macroeconomic uncertainty through the lens of professional forecasters abstractwe analyze the evolution of macroeconomic uncertainty in the united states based on the forecast errors of consensus survey forecasts of various economic indicators comprehensive information contained in the survey forecasts enables us to capture a real time measure of uncertainty surrounding subjective forecasts in a simple framework we jointly model and estimate macroeconomic common and indicator specific uncertainties of four indicators using a factor stochastic volatility model our macroeconomic uncertainty estimates have three major spikes has three major spikes aligned with the 1973 1975 1980 and 2007 2009 recessions while other recessions were characterized by increases in indicator specific uncertainties we also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series finally our macroeconomic uncertainty has a persistent negative impact on real economic activity rather than producing wait and see dynamics

the effects of oil price uncertainty on global real economic activity this paper investigates the effect of oil price uncertainty on global real economic activity using a quarterly vector autoregressive model with stochastic volatility in mean stochastic volatility allows oil price uncertainty to vary separately from changes in the level of oil prices and allows one to incorporate an extraneous indicator of oil price uncertainty such as realized volatility that greatly improves the precision of the estimated uncertainty series the estimation results show that an oil price uncertainty shock has negative effects on world industrial production all else equal for example it is shown that a doubling of oil price volatility is associated with a cumulative decline as high as 0 3 percentage points in world industrial production

a new index of financial conditions we use factor augmented vector autoregressive models with time varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key us macroeconomic variables time variation in the models parameters allows for the weights attached to each financial variable in the index to evolve over time furthermore we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time we discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables

uncertainty across volatility regimes we propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on uncertainty this approach allows us to simultaneously address two major questions in the empirical literature is uncertainty a cause or effect of decline in economic activity does the relationship between uncertainty and economic activity change across macroeconomic regimes results based on a small scale vector autoregression with us monthly data suggest that i uncertainty is an exogenous source of decline of economic activity and ii the effects of uncertainty shocks amplify in periods of economic and financial turmoil

short term gdp forecasting with a mixed frequency dynamic factor model with stochastic volatility in this article we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time varying stochastic volatilities we use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results first we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals second we show how our setup allows to make a probabilistic assessment of the contribution of releases to forecast revisions third we examine point and density out of sample forecast accuracy we find that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy supplementary materials for this article are available online

the stochastic volatility in mean model empirical evidence from international stock markets in this paper we present an exact maximum likelihood treatment for the estimation of a stochastic volatility in mean svm model based on monte carlo simulation methods the svm model incorporates the unobserved volatility as an explanatory variable in the mean equation the same extension is developed elsewhere for autoregressive conditional heteroscedastic arch models known as the arch in mean arch m model the estimation of arch models is relatively easy compared with that of the stochastic volatility sv model however efficient monte carlo simulation methods for sv models have been developed to overcome some of these problems the details of modifications required for estimating the volatility in mean effect are presented in this paper together with a monte carlo study to investigate the finite sample properties of the svm estimators taking these developments of estimation methods into account we regard sv and svm models as practical alternatives to their arch counterparts and therefore it is of interest to study and compare the two classes of volatility models we present an empirical study of the intertemporal relationship between stock index returns and their volatility for the united kingdom the united states and japan this phenomenon has been discussed in the financial economic literature but has proved hard to find empirically we provide evidence of a negative but weak relationship between returns and contemporaneous volatility which is indirect evidence of a positive relation between the expected components of the return and the volatility process copyright 2002 john wiley sons ltd

measuring macroeconomic uncertainty a historical perspective abstract using 100 years of data we provide a new macroeconomic uncertainty proxy for the us and other 14 major industrial countries we show macroeconomic uncertainty experiences an overall declining pattern in the century and the influential political and economic events two world wars and major recessions are associated with a dramatic surge in macroeconomic uncertainty meanwhile a global common component plays an important role in driving uncertainty fluctuations across countries

measuring uncertainty and its impact on the economy we propose a new framework for measuring uncertainty and its effects on the economy based on a large var model with errors whose stochastic volatility is driven by two common unobservable factors representing aggregate macroeconomic and financial uncertainty the uncertainty measures can also influence the levels of the variables so that contrary to most existing measures ours reflect changes in both the conditional mean and volatility of the variables and their impact on the economy can be assessed within the same framework moreover identification of the uncertainty shocks is simplified with respect to standard var based analysis in line with the favar approach and with heteroskedasticity based identification finally the model which is also applicable in other contexts is estimated with a new bayesian algorithm which is computationally efficient and allows for jointly modeling many variables while previous var models with stochastic volatility could only handle a handful of variables empirically we apply the method to estimate uncertainty and its effects using us data finding that there is indeed substantial commonality in uncertainty sizable effects of uncertainty on key macroeconomic and financial variables with responses in line with economic theory

internal rebellions and external threats a model of government organizational forms in ancient china in ancient china a ruler needed to handle both internal rebellions and external threats to decrease the possibility of internal rebellions a ruler could organize the government to establish the balance of power among ministers while effective in preventing internal rebellions this approach could make the defense of the country against external threats less effective the tradeoff between preventing internal rebellions and dealing with external threats in a ruler s choice of government organizational form is affected by the size of the population the level of coordination efficiency and the degree of increasing returns in the military sector if the magnitude of external threats increases regardless of the type of equilibrium organizational form the equilibrium level of the concentration of power among division heads increases

the impact of uncertainty shocks under measurement error a proxy svar approach a growing literature considers the impact of uncertainty using svar models that include proxies for uncertainty shocks as endogenous variables in this paper we consider the impact of measurement error in these proxies on the estimated impulse responses we show via a monte carlo experiment that measurement error can result in attenuation bias in impulse responses in contrast the proxy svar that uses the uncertainty shock proxy as an instrument does not suffer from this bias applying this latter method to the bloom 2009 data set results in impulse responses to uncertainty shocks that are larger in magnitude and more persistent than those obtained from a recursive svar

the impact of the volatility of monetary policy shocks this paper studies the impact of the volatility of monetary policy using a structural vector auroregression svar model enriched along two dimensions first it allows for time varying variance of monetary policy shocks via a stochastic volatility specification second it allows a dynamic interaction between the level of the endogenous variables in the var and the time varying volatility the analysis establishes that the nominal interest rate output growth and inflation fall in reaction to an increase in the volatility of monetary policy the analysis also develops a dynamic stochastic general equilibrium model enriched with stochastic volatility to monetary policy that generates similar responses and provides a theoretical underpinning of these findings

time varying structural vector autoregressions and monetary policy a corrigendum this note shows how to apply the procedure of kim et al 1998 to the estimation of var dsge factor and unobserved components models with stochastic volatility in particular it revisits the estimation algorithm of the time varying var model of primiceri 2005 the main difference of the new algorithm is the ordering of the various mcmc steps with each individual step remaining the same

explaining the great moderation it is not the shocks this paper shows that the explanation of the decline in the volatility of gdp growth since the mid eighties is not the decline in the volatility of exogenous shocks but rather a change in their propagation mechanism

common drifting volatility in large bayesian vars the general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar we propose two models in which conditional volatilities feature comovement and study them using u s macroeconomic data the first model specifies the conditional volatilities as driven by a single common unobserved factor plus an idiosyncratic component we label this model bvar with general factor stochastic volatility bvar gfsv and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate the second model which we label bvar with common stochastic volatility bvar csv is a special case of the bvar gfsv in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities such restrictions permit a convenient kronecker structure for the posterior variance of the var coefficients which in turn permits estimating the model even with large datasets while perhaps misspecifi

forecasting with medium and large bayesian vars this paper is motivated by the recent interest in the use of bayesian vars for forecasting even in cases where the number of dependent variables is large in such cases factor methods have been traditionally used but recent work using a particular prior suggests that bayesian var methods can forecast better in this paper we consider a range of alternative priors which have been used with small vars discuss the issues which arise when they are used with medium and large vars and examine their forecast performance using a us macroeconomic dataset containing 168 variables we find that bayesian vars do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches typically we find that the simple minnesota prior forecasts well in medium and large vars which makes this prior attractive relative to computationally more demanding alternatives our empirical results show the importance of using forecast metrics based on the entire predictive density instead of relying solely on those based on point forecasts

fluctuations in uncertainty this review article tries to answer four questions i what are the stylized facts about uncertainty over time ii why does uncertainty vary iii do fluctuations in uncertainty matter and iv did higher uncertainty worsen the great recession of 2007 2009 on the first question both macro and micro uncertainty appears to rise sharply in recessions on the second question the types of exogenous shocks like wars financial panics and oil price jumps that cause recessions appear to directly increase uncertainty and uncertainty also appears to endogenously rise further during recessions on the third question the evidence suggests uncertainty is damaging for short run investment and hiring but there is some evidence it may stimulate longer run innovation finally in terms of the great recession the large jump in uncertainty in 2008 potentially accounted for about one third of the drop in gdp

innocent bystanders monetary policy and inequality abstract we study the effects of monetary policy shocks on and their historical contribution to consumption and income inequality in the united states since 1980 as measured by the consumer expenditure survey contractionary monetary policy systematically increases inequality in labor earnings total income consumption and total expenditures furthermore monetary policy shocks account for a non trivial component of the historical cyclical variation in income and consumption inequality using detailed micro level data on income and consumption we document some of the different channels via which monetary policy shocks affect inequality as well as how these channels depend on the nature of the change in monetary policy

fragility and the effect of international uncertainty shocks abstract when economic systems are fragile even modest shocks for example a shock to international capital movements can have strong negative effects on key macroeconomic fundamentals this paper proposes a large scale bayesian vector autoregression with factor stochastic volatility to investigate the macroeconomic consequences of international uncertainty shocks in g7 countries and shows that uncertainty increases fragility the factor structure enables us to identify an international uncertainty shock by assuming that it is the joint volatility process that determines the dynamics of the variance covariance matrix of the common factors to allow for first and second moment shocks we moreover assume that the uncertainty factor enters the var equation as an additional regressor our findings suggest that an international uncertainty shock has negative effects across all economies and variables under consideration leading to strong declines in output prices exports interest rates and equity prices the precise degree of fragility varies across countries a simple correlation exercise suggests that structural differences may be partially responsible for the observed differences

is all space created equal uncovering the relationship between competing land uses in subdivisions abstract evaluating the importance of different forms of open space to households requires an evaluation of the service flows provided by each type of open space for many non market goods these flows occur over multiple spatial scales and require analysis that simultaneously accounts for capitalization at each scale to meet this often overlooked need we apply a newly developed extension to the hausman taylor model that treats multiple housing transactions occurring in a spatial location as a panel this methodology allows us to account for omitted variables within a subdivision while instrumenting for variables identified through differences between subdivisions we measure capitalization of open space at three distinct spatial extents adjacency walkability and subdivision wide we find that the interactions between subdivision open space and private open space in the form of lot size change from complementarity at small scales to substitutability at large scales these results confirm much of the intuition developed by ecologists and public planners on the likely service flows associated with open space and show how our approach to accounting for multiple spatial scales of capitalization in the evaluation of non market goods could be beneficial to other areas of applied research

were there regime switches in u s monetary policy working paper 2004 14 june 2004 abstract a multivariate model identifying monetary policy and allowing for simultaneity and regime switching in coefficients and variances is confronted with u s data since 1959 the best fit is with a model that allows time variation in structural disturbance variances only among models that also allow for changes in equation coefficients the best fit is for a model that allows coefficients to change only in the monetary policy rule that model allows switching among three main regimes and one rarely and briefly occurring regime the three main regimes correspond roughly to periods when most observers believe that monetary policy actually differed and the differences in policy behavior are substantively interesting though statistically ill determined the estimates imply monetary targeting was central in the early 80s but was also important sporadically in the 70s the changes in regime were essential neither to the rise in inflation in the 70s nor to its decline in the 80s jel classification e52 e47 c53 key words counterfactuals lucas critique policy rule monetary targeting simultaneity volatility model comparison i the debate over monetary policy change in an influential paper clarida gali and gertler 2000 cgg presented evidence that us monetary policy changed between the 1970 s and the 1980 s indeed that in the 70 s it was drastically worse they found that the policy rule apparently followed in the 70 s was one that when embedded in most stochastic general equilibrium models would imply non uniqueness of the equilibrium and hence vulnerability of the economy to sunspot fluctuations of arbitrarily large size their estimated policy rule for the later period on the other hand eliminated this indeterminacy these results are a possible explanation of the volatile and rising inflation of the 70 s and of its subsequent decline the cgg analysis has two important weaknesses one is that it fails to account for stochastic volatility us macroeconomic variables and particularly the federal funds rate have gone through periods of tranquility and of agitation with forecast error variances varying greatly from period to period ignoring such variation does not lead to inconsistent estimates of model parameters when the forecasting equations themselves are constant but it strongly biases toward a finding of changed parameters tests of the stability of the forecasting equations the other weakness is that the cgg analysis rests on powerful and implausible identifying assumptions they require that we accept that the response of the monetary authority to expected future inflation and output does not depend on the recent history of inflation money growth or output it is hard to understand why this should be so especially in the 70 s when monetarism was a prominent theme in policy debates congress was requiring reports from the fed of projected time paths of monetary aggregates and financial markets were reacting sensitively to weekly money supply numbers the requirement for existence and uniqueness of equilibrium in dynamic models is that the monetary policy rule show a more than unit response of interest rates to the sum of the logs of all nominal variables that appear on the right hand side of the reaction function if we force a particular measure of expected future inflation to proxy for all the nominal variables that actually appear independently in the reaction function we are bound to get distorted conclusions on the one hand because expected future inflation will be a noisy measure of the full set of nominal influences on policy we might get downward bias in our estimates from the usual errorsin variables effect on the other hand to the extent that expected future inflation like most expected future values shows less variation than current nominal variables we could find a mistaken scaling up of coefficients

adjustment of consumers durables stocks evidence from automobile purchases this paper tests an optimal s s rule in household durable purchases and examines directly the resulting aggregate expenditure dynamics the observed decision rule responds to income uncertainty and growth as predicted by an s s model resulting from transactions costs tests against liquidity constraints find that about half the households purchase according to an optimal s s rule aggregating the s s rule over households produces a cross section distribution of durables holdings the empirical distribution is similar to that predicted theoretically as is its response to aggregate shocks furthermore simulations of aggregate expenditure based on the household distribution exhibit dynamics consistent with those observed in the 1980s

uncertainty shocks and unemployment dynamics in u s recessions abstract what are the effects of uncertainty shocks on unemployment dynamics we answer this question by estimating non linear smooth transition vars with post wwii u s data the relevance of uncertainty shocks is found to be much larger than that predicted by standard linear vars in terms of i magnitude of the reaction of the unemployment rate to such shocks and ii contribution to the variance of the prediction errors of unemployment at business cycle frequencies the ability of different classes of dsge models to replicate our results is discussed

uncertainty and consumer durables adjustment we characterize infrequent durables stock adjustment by consumers who also derive utility from non durable consumption flows in the presence of idiosyncratic income uncertainty the data we analyse include subjective future income uncertainty measures which we use as instruments in the estimation of relevant parameters of heterogeneous consumers dynamic adjustment problems the data feature two conceptually distinct sources of variation cross sectional heterogeneity of the sampled households dynamic problems and history dependent heterogeneity in their situation during the observation period we note that the latter should affect the likelihood but not the size of stock adjustment decisions and find broad support for theoretical predictions in formal selection controlled regressions based on this insight

monetary policy uncertainty and economic fluctuations uncertainty associated with the monetary policy transmission mechanism is a key driving force of business cycles to investigate this link we propose a new term structure model that allows the volatility of the yield curve to interact with macroeconomic indicators the data favors a model with two volatility factors that capture shortterm and long term monetary policy uncertainty increases in either of them lead higher unemployment rates but they interact with ination in opposite directions

the stochastic volatility in mean model with time varying parameters an application to inflation modeling this article generalizes the popular stochastic volatility in mean model to allow for time varying parameters in the conditional mean the estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance and its coefficient in the former is time varying we develop an efficient markov chain monte carlo algorithm based on band and sparse matrix algorithms instead of the kalman filter to estimate this more general variant the methodology is illustrated with an application that involves u s u k and germany inflation the estimation results show substantial time variation in the coefficient associated with the volatility highlighting the empirical relevance of the proposed extension moreover in a pseudo out of sample forecasting exercise the proposed variant also forecasts better than various standard benchmarks

macroeconomic forecasting using diffusion indexes this article studies forecasting a macroeconomic time series variable using a large number of predictors the predictors are summarized using a small number of indexes constructed by principal component analysis an approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts the method is used to construct 6 12 and 24 monthahead forecasts for eight monthly u s macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998 during this sample period these new forecasts outperformed univariate autoregressions small vector autoregressions and leading indicator models