tracking the slowdown in long run gdp growth using a dynamic factor model that allows for changes in both the long run growth rate of output and the volatility of business cycles we document a significant decline in long run output growth in the united states our evidence supports the view that most of this slowdown occurred prior to the great recession we show how to use the model to decompose changes in long run growth into its underlying drivers at low frequencies a decline in the growth rate of labor productivity appears to be behind the recent slowdown in gdp growth for both the united states and other advanced economies when applied to real time data the proposed model is capable of detecting shifts in long run growth in a timely and reliable manner

dynamic model averaging in large model spaces using dynamic occam s window abstract bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data when data arrive sequentially and the generating model can change over time dynamic model averaging dma extends model averaging to deal with this situation often in macroeconomics however many candidate explanatory variables are available and the number of possible models becomes too large for dma to be applied in its original form we propose a new method for this situation which allows us to perform dma without considering the whole model space but using a subset of models and dynamically optimizing the choice of models at each point in time this yields a dynamic form of occam s window we evaluate the method in the context of the problem of nowcasting gdp in the euro area we find that its forecasting performance compares well with that of other methods

nowcasting sales growth of manufacturing companies in india abstractthe performance of private corporate sector is used as an important demand indicator for monetary policy making as these data are received with a lag assessing and monitoring of corporate sales on a real time basis poses a significant challenge to policy makers in india in this context this article attempts to nowcast quarterly sales growth of indian manufacturing companies and gdp growth of india using dynamic factor modelling framework a multiple level framework through turning point analysis and elastic net structure is used to overcome the overfitting problem during variable selection empirical results show improvement in forecast accuracy for one quarter ahead nowcast using 3 factor and 4 factor models over the benchmark model however absolute dominance of 3 factor models over 4 factor models was not established as such the article has proposed a forecast combination technique to nowcast sales growth of manufacturing companies in india

operationalizing opportunities in entrepreneurship research use of data envelopment analysis despite the impressive development of substantive theories in entrepreneurship without the development of measurement theories further advancement of the field is problematic in particular the notion of opportunities central to entrepreneurship research requires adequate macro level operationalization we demonstrate how to employ data envelopment analysis dea to operationalize not only innovative opportunities but also technological arbitrage opportunities we provide an illustrative example based on a sample of 66 countries during the period of 1993 2002 we include estimates of innovative and arbitrage opportunities for possible use by other scholars discuss the promise and limitations of such estimates demonstrate how both innovative and arbitrage opportunities correlate with the rates of entrepreneurial activity and suggest several possible directions for future research

macroeconomic forecasting in the euro area country specific versus area wide information abstract this paper compares several time series methods for short run forecasting of euro wide inflation and real activity using data from 1982 to 1997 forecasts are constructed from univariate autoregressions vector autoregressions single equation models that include euro wide and us aggregates and large model methods in which forecasts are based on estimates of common dynamic factors aggregate euro wide forecasts are constructed from models that utilize only aggregate euro wide variables and by aggregating country specific models the results suggest that forecasts constructed by aggregating the country specific models are more accurate than forecasts constructed using the aggregate data

exploiting the monthly data flow in structural forecasting a quarterly stochastic general equilibrium dsge model is combined with a now casting model designed to read timely monthly information as it becomes available this implies 1 mapping the structural quarterly dsge with a monthly version that maintains the same economic restrictions 2 augmenting the model with a richer data set and 3 updating the estimates of the dsge s structural shocks in real time following the publication calendar of the data our empirical results show that our methodology enhances the predictive accuracy in now casting an analysis of the great recession also shows that our framework would have helped tracing the dsge s structural shocks in real time obtaining for example a more timely account of the 2008 contraction

the return of the wage phillips curve the standard new keynesian model with staggered wage setting is shown to imply a simple dynamic relation between wage inflation and unemployment under some assumptions that relation takes a form similar to that found in empirical applications starting with the original phillips 1958 curve and may thus be viewed as providing some theoretical foundations to the latter the structural wage equation derived here is shown to account reasonably well for the comovement of wage inflation and the unemployment rate in the u s economy even under the strong assumption of a constant natural rate of unemployment

nowcasting global economic growth a factor augmented mixed frequency approach assessing accurately global economic conditions is a great challenge for economists the international monetary fund proposes within its periodic world economic outlook report a measure of the global gdp annual growth that is generally considered as the benchmark nowcast by macroeconomists in this paper we put forward an alternative approach to provide monthly nowcasts of the annual global growth rate our approach builds on a factor augmented mixed data sampling fa midas model that enables i to account for a large monthly database including various countries and sectors of the global economy and ii to nowcast a low frequency macroeconomic variable using higher frequency information pseudo real time results over the period 2010 16 show that this approach provides reliable and timely nowcasts of the world gdp annual growth on a monthly basis

nowcasting the real time informational content of macroeconomic data abstract a formal method is developed for evaluating the marginal impact that intra monthly data releases have on current quarter forecasts nowcasts of real gross domestic product gdp growth the method can track the real time flow of the type of information monitored by central banks because it can handle large data sets with staggered data release dates each time new data are released the nowcasts are updated on the basis of progressively larger data sets that reflecting the unsynchronized data release dates have a jagged edge across the most recent months

forecasting the world economy in the short term 1433 audrone jakaitiene and stephane dees forecasting the world economy is a difficult task given the complex interrelationships within and across countries this paper proposes a number of approaches to forecast short term changes in selected world economic variables and aims first at ranking various forecasting methods in terms of forecast accuracy and second at checking whether methods forecasting directly aggregate variables direct approaches outperform methods based on the aggregation of country specific forecasts bottom up approaches overall all methods perform better than a simple benchmark for short horizons up to 3 months ahead among the forecasting approaches used factor models appear to perform the best moreover direct approaches outperform bottom up ones for real variables but not for prices finally when country specific forecasts are adjusted to match direct forecasts at the aggregate levels top down approaches the forecast accuracy is neither improved nor deteriorated i e top down and bottom up approaches are broadly equivalent in terms of country specific forecast accuracy

dynamic hierarchical factor models this paper uses multilevel factor models to characterize within and between block variations as well as idiosyncratic noise in large dynamic panels block level shocks are distinguished from genuinely common shocks and the estimated block level factors are easy to interpret the framework achieves dimension reduction and yet explicitly allows for heterogeneity between blocks the model is estimated using an mcmc algorithm that takes into account the hierarchical structure of the factors the importance of block level variations is illustrated in a four level model estimated on a panel of 445 series related to different categories of real activity in the united states

retail price cycles and the presence of small firms abstract in many canadian cities retail gasoline prices remain unchanged for weeks at a time while in other cities retail prices cycle increasing sharply and declining slowly in maskin and tirole econometrica 56 1988 571 both price cycles and rigid prices can exist in equilibrium in this article i demonstrate that the existence of these equilibria is dependent on the number of retail outlets operated by the firms constant price equilibria are found to exist only when firms are of similar size however cycle equilibria can be constructed for a wide range of relative sizes

mixed frequency var models with markov switching dynamics this paper extends the markov switching vector autoregressive models to accommodate both the typical lack of synchronicity that characterizes the real time daily flow of macroeconomic information and economic indicators sampled at different frequencies the results of the empirical application suggest that the model is able to capture the features of the nber business cycle chronology very accurately

monthly pass through ratios abstract this paper estimates monthly pass through ratios from import prices to consumer prices in real time conventional time series methods impose restrictions to generate exogenous shocks on exchange rates or import prices when estimating pass through coefficients instead our estimation strategy follows an event study approach based on monthly releases in import prices projections from a dynamic common factor model with daily panels before and after monthly releases of import prices define the innovation for import prices we apply our identification procedure to swiss prices and find strong evidence that the median of the monthly pass through ratio is around 0 3 tests show that standard assumptions of non real time data and limited information breath are critical for the pass through estimates

forecasting the gdp of a small open developing economy an application of favar models gdp forecasting remains a challenge for a small open developing economy faced with insufficient and low frequency data central bank forecasters cannot project gdp reliably for the purpose of mone

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

cyclic pricing by a durable goods monopolist in the model of this paper a monopoly seller of a durable good holds periodic sales as a means of price discrimination a new cohort of consumers enters the market in each period interested in purchasing the good either immediately or after a delay within each cohort consumers vary in their tastes for the good under broad conditions the seller will vary the price over time in most periods he will charge a price just low enough to sell immediately to consumers with a high willingness to pay periodically however he will drop the price far enough to sell to an accumulated group of consumers with a low willingness to pay

gdp nowcasting application and constraints in a small open developing economy despite data limitations an attempt is made to find out if a gdp nowcasting model can provide reliable forecasts for a small open economy two competing bayesian vector autoregressive models are tested rigorously to obtain the optimal model by minimizing in sample forecasting errors the main finding of this study is that gdp nowcasting can produce reliable results for a small open economy despite the unavailability of sufficient data sets and the lack of high frequency indicators

a new model for cycles in retail petrol prices abstract this paper explores the way that retail petrol prices may vary in a cyclical way as a result of competitive behavior by petrol retailers when some drivers choose to fill up early when a low petrol price is available we include a stochastic model for an individual driver s use of petrol and consider what happens as the expected future price of petrol is adjusted either according to observed prices or in anticipation of cyclical behavior this model is different from most previous work on petrol price cycles that has focussed on edgeworth cycles

sustainable entrepreneurship is entrepreneurial will enough a north south comparison based on an analysis of 44 cases in canada tunisia and cameroon this research attempts to determine the fundaments of sustainable entrepreneurship se in an international perspective and to shed the light on the potential impact of economic institutional and cultural dimensions upon diverse levels of sustainability in small and medium size firms smes neo institutional and entrepreneurship theories were combined in an integrative conceptual model to fully embrace the meanings and practices of se and to question the culture free argument of some prescriptions as well as some international programs the analysis of the rationale behind entrepreneurs committed to sustainability in the three countries compared to less engaged firms shows that entrepreneurs individual values are crucial in all cases socio cultural specificities and institutional realities can however be more or less inductive to the adoption of sustainable practices in smes this study also demonstrates that external stimuli are needed in tunisia and cameroon to reach the same environmental involvement as found in canada moreover support programs to assist smes with the adoption of sustainable practices and communication about the issue have to be tailored to the entrepreneur s motives the firms levels of openness to sustainability the socio cultural practices already embedded and the countries priorities implications for policy management and research are also discussed

predictability of output growth and inflation a multi horizon survey approach we develop an unobserved components approach to study surveys of forecasts containing multiple forecast horizons under the assumption that forecasters optimally update their beliefs about past current and future state variables as new information arrives we use our model to extract information on the degree of predictability of the state variable and the importance of measurement errors in the observables empirical estimates of the model are obtained using survey forecasts of annual gdp growth and inflation in the united states with forecast horizons ranging from 1 to 24 months and the model is found to closely match the joint realization of forecast errors at different horizons our empirical results suggest that professional forecasters face severe measurement error problems for gdp growth in real time while this is much less of a problem for inflation moreover inflation exhibits greater persistence and thus is predictable at longer horizons than gdp growth and the persistent component of bot

forecasting consumption the role of consumer confidence in real time with many predictors summary we study the role of consumer confidence in forecasting real personal consumption expenditure and contribute to the extant literature in three substantive ways first we re examine existing empirical models of consumption and consumer confidence not only at the quarterly frequency but using monthly data as well second we employ real time data in addition to commonly used revised vintages third we investigate the role of consumer confidence in a rich information context we produce forecasts of consumption expenditures with and without consumer confidence measures using a dynamic factor model and a large real time jagged edge dataset in a robust way we establish the important role of confidence surveys in improving the accuracy of consumption forecasts manifesting primarily through the services component during the recession of 2007 2009 sentiment is found to have a more pervasive effect on all components of aggregate consumption durables non durables and services copyright 2015 john wiley sons ltd

u s monetary policy surprises identification with shifts and rotations in the term structure we develop a model to extract measures of monetary policy surprises from the maturity structure of the yield curve the model endogenously allows for the fact that the yield curve may either shift or rotate in response to monetary policy shocks a latent factor model approach with identification through heteroskedasticity harnesses the term structure to extract monetary policy shocks the approach offers informational advantages over event studies results from the u s term structure from 1994 strongly support the hypothesis that differing term structure responses are reactions to different types of monetary policy shock rather than differing reactions to the same policy shock

the distribution of schumpeterian and kirznerian opportunities despite their presence in modern entrepreneurship theories empirical investigations of opportunity types are scarce we develop a 5 dimensional bipolar scale to measure the extent in which opportunities are schumpeterian or kirznerian drawing on this measure we examine the shape of the opportunity distribution in relation to the environment and the characteristics of individuals who exploit them across a sample of 181 new products introduced by high tech entrepreneurs we find that pure schumpeterian and kirznerian types are rare but rather opportunities display varying degrees of membership to one or the other type the distribution is more skewed toward schumpeterian opportunities for larger ventures with a strategic focus on future needs and operating in growing markets in contrast kirznerian opportunities are more frequently pursued by small ventures and by entrepreneurs that are young with a strategic focus on present needs and who are less innovative in their behavior

estimating time variation in measurement error from data revisions an application to backcasting and forecasting in dynamic models over time economic statistics are refined this implies that data measuring recent economic events are typically less reliable than older data such time variation in measurement error affects optimal forecasts measurement error and its time variation are of course unobserved our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency we illustrate the gains in forecasting performance from exploiting these estimates using a real time dataset on uk aggregate expenditure data copyright 2009 john wiley sons ltd

forecasting oil prices high frequency financial data are indeed useful abstract the paper examines the importance of combining high frequency financial information along with the oil market fundamentals in order to gain incremental forecasting accuracy for oil prices inspired by french et al 1987 and bollerslev et al 1988 who maintain that future asset returns are also influenced by past volatility we use daily volatilities and returns from financial and commodity markets to generate real out of sample forecasts for the monthly oil futures prices our results convincingly show that although the oil market fundamentals are useful for long run forecasting horizons the combination of the latter with high frequency financial data significantly improve oil price forecasts by reducing the rmse of the no change forecast by approximately 68 results are even more impressive during the oil price collapse period of 2014 15 these findings suggest that we cannot ignore the information extracted from the financial markets when forecasting oil prices our results are both statistically and economically significant as suggested by several robustness tests

automated earnings forecasts beat analysts or combine and conquer prior studies attribute analysts forecast superiority over time series forecasting models to their access to a large set of firm industry and macroeconomic information an information advantage which they use to update their forecasts on a daily weekly or monthly basis a timing advantage this study leverages recently developed mixed data sampling midas regression methods to synthesize a broad spectrum of high frequency data to construct forecasts of firm level earnings we compare the accuracy of these forecasts to those of analysts at short horizons of one quarter or less we find that our midas forecasts are more accurate and have forecast errors that are smaller than analysts when forecast dispersion is high and when the firm size is smaller in addition we find that combining our midas forecasts with analysts forecasts systematically outperforms analysts alone which indicates that our midas models provide information orthogonal to analysts our results provide preliminary support for th

a theory of dynamic oligopoly ii price competition kinked demand curves and edgeworth cycles we provide game theoretic foundations for the classic kinked demand curve equilibrium and edgeworth cycle we analyze a model in which firms take turns choosing prices the model is intended to capture the idea of reactions based on short run commitment in a markov perfect equilibrium mpe a firm s move in any period depends only on the other firm s current price there are multiple mpe s consisting of both kinked demand curve equilibria and edgeworth cycles in any mpe profit is bounded away from the bertrand equilibrium level we show that a kinked demand curve at the monopoly price is the unique symmetric renegotiation proof equilibrium when there is little discounting we then endogenize the timing by allowing firms to move at any time subject to short run commitments we find that firms end up alternating thus vindicating the ad hoc timing assumption of our simpler model we also discuss how the model can be enriched to provide explanations for excess capacity and market sharing keiiwords tacit collusion markov perfect equilibrium kinked demand curve edgeworth cycle excess capacity market sharing endogenous timing

why do forecasters disagree lessons from the term structure of cross sectional dispersion abstract key sources of disagreement among economic forecasters are identified by using data on cross sectional dispersion in forecasters long and short run predictions of macroeconomic variables dispersion among forecasters is highest at long horizons where private information is of limited value and lower at short forecast horizons moreover differences in views persist through time such differences in opinion cannot be explained by differences in information sets our results indicate they stem from heterogeneity in priors or models differences in opinion move countercyclically with heterogeneity being strongest during recessions where forecasters appear to place greater weight on their prior beliefs

supplanting the minnesota prior forecasting macroeconomic time series using real business cycle model priors abstract although general equilibrium models are in wide use in the theoretical macroeconomic literature their empirical relevance is uncertain we develop procedures for using dynamic general equilibrium models to aid in analyzing the observed time series relationships among macroeconomic variables our strategy is based on that developed by doan litterman and sims 1984 who constructed a procedure for improving time series forecasts by shrinking vector autoregression coefficient estimates toward a prior view that vector time series are well described as collections of independent random walks in our case the prior is derived from a fully specified general equilibrium model we demonstrate that like the atheoretical random walk priors real business cycle model priors can aid in forecasting

gas wars retail gasoline price fluctuations this paper examines the retail gasoline price fluctuations known as gas wars weekly prices for the los angeles area were examined for the period from january 2 1968 through december 23 1975 the observed fluctuations were remarkably regular and apparently large ly predictable the data appear consistent with the recent game theory model of e maskin and j tirole in which competing firms produce edgeworth cycles by undercutting each other and eventually relenting copyright 1993 by mit press

federal reserve information and the behavior of interest rates this paper tests for the existence of asymmetric information between the federal reserve and the public by examining federal reserve and commercial inflation forecasts it demonstrates that the federal reserve has considerable information about inflation beyond what is known to commercial forecasters it also shows that monetary policy actions provide signals of the federal reserve s information and that commercial forecasters modify their forecasts in response to those signals these findings may explain why long term interest rates typically rise in response to shifts to tighter monetary policy jel e52 e43 d82 asymmetric information between the federal reserve and the public is a phenomenon that is often posited but rarely tested numerous models of central bank behavior for example show that the existence of asymmetric information has important implications for the effectiveness of policy and the consequences of dynamic inconsistency yet there

words are the new numbers a newsy coincident index of the business cycle i construct a daily business cycle index based on quarterly gdp growth and textual information contained in a daily business newspaper the newspaper data are decomposed into time series representi

real time data and fiscal policy analysis a survey of the literature abstractthis paper surveys the empirical research on scal policy analysis based on real timedata this literature can be broadly divided in three groups that focus on 1 the statisticalproperties of revisions in scal data 2 the political and institutional determinants of scaldata revisions and of one year ahead projection errors by governments and 3 the reactionof scal policies to the business cycle it emerges that rst scal data revisions are largeand initial releases are biased estimates of nal values second the presence of strong scalrules and institutions leads to relatively more accurate releases of scal data and smalldeviations of scal outcomes from government plans third the cyclical stance of scalpolicies is estimated to be more counter cyclical when real time data are used instead ofex post data finally more work is needed for the development of real time datasets for scalpolicy analysis in particular a comprehensive real time dataset including scal variablesfor industrialized and possibly developing countries published and maintained by centralbanks or other institutions is still missing

facts and challenges from the great recession for forecasting and macroeconomic modeling this paper provides a survey of business cycle facts updated to take account of recent data emphasis is given to the great recession which was unlike most other postwar recessions in the united states in being driven by deleveraging and financial market factors we document how recessions with financial market origins are different from those driven by supply or monetary policy shocks this helps explain why economic models and predictors that work well at some times do poorly at other times we discuss challenges for forecasters and empirical researchers in light of the updated business cycle facts

have standard vars remained stable since the crisis small or medium scale vars are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism this requires the var parameters to be stable over the evaluation and forecast sample or to explicitly consider parameter time variation the earlier literature focused on whether there were sizable parameter changes in the early 1980s in either the conditional mean or variance parameters and in the subsequent period till the beginning of the new century in this paper we conduct a similar analysis but focus on the effects of the recent crisis using a range of techniques we provide substantial evidence against parameter stability the evolution of the unemployment rate seems particularly different relative to its past behavior we then discuss and evaluate alternative methods to handle parameter instability in a forecasting context while none of the methods clearly emerges as best some techniques turn out to be useful to improve the forecasting performance

investment shocks and business cycles abstract the origins of business cycles are still controversial among macroeconomists this paper contributes to this debate by studying the driving forces of fluctuations in an estimated new neoclassical synthesis model of the u s economy in this model most of the variability of output and hours at business cycle frequencies is due to shocks to the marginal efficiency of investment imperfect competition and to a lesser extent technological frictions are the key to their transmission although labor supply shocks explain a large fraction of the fluctuations in hours at very low frequencies they are irrelevant over the business cycle this finding is important because the microfoundations of these disturbances are widely regarded as unappealing

the yield spread puzzle and the information content of spf forecasts while the yield spread has long been recognized as a good predictor of recessions it seems to have been largely overlooked by professional forecasters we examine this puzzle established by rudebusch and williams 2009 in a data rich environment including not just the yield spread but many other predictors as well we confirm the puzzle in this context by examining the contributions of both the spf forecasts and the yield spread in predicting recessions and by examining the information content of spf forecasts directly furthermore we take the first step towards a possible resolution of this puzzle by recognizing the heterogeneity across professional forecasters

estimating the euler equation for output new keynesian macroeconomic models have generally emphasized that expectations of future output are a key factor in determining current output the theoretical motivation for such forward looking behavior relies on a straightforward generalization of the well known euler equation for consumption in this paper we use maximum likelihood and generalized method of moments gmm methods to explore the empirical importance of output expectations we find little evidence that rational expectations of future output help determine current output especially after taking into account the small sample bias in gmm

combining judgment and models this paper proposes a parsimonious and model consistent method for combining forecasts generated by structural microfounded models and judgmental forecasts the method delivers along several dimensions first it improves the forecasting performance of the model second it allows interpreting the judgmental forecasts through the lens of the model finally it provides a framework to assess the informational content of the judgmental forecasters i illustrate the proposed methodology with a real time forecasting exercise using a simple neo keynesian dynamic stochastic general equilibrium model and the survey of professional forecasters 2010 the ohio state university

are daily financial data useful for forecasting gdp evidence from mexico this article evaluates the use of financial data sampled at high frequencies to improve short term forecasts of quarterly gdp for mexico the model uses both quarterly and daily sampling frequencies while remaining parsimonious in particular the mixed data sampling midas regression model is employed to deal with the multi frequency problem to preserve parsimony factor analysis and forecast combination techniques are used to summarize the information contained in a data set containing 392 daily financial series our findings suggest that the midas model incorporating daily financial data leads to improvements in quarterly forecasts of gdp growth over traditional models that either rely only on quarterly macroeconomic data or average daily frequency data the evidence suggests that this methodology improves the forecasts for the mexican gdp notwithstanding its higher volatility relative to that of developed countries furthermore we explore the ability of the midas model to provide forecast updates for gdp growth nowcasting

forecasting core inflation the case of south africa forecasting and estimating core inflation has recently gained attention especially for inflation targeting countries following research showing that targeting headline inflation may not be optimal a central bank can miss the signal due to the noise despite its importance there is sparse literature on estimating and forecasting core inflation in south africa with the focus still on measuring core inflation this paper emphasises predicting core inflation using large time varying parameter vector autoregressive models tvp vars factor augmented var and structural break models using quarterly data from 1981q1 to 2013q4 we use mean squared forecast errors msfe and predictive likelihoods to evaluate the forecasts in general we find that i small tvp vars consistently outperform all other models ii models where the errors are heteroscedastic do better than models with homoscedastic errors iii models assuming that the forgetting factor remains 0 99 throughout the forecast period outperforms models that allow for the forgetting factors to change with time and iv allowing for structural break does not improve the predictability of core inflation overall our results imply that additional information on the growth rate of the economy and interest rate is sufficient to forecast core inflation accurately but the relationship between these three variables needs to be modelled in a time varying nonlinear fashion

has monetary policy become more effective we investigate the implications of changes in the structure of the u s economy for monetary policy effectiveness estimating a vector autoregression over the pre and post 1980 periods we provide evidence of a reduced effect of monetary policy shocks in the latter period we estimate a structural model that replicates well the economy s response in both periods and perform counterfactual experiments to determine the source of the change in the monetary transmission mechanism and in the economy s volatility we find that by responding more strongly to inflation expectations monetary policy has stabilized the economy more effectively in the post 1980 period

retail gasoline price cycles across spatially dispersed gasoline stations abstract studies of volatility in retail gasoline prices have used either weekly averages of the prices at a small subset of stations in a city or daily observations on prices at a small number of stations this paper uses daily observations on station specific prices within a large canadian metropolitan area to address whether weekly average prices or prices for a small number of stations provide an accurate description of pricing in a market with volatility this paper considers whether researchers using restricted data can draw meaningful conclusions about the appropriate theoretical model and the policy response to allegations of anticompetitive behavior

markov switching midas models this article introduces a new regression model markov switching mixed data sampling ms midas that incorporates regime changes in the parameters of the mixed data sampling midas models and allows for the use of mixed frequency data in markov switching models after a discussion of estimation and inference for ms midas and a small sample simulation based evaluation the ms midas model is applied to the prediction of the u s economic activity in terms of both quantitative forecasts of the aggregate economic activity and the prediction of the business cycle regimes both simulation and empirical results indicate that ms midas is a very useful specification

mixed frequency structural models identification estimation and policy analysis summary the mismatch between the timescale of dsge dynamic stochastic general equilibrium models and the data used in their estimation translates into identification problems estimation bias and distortions in policy analysis we propose an estimation strategy based on mixed frequency data to alleviate these shortcomings the virtues of our approach are explored for two monetary policy models copyright 2014 john wiley sons ltd

the external finance premium and the macroeconomy us post wwii evidence abstract the central variable of theories of financial frictions the external finance premium is unobservable this paper distils the external finance premium from a dynamic stochastic general equilibrium dsge model estimated on us macroeconomic data covering the period 1954 to 2004 within the dsge framework movements in the premium can be given an interpretation in terms of shocks driving business cycles a key result is that the estimate based solely on non financial macroeconomic data picks up over 70 of the dynamics of lower grade corporate bond spreads the paper also identifies a gain in fitting key macroeconomic aggregates by including financial frictions in the model and documents how shock transmission is affected

monetary policy in a data rich environment abstract most empirical analyses of monetary policy have been confined to frameworks in which the federal reserve is implicitly assumed to exploit only a limited amount of information despite the fact that the fed actively monitors literally thousands of economic time series this article explores the feasibility of incorporating richer information sets into the analysis both positive and normative of fed policymaking we employ a factor model approach developed by stock j h watson m w diffusion indices journal of business economic statistics 2002 20 2 147 forecasting inflation 1999 journal of monetary economics 44 2 293 that permits the systematic information in large data sets to be summarized by relatively few estimated factors with this framework we reconfirm stock and watson s result that the use of large data sets can improve forecast accuracy and we show that this result does not seem to depend on the use of finally revised as opposed to real time data we estimate policy reaction functions for the fed that take into account its data rich environment and provide a test of the hypothesis that fed actions are explained solely by its forecasts of inflation and real activity finally we explore the possibility of developing an expert system that could aggregate diverse information and provide benchmark policy settings

euro area business cycles in turbulent times convergence or decoupling we study the business cycle properties of the four largest european economies in the wake of the recent recession episodes the analysis is based on the factors estimated from a multi country and multi sector data rich environment we measure alikeness of business cycles by studying the synchronization of up and down phases the convergence properties of country fluctuations towards the euro area ea cycles and the contribution of the ea factor to national gdp volatilities while the economic fluctuations of the four ea member states were similar before the global financial turmoil we gather compelling evidence of an asymmetric behaviour of spanish fluctuations relative to the ea one

what are the macroeconomic effects of high frequency uncertainty shocks this paper evaluates the effects of high frequency uncertainty shocks on a set of low frequency macroeconomic variables that are representative of the u s economy rather than estimating models at the same common low frequency we use recently developed econometric models which allows us to deal with data of different sampling frequencies

short term forecasting with mixed frequency data a midasso approach abstractin this article we extend the targeted regressor approach suggested in bai and ng 2008 for variables sampled at the same frequency to mixed frequency data our midasso approach is a combination of the unrestricted mixed frequency data sampling approach u midas see foroni et al 2015 castle et al 2009 bec and mogliani 2013 and the lasso type penalized regression used in bai and ng 2008 called the elastic net zou and hastie 2005 we illustrate our approach by forecasting the quarterly real gdp growth rate in switzerland

testing the implications of long run neutrality for monetary business cycle models this paper compares sample fluctuations of the us business cycle with those predicted by a class of equilibrium monetary business cycle models the predictions of the models are generated using the longrun neutrality restrictions implicit in the models by imposing these restrictions on sample data tests of the ability of the models to replicate the dynamics of the us business cycle are constructed although the predictions of the models for real side variables are rejected there is evidence that the nominal side predictions of the models are not rejected

factor augmented varma models with macroeconomic applications we study the relationship between vector autoregressive moving average varma and factor representations of a vector stochastic process we observe that in general vector time series and factors cannot both follow finite order var models instead a var factor dynamics induces a varma process while a var process entails varma factors we propose to combine factor and varma modeling by using factor augmented varma favarma models this approach is applied to forecasting key macroeconomic aggregates using large u s and canadian monthly panels the results show that favarma models yield substantial improvements over standard factor models including precise representations of the effect and transmission of monetary policy

real time forecasting with a mixed frequency var this article develops a vector autoregression var for time series which are observed at mixed frequencies quarterly and monthly the model is cast in state space form and estimated with bayesian methods under a minnesota style prior we show how to evaluate the marginal data density to implement a data driven hyperparameter selection using a real time dataset we evaluate forecasts from the mixed frequency var and compare them to standard quarterly frequency var and to forecasts from midas regressions we document the extent to which information that becomes available within the quarter improves the forecasts in real time this article has online supplementary materials

long run implications of investment specific technological change an attempt is made to disentangle its effects from the more traditional hicksneutral form of technological progress the balanced growth path for the model is characterized and calibrated to u s national income and product account nipa data the quantitative analysis suggests that investment specific technological change accounts for the major part of growth jel e13 030 041 047

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

priors from general equilibrium models for vars abstract this paper uses a simple new keynesian dynamic stochastic general equilibrium model as a prior for a vector autoregression shows that the resulting model is competitive with standard benchmarks in terms of forecasting and can be used for policy analysis jel classification c11 c32 c53 key words bayesian analysis dsge models forecasting vector autoregressions 1 introduction dynamic stochastic general equilibrium dsge models are popular nowadays in macroeconomics they are taught in virtually every economics ph d program and represent a predominant share of publications in the field yet when it comes to policy making these models are scarcely used at least from a quantitative point of view the main quantitative workhorse for policy making at the federal reserve system is frb us a macro econometric model built in the cowles foundation tradition a style of macroeconomics that is no longer taught in many ph d programs 1 in their decision process fed policy makers rely heavily on forecasting they want to know the expected path of inflation in the next few quarters and by how much a 50 basis point increase in the federal funds rate would affect that path frb us offers answers to these questions answers that many macroeconomists would regard with suspicion given both the lucas 1976 critique and the fact that in general the restrictions imposed by cowles foundation models are at odds with dynamic general equilibrium macroeconomics sims 1980 general equilibrium models on the other hand have a hard time providing alternative answers the fact that these models are perceived to do badly in terms of forecasting as they are scarcely parameterized is perhaps one of the reasons why they are not at the forefront of policy making while progress is being made in the development of dsge models that deliver acceptable forecasts e g smets and wouters 2003 this paper proposes an approach that combines a stylized general equilibrium model with a vector autoregression var to obtain a specification that both forecasts well and is usable for policy analysis specifically the approach involves using prior information coming from a dsge model in the estimation of a vector autoregression loosely speaking this prior can be thought of as the result of the following exercise i simulate time series data from the dsge model ii fit a var to these data in practice we replace the sample moments of the simulated data by population moments computed from the dsge model solution since the dsge model depends on unknown structural parameters we use a hierarchical prior in our analysis by placing a distribution on the dsge model parameters a tightness parameter controls the weight of the dsge model prior relative to the weight of the actual sample markov chain monte carlo methods are used to generate draws from the joint posterior distribution of the var and dsge model parameters the paper shows that the approach makes even a fairly stylized new keynesian dsge model competitive with standard benchmarks in terms of forecasting real output growth inflation and the nominal interest rate the three variables that are of most interest to policy makers 2 up to this point our procedure borrows from the work of dejong ingram and whiteman 1993 and ingram and whiteman 1994 who are the first to use priors from dsge models for vars ingram and whiteman showed that prior information from the bare bones stochastic growth model of king plosser and rebelo 1988 is helpful in forecasting real economic activity such as output consumption investment and hours worked in addition to documenting the forecasting performance of a trivariate var with a prior derived from a monetary dsge model this paper makes two contributions that significantly extend the earlier work first we show formally how posterior inference for the var parameters can be translated into posterior inference for dsge model parameters

forecasting oil price realized volatility using information channels from other asset classes motivated from ross 1989 who maintains that asset volatilities are synonymous to the information flow we claim that cross market volatility transmission effects are synonymous to cross market information flows or information channels from one market to another based on this assertion we assess whether cross market volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting we concentrate on realized volatilities derived from the intra day prices of the brent crude oil and four different asset classes stocks forex commodities and macro which represent the different information channels by which oil price volatility is impacted from we employ a har framework and estimate forecasts for 1 day to 66 days ahead our findings provide strong evidence that the use of the different information channels enhances the predictive accuracy of oil price realized volatility at all forecasting horizons numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results

the alert and creative entrepreneur a clarification israel m kirzner is the 2006 winner of the international award for entrepreneurship and small business research the fsf nutek award in this prize lecture he argues that a number of those who have commented on his work have misunderstood certain aspects of his theoretical system and as a result the common distinction in the literature between schumpeterian and kirznerian entrepreneurs is flawed he also argues that his understanding of the market process set in motion by entrepreneurial decisions provides a theoretical underpinning for public policy vis a vis entrepreneurship professor kirzner s main contributions to the economics of entrepreneurship were also presented and evaluated by douhan et al small business economics 29 1 2 213 223 2007

the effectiveness of non standard monetary policy measures evidence from survey data we assess the perception of professional forecasters regarding the effectiveness of unconventionalmonetary policy measures undertaken by the u s federal reserve after the collapse of lehmanbrothers using individual survey data we analyse the changes in forecasting of bond yields aroundthe announcement and implementation dates of non standard monetary policies the resultsindicate that bond yields are expected to drop significantly for at least one year after theannouncement and the implementation of accommodative policies

on the fit of new keynesian models this article provides new tools for the evaluation of dynamic stochastic general equilibrium dsge models and applies them to a large scale new keynesian model we approximate the dsge model by a vector autoregression and then systematically relax the implied cross equation restrictions and document how the model fit changes we also compare the dsge model s impulse responses to structural shocks with those obtained after relaxing its restrictions we find that the degree of misspecification in this large scale dsge model is no longer so large as to prevent its use in day to day policy analysis yet is not small enough to be ignored

divisia monetary aggregates and us gdp nowcasting this article nowcasts us quarterly real gdp growth rate with dynamic factor model dfm using divisia monetary aggregate index divisia m1 m2 m3 and exploits information from a large unbalanced

financial conditions indexes for the united states and euro area abstract financial conditions indexes are developed for the united states and euro area using a wide range of financial indicators and a dynamic factor model the financial conditions indexes are shown to be useful for forecasting economic activity and have good revision properties variants of the indexes that allow for cross economy effects reveal very strong financial linkages across the united states and euro area

the meaning of the market process essays in the development of modern austrian economics part i the market process approach 1 market process theory in defence of the austrian middle ground 2 the meaning of the market process part ii the emergence of the austrian view 3 the austrian school of economics 4 carl menger and the subjectivist tradition in economics 5 menger classical liberalism and the austrian school of economics 6 the economic calculation debate lessons for austrians 7 ludwig von mises and friedrich von hayek the modern extension of austrian subjectivism part iii some new explorations in the austrian approach 8 prices the communication of knowledge and the discovery process 9 economic planning and the knowledge problem 10 knowledge problems and their solutions some relevant distinctions 11 welfare economics a modern austrian perspective part iv some related issues emerging from the austrian approach 12 self interest and the new bashing of economics 13 discovery private property and the theory of justice

edgeworth price cycles evidence from the toronto retail gasoline market i exploit a new station level twelve hourly price dataset to examine the strong retail price cycles in the toronto gasoline market the cycles appear similar to theoretical edgeworth cycles strongly asymmetric tall rapid and highly synchronous across stations i test a series of predictions made by the theory about how firm behaviors would differentially evolve over the path of a cycle the evidence is consistent with the existence of edgeworth cycles and inconsistent with competing hypotheses while the cycles are an interesting phenomenon for study in their own right the evidence has important policy and welfare implications

what are the drivers of business demography and employment in the countries of the european union the aim of this contribution is to establish a typology of european entrepreneurship countries with respect to variables related to entrepreneurial activity and economic development using a combin

nowcasting u s headline and core inflation we propose a new and parsimonious model for nowcasting headline and core inflation in the u s price index for personal consumption expenditures pce and the consumer price index cpi

introducing the euro sting short term indicator of euro area growth we set out a model to compute short term forecasts of the euro area gdp growth in real time to allow for forecast evaluation we construct a real time dataset that changes for each vintage date and includes the exact information that was available at the time of each forecast with this dataset we show that our simple factor model algorithm which uses an easy to replicate methodology is able to forecast the euro area gdp growth as well as professional forecasters who can combine the best forecasting tools with the possibility of incorporating their own judgement in this context we provide examples showing how data revisions and data availability affect point forecasts and forecast uncertainty copyright 2010 john wiley sons ltd

israel m kirzner an outstanding austrian contributor to the economics of entrepreneurship israel m kirzner is the 2006 winner of the international award for entrepreneurship and small business research in this essay we present and evaluate his main contributions to the economics of entrepreneurship