spirits.

In the case of the responses to news, both retail sales and the labor market variables that make up the state coincident economic indexes see similar time patterns.  The responses rise slowly at first before plateauing a couple of years after the innovation has hit the state economy.  In general, it is also the case that, in response to a shock to animal spirits, the time paths are also pretty similar, exhibiting a hump-shaped response at intermediate horizons.  One curious aspect of the findings that we have reported so far is that, in general, the effects of animal spirits shocks are fairly persistent.  For many of the macroeconomic time series that we study, it takes up to two years for the response to animal spirits innovations to peak.  This might seem surprising, even though (by construction) the effects erode to zero eventually, because it is not clear that such spontaneous urges to action should last as long as they appear to.  This is a question that we aim to return to in the future.           

\section{Conclusion}\label{conclusion}  

In this paper, we address whether ``animal spirits," as described by Keynes (1936), can have real effects on business cycle fluctuations.  This question is motivated by recent theoretical literature that looks to formalize a role for such sentiments shocks within a modern rational expectations framework.  We leverage data on consumer confidence provided by the Conference Board and indexes of coincident economic activity, both at monthly frequency and at the level of U.S. states to provide an answer to this question.  To identify shocks to animal spirits, separate from shocks to news about future economic fundamentals, we employ long run restrictions in a structural vector autoregression framework, following the seminal paper by Blanchard and Quah (1989).  In particular, we assume that shocks to animal spirits can not have an effect on the level of output in the long run and that, on impact, they must have a positive effect on both output and on measures of consumer confidence.

Our findings suggest that animal spirits shocks can indeed generate business cycle fluctuations.  Specifically, two years after a positive shock to animal spirits, the level of output is about three percent higher, but this positive effect fades after about five years.  In contrast, the bulk of variation (more than 85\% at the five-year horizon)in output growth is caused by shocks to economic fundamentals (or ``news").  Thus, although animal spirits can give important fluctuations at business cycle frequencies, the main driver of output in the short run and in the long run is our estimated news shock.

Retail sales, a proxy for consumption, respond positively to both news and animal spirits shocks, though again, the response is stronger for news shocks.  Among the components that make up the state-level coincident economic indexes, the most precisely estimated responses are in nonfarm payrolls, the unemployment rate, and aggregate wages and salaries.

Certainly, these results would benefit from more formal structural modeling.  In future, we hope to reconsider them in the context of a Dynamic Stochastic General Equilibrium (DSGE) model to better understand certain dynamics that we observe.  In particular, it would be worthwhile to understand why innovations in animal spirits should have such persistent effects on output.  Questions such as this will be left to future research.   






















\newpage

\begin{landscape}
\begin{table}[htb]
\begin{center}
\caption{Conference Board Categorization of States into Regions}\label{regionslist}
\scalebox{0.9}{\begin{tabular}{ccccc}
\hline \hline
\\
\textit{Region} & & & & \\
\hline
\\
New England & Middle Atlantic & South Atlantic & East North Central & East South Central \\
\hline
Connecticut & New Jersey & Delaware & Illinois & Alabama\\
Maine & New York & Florida & Indiana & Kentucky\\
Massachusetts & Pennsylvania & Georgia & Michigan & Mississippi \\
New Hampshire & & Maryland & Ohio & Tennessee\\
Rhode Island & & North Carolina & Wisconsin & \\
Vermont & & South Carolina & & \\
 & & Virginia & & \\
 & & West Virginia & & \\
 \hline
 \\
\textit{Region} & &&&\\
\hline
\\
West North Central & West South Central & Mountain & Pacific & \\
\hline
Iowa & Arkansas & Arizona & Alaska & \\
Kansas & Louisiana & Colorado & California & \\
Minnesota & Oklahoma & Idaho & Hawaii & \\
Missouri & Texas & Montana & Oregon & \\
Nebraska & & Nevada & Washington & \\
North Dakota & & New Mexico & & \\
South Dakota & & Utah & & \\
& & Wyoming & & \\
\hline
\end{tabular}}
\end{center}
 {\footnotesize \textit{Notes:} This table provides a list of states belonging to each of the regions for which the Conference Board compiles a separate consumer confidence index.}
\end{table}
\end{landscape}




\newpage

\begin{landscape}
\begin{table}[htb]
\begin{center}
\caption{Summary Statistics of Key Variables}\label{summstat}
\begin{tabular}{lcccc}
\hline \hline
Variable & Mean & Std Dev 1 & Std Dev 2 & Observations \\
\hline
State-Level Consumer Confidence & 92.46 & 14.88 & 28.57 & 20400  \\
Coincident Economic Index Growth (Annualized) & 2.26 & 2.60 & 3.85 & 20350 \\
State-Level Retail Sales Growth (Annualized) & 1.05 & 7.22 & 7.34 & 3486 \\
\hline \hline
\end{tabular}
\end{center}
 {\footnotesize \textit{Notes:} Consumer Confidence units are index points.  Growth in the coincident economic index and retail sales are expressed in percentage points.  ``Std Dev 1" is defined as the time average of $[(1/n)\sum_i (X_{it} - \bar{X}_t)^2]^{1/2}$.  ``Std Dev 2" is defined as the cross sectional average of $[(1/T)\sum_t (X_{it} - \bar{X}_i)^2]^{1/2}$.  }
\end{table}
\end{landscape}

\newpage 

\begin{table}[htb]
\begin{center}
\caption{Variance Decomposition of Effects on State Coincident Economic Index and Consumer Confidence}\label{var_decomp}
\begin{tabular}{lcc}
\hline \hline
\\
Horizon & \multicolumn{2}{c}{\textit{Proportion of Variance in Coincident Economic Index due to:}} \\
 & News & Animal Spirits \\
\hline
\\
1 month & 0.997 & 0.003 \\
6 months & 0.965 & 0.035 \\
12 months & 0.910 & 0.090 \\
24 months & 0.903 & 0.097 \\
36 months & 0.902 & 0.098 \\
48 months & 0.890 & 0.110 \\
60 months & 0.860 & 0.139 \\
\hline 
\\
Horizon & \multicolumn{2}{c}{\textit{Proportion of Variance in Consumer Confidence due to:}} \\
 & News & Animal Spirits \\
\hline
\\
1 month & 0.105 & 0.895  \\
6 months  & 0.067 & 0.933  \\
12 months & 0.060 & 0.940 \\
24 months & 0.082 & 0.918 \\
36 months & 0.100 & 0.900 \\
48 months & 0.109 & 0.891 \\
60 months & 0.119 & 0.881 \\
\hline \hline
\end{tabular}
\end{center}
 {\footnotesize \textit{Notes:} In each panel, each cell of the table reports the proportion of the overall variance in the given endogenous variable attributable to the shock given by the column heading at the time horizon given by the row heading.}
\end{table}















\begin{landscape}
\begin{figure*}[htb]
\centering

%\begin{figure*}{.35\textwidth}
\centering
\subfigure {\includegraphics[height=1in,width=2in]{connecticut.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{newjersey.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{delaware.pdf}}

\subfigure {\includegraphics[height=1in,width=2in]{illinois.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{alabama.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{iowa.pdf}}

\subfigure {\includegraphics[height=1in,width=2in]{arkansas.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{arizona.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{alaska.pdf}}

\caption{Consumer Confidence}\label{cci_graphs}
 {\footnotesize Each figure plots the state-level consumer confidence measure over time.  The states are chosen as the first state alphabetically in the region for which the Conference Board tabulates its consumer confidence measure.  The derivation of the state level consumer confidence measure from the region level index is explained in the text.}

\end{figure*}

\end{landscape}
















\begin{landscape}
\begin{figure*}[htb]
\centering


\subfigure {\includegraphics[height=1in,width=2in]{connecticut_cei.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{newjersey_cei.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{delaware_cei.pdf}}

\subfigure {\includegraphics[height=1in,width=2in]{illinois_cei.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{alabama_cei.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{iowa_cei.pdf}}

\subfigure {\includegraphics[height=1in,width=2in]{arkansas_cei.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{arizona_cei.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{alaska_cei.pdf}}

\caption{State Economic Indexes}\label{cei_graphs}
 {\footnotesize Each figure plots the annualized growth rate in the state-level coincident economic index computed by the Federal Reserve Bank of Philadelphia.  The states are chosen as the first state alphabetically in the region for which the Conference Board tabulates its consumer confidence measure.}
\end{figure*} 
\end{landscape}










\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{cei_cci_connecticut.pdf}
\caption{Consumer Confidence and Economic Activity Growth in Connecticut}\label{connecticut_both}
 {\footnotesize The figure plots the state-level consumer confidence measure computed by the Conference Board and annualized growth in the state-level coincident economic index computed by the Federal Reserve Bank of Philadelphia over time in Connecticut.  }
\end{figure} 










\begin{landscape}
\begin{figure*}[htb]
\centering



\subfigure {\includegraphics[height=1in,width=2in]{connecticut_retail.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{newjersey_retail.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{illinois_retail.pdf}}


\subfigure {\includegraphics[height=1in,width=2in]{alabama_retail.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{iowa_retail.pdf}}
\subfigure {\includegraphics[height=1in,width=2in]{arkansas_retail.pdf}}

\subfigure {\includegraphics[height=1in,width=2in]{arizona_retail.pdf}}



\caption{State Retail Sales}\label{retail_graphs}
 {\footnotesize Each figure plots the annualized growth rate in state-level retail sales.  The states are chosen as the first state alphabetically in the region for which the Conference Board tabulates its consumer confidence measure.}
\end{figure*} 
\end{landscape}




\begin{landscape}
\begin{figure}[htb]
\centering
\includegraphics[height=5in,width=7in]{lrvar_impulseresponses.pdf}
\caption{Impulse Responses of Output and Consumer Confidence}\label{main_irf}
 {\footnotesize The figures in the top row plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $\Delta y_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $\Delta y_{i,t}$ denotes annualized growth in the state economic activity index for state $i$ in time $t$.  The figures in the bottom row plot the sequences of  $\{\beta_{3,j}\}$ (left) and $\{\beta_{4,j}\}$ (right) from the regression $Conf_{i,t} = \alpha_{2,i} + \delta_{2,t}  + \sum_{j=0}^{T} \beta_{3,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{4,j} \epsilon_{SPIRITS,i,t-j} + v_{2,i,t}$, where $Conf_{i,t}$ denotes the consumer confidence index in state $i$ at time $t$.  The frequency of the regression is monthly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 
\end{landscape}

\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{nonfarm_graph.pdf}
\caption{Impulse Responses of Nonfarm Payrolls}\label{nonfarm_irf}
 {\footnotesize The figures plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $\Delta nonfarm_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $\Delta nonfarm_{i,t}$ denotes annualized growth in the state nonfarm payroll employees for state $i$ in time $t$.  The frequency of the regression is monthly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 

\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{unemp_graph.pdf}
\caption{Impulse Responses of Unemployment Rate}\label{unemp_irf}
 {\footnotesize The figures plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $U_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $U_{i,t}$ denotes the unemployment rate in state $i$ in time $t$.  The frequency of the regression is monthly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 

\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{hours_graph.pdf}
\caption{Impulse Responses of Average Weekly Hours of Manufacturing Workers}\label{hours_irf}
 {\footnotesize The figures plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $N_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $N_{i,t}$ denotes average weekly hours for manufacturing production workers in state $i$ in time $t$.  The frequency of the regression is monthly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 

\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{wages_graph.pdf}
\caption{Impulse Responses of Real Wages and Salaries}\label{wages_irf}
 {\footnotesize The figures plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $\Delta w_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $\Delta w_{i,t}$ denotes annualized growth in the wages and salaries deflated by the national consumer price index in state $i$ in time $t$.  The frequency of the regression is quarterly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 

\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{wages_per_workers.pdf}
\caption{Impulse Responses of Real Wages and Salaries per Worker}\label{wagespw_irf}
 {\footnotesize The figures plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $\Delta w_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $\Delta w_{i,t}$ denotes annualized growth in nonfarm wages and salaries per nonfarm worker deflated by the national consumer price index in state $i$ in time $t$.  The frequency of the regression is quarterly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 

\begin{figure}[htb]
\centering
\includegraphics[height=3in,width=5in]{lrvar_retail_standard.pdf}
\caption{Impulse Responses of Retail Sales}\label{retail_irf}
 {\footnotesize The figures plot the accumulated sums of $\{\beta_{1,j}\}$ (left) and $\{\beta_{2,j}\}$ (right) from the regression $\Delta c_{i,t} = \alpha_{1,i} + \delta_{1,t} + \sum_{j=0}^{T} \beta_{1,j} \epsilon_{NEWS,i,t-j} + \sum_{j=0}^{T} \beta_{2,j} \epsilon_{SPIRITS,i,t-j} + v_{1,i,t}$, where $\Delta c_{i,t}$ denotes annualized growth in the state retail sales for state $i$ in time $t$.  The frequency of the regression is quarterly.  The dashed lines represent 95\% confidence bands.}
\end{figure} 



