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

short run forecasting of the euro dollar exchange rate with economic fundamentals abstract we propose a fundamentals based econometric model for the weekly changes in the euro dollar rate with the distinctive feature of mixing economic variables quoted at different frequencies the model obtains good in sample fit and more importantly encouraging out of sample forecasting results at horizons ranging from one week to one month specifically we obtain statistically significant improvements upon the hard to beat random walk model using traditional statistical measures of forecasting error at all horizons moreover our model obtains a great improvement when we use the direction of change metric which has more economic relevance than other loss measures with this measure our model performs much better at all forecasting horizons than a naive model that predicts the exchange rate as an equal chance to go up or down with statistically significant improvements

nowcasting mexico s short term gdp growth in real time a factor model versus professional forecasters we introduce a novel real time database for the mexican economy and propose a small scale mixed frequency dynamic factor model for nowcasting mexico s short term gdp growth in real time we compare our factor based backcasts nowcasts and forecasts with those of the consensus of the survey of professional forecasters during the period from the second quarter of 2008 through the second quarter of 2014 our results suggest that our factor based backcasts nowcasts and forecasts outperform those of the consensus of professional forecasters in real time comparisons despite some structural instability during the 2008 09 crisis and its aftermath in 2010

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

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

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

promoting local foods in small island states the role of information policies ensuring the success of agriculture is at the heart of food security and it is necessary to examine strategies that tackle agricultural development through the production and consumption of sustainable food products one way to increase food security in small island states is to develop local food sectors by increasing consumers awareness about local products with sustainable characteristics we designed an economic experiment to assess consumers willingness to pay wtp for yams with sustainable characteristics the origin the intrinsic quality of an alternative variety and the mode of production the results showed that labelling significantly changes consumers valuation of the different yam profiles consumers wtp for local yams 2 85 kg 1 was significantly higher than for imported yams 1 80 kg 1 while the organic mode of production derives a significant premium if information on production methods has been disseminated the results also suggested that organoleptic characteristics of new varieties should not be overlooked we conclude on how the labelling strategy could be facilitated through a policy targeting the development of the local food sector

local resource reallocation considering practical feasibility a centralized data envelopment analysis approach abstractmany studies have addressed resource allocation issues by using centralized data envelopment analysis models however few of them provided a local resource reallocation plan which may be carried out more easily this article proposes two models to get such plans the first one considers an acceptable percentage of changes in resources to ensure practical feasibility the second one further considers a situation where resources can only be allowed to shift within specific units the proposed models are applied to an empirical study based on a hospital system in taiwan to demonstrate the practical feasibility of the suggested plans we compare our reallocation suggestions to a plan that can make the whole system be perfectly efficient the main conclusion is that the plans derived from our models are more practically feasible than that with perfect efficiency

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

health system effects on cost efficiency in the oecd countries this paper investigates the effects of different health systems on cost efficiency in inpatient health care among the oecd countries the results indicate that public contract systems are more efficient and that public integrated systems are less efficient than public reimbursement systems

real time macroeconomic monitoring real activity inflation and interactions the authors sketch a framework for monitoring macroeconomic activity in real time and push it in new directions in particular they focus not only on real activity which has received most attention to date but also on inflation and its interaction with real activity as for the recent recession the authors find that 1 it likely ended around july 2009 2 its most extreme aspects concern a real activity decline that was unusually long but less unusually deep and an inflation decline that was unusually deep but brief and 3 its real activity and inflation interactions were strongly positive consistent with an adverse demand shock this abstract was borrowed from another version of this item

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

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

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

euromind d a density estimate of monthly gross domestic product for the euro area euromind d is a density estimate of monthly gross domestic product gdp constructed according to a bottom up approach pooling the density estimates of eleven gdp components by output and expenditure type the components density estimates are obtained from a medium size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged edged data structures they reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters and conditional simulation filters for simulating from the predictive distribution of gdp both algorithms process sequentially the data as they become available in real time the gdp density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules

measuring hospital efficiency with frontier cost functions this paper uses a stochastic frontier multiproduct cost function to derive hospital specific measures of inefficiency the cost function includes direct measures of illness severity output quality and patient outcomes to reduce the likelihood that the inefficiency estimates are capturing unmeasured differences in hospital outputs models are estimated using data from the aha annual survey medicare hospital cost reports and medpar we explicitly test the assumption of output endogeneity and reject it in this application we conclude that inefficiency accounts for 13 6 percent of total hospital costs this estimate is robust with respect to model specification and approaches to pooling data across distinct groups of hospitals

business cycle turning points a new coincident index and tests of duration dependence based on a dynamic factor model with regime switching the synthesis of the dynamic factor model of stock and watson 1989 and the regime switching model of hamilton 1989 proposed by diebold and rudebusch 1996 potentially encompasses both features of the business cycle identified by burns and mitchell 1946 1 comovement among economic variables through the cycle and 2 nonlinearity in its evolution however maximum likelihood estimation has required approximation recent advances in multimove gibbs sampling methodology open the way to approximation free inference in such non gaussian nonlinear models this paper estimates the model for u s data and attempts to address three questions are both features of the business cycle empirically relevant might the implied new index of coincident indicators be a useful one in practice do the resulting estimates of regime switches show evidence of duration dependence the answers to all three would appear to be yes

a new coincident index of business cycles based on monthly and quarterly series maximum likelihood factor analysis of time series is possible even when some series are quarterly and others are monthly treating quarterly series as monthly series with missing observations and replacing them with artificial observations independent of the model parameters one can apply the kalman filter to a state space representation of a factor model and evaluate the likelihood function an application to quarterly real gdp and monthly coincident business cycle indicators gives a new coincident index of business cycles the new index is essentially the smoothed estimate of latent monthly real gdp and should improve upon the stock watson index

what experimental protocol influence disparities between actual and hypothetical stated values preferences elicited in hypothetical settings have recently come underscrutiny causing estimates from the contingent valuation method to bechallenged due to perceived hypothetical bias given that the receivedliterature derives value estimates using heterogeneous experimentaltechniques understanding the effects of important design parameters onthe magnitude of hypothetical bias is invaluable in this paper we addressthis issue statistically by using a meta analysis to examine data from 29experimental studies our empirical findings suggest that on averagesubjects overstate their preferences by a factor of about 3 in hypotheticalsettings and that the degree of over revelation is influenced by thedistinction between willingness to pay and willingness to accept publicversus private goods and several elicitation methods

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

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

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

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

aggregate health care expenditures and national income is health care a luxury good it is well known that a strong relationship exists between national expenditures on health care and national income this has been used to suggest that health care is a luxury good and that factors such as the type of health care delivery system in a country are of little importance in determining expenditure levels this paper argues that these implications rely upon the application of microeconomic analysis to macroeconomic data and that this is not appropriate as well as raising questions about the inferences drawn from previous studies new empirical evidence is presented which casts some doubts on previous findings international comparisons are based on purchasing power parity rather than exchange rate conversions underlining the importance of prices as well as quantities in the relationship and leading to the conclusion that the aggregate data show health care to be if anything a necessity rather than a luxury good

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

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

regional output growth in the united kingdom more timely and higher frequency estimates from 1970 output growth estimates for the regions of the uk are currently published at the annual frequency only and are released with a long delay regional economists and policymakers would benefit from having higher frequency and more timely estimates in this paper we develop a mixed frequency vector autoregressive mf var model and use it to produce estimates of quarterly regional output growth temporal and cross sectional restrictions are imposed in the model to ensure that our quarterly regional estimates are consistent with the annual regional observations and the observed quarterly uk totals we use a machine learning method based on the hierarchical dirichlet laplace prior to ensure optimal shrinkage and parsimony in our over parameterised mf var thus this paper presents a new regional quarterly database of nominal and real gross value added dating back to 1970 we describe how we update and evaluate these estimates on an ongoing quarterly basis to publish online at www escoe ac uk regionalnowcasting more timely estimates of regional economic growth we illustrate how the new quarterly data can be used to contribute to our historical understanding of business cycle dynamics and connectedness between regions

energy abatement in chinese industry cost evaluation of regulation strategies and allocation alternatives abstract for chinese industry the costs of different energy consumption abatement scenarios are evaluated by the method of directional distance function these scenarios are based on the combinations of regulation strategies and allocation alternatives the former are sectors and provinces and the latter include the five principles of average intensity share absolute share discriminatory absolute and discriminatory intensity for all the scenarios the quantitative impacts in terms of output potential loss are calculated and compared due to less output potential loss for all the allocation alternatives the sector regulation strategy is shown to be more effective than the province regulation strategy it is also demonstrated that among all the scenarios considered the sector regulation based on the intensity share principle and the province regulation based on the absolute share principle are the two optimal the performances of energy abatement allocation of the 11th and 12th five year plans of china are assessed against the simulated scenarios

factor forecasting using international targeted predictors the case of german gdp abstract papers on factor forecasting are often focused on national data only this paper highlights the role of international data for forecasting german gdp and shows how targeted predictors as proposed by bai and ng forecasting economic time series using targeted predictors journal of econometrics 146 2008 304 317 can improve forecast accuracy

consumers willingness to pay for eco friendly apples under different labels evidences from a lab experiment abstract an experiment was conducted in france to evaluate the impact of health and environmental information on consumers choices between conventional and organic apples results show that additional and precise messages about both pesticides use and pesticides residues significantly impact consumers choices between both products the experiment also studied the effect of a new label signaling apples that only use few pesticides compared to conventional apples with elicited willingness to pay we show that the introduction of this new label increases the average participants surplus whatever the information context for participants because of a higher quality compared to conventional apples and a lower price compared to organic products in order to complement this label a minimum quality standard imposing the use of few pesticides is socially optimal when initial participants knowledge is limited

performance evaluation of the english premier football league with data envelopment analysis this paper uses data envelopment analysis dea to evaluate the performance of english premier league football clubs from 1998 99 to 2002 03 combining sport and financial variables the paper evaluates how close the clubs are relative to the frontier of best practices analysing how they manage sport as well as financial results managerial implications of the research are devised

estimating market demand for fresh yam characteristics using contingent valuation implications for crop breeding and production choices this study uses the box cox bc transformations to examine nonlinearity in price modeling and compare results from several functional specifications in hedonic price analysis of yam in togo based on a sample of 6 402 observations on price and produce characteristics generated through a market experiment it is found that the bc quadratic asymmetric specification is the most suitable function several characteristics are valued more than others and prices vary across species time and market sites some residual symptoms of pest and disease damages on yam tubers reduce their market values tuber weight exhibits a diminishing marginal value and price per kilogram decreases above an optimum size therefore producers do not derive additional reward from extra large tubers we conclude that to effectively access and benefit from urban markets producers should focus on small size low weight and conical shaped tubers which are easy to process and meet the esthetic qualities preferred by urban consumers the implications for research on improved variety development to reduce poverty and for crop and resource management practices are drawn

comparing french and us hospital technologies a directional input distance function approach french and us hospital technologies are compared using directional input distance functions the aggregation properties of the directional distance function allow comparison of hospital industry level performance as well as standard firm level performance with regard to productive efficiency in addition the underlying constituents of efficiency in the short run congestion and technical inefficiency and in the long run scale inefficiency are analysed by decomposing the overall measure by virtue of using the directional distance function it is also possible to obtain an estimate of a lower bound on allocative inefficiency it is found that french and us hospitals use quite different technologies long run scale inefficiencies cause most of the french hospitals inefficiency while short run technical inefficiency is the main source of overall productive inefficiency in the us hospitals

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

the franc shock and swiss gdp how long does it take to start feeling the pain abstractthe article addresses the question on what is the typical time horizon over which a full transmission of movements in the real exchange rate takes place into real economy we base our analysis on the mixed frequency small scale dynamic factor model dfm proposed by siliverstovs in 2012 fitted to the swiss data in this article we augment the benchmark model with the real exchange rate of the swiss franc vis a vis currencies of its 24 trading partners while keeping the rest of model specification intact we are interested in investigating the relationship between the common latent factor representing the swiss business cycle and the real exchange rate we explore the temporal relationship between these two variables by varying the time lag with which the real exchange rate enters the factor model by recording the magnitude and statistical significance of the factor loading coefficient in the equation pertaining to the real exchange rate variable our main conclusion is that the fluctuations in

metro business cycles abstract we construct monthly economic activity indices for the 50 largest u s metropolitan statistical areas msas beginning in 1990 each index is derived from a dynamic factor model based on twelve underlying variables capturing various aspects of metro area economic activity to accommodate mixed frequency data and differences in data publication lags we estimate the dynamic factor model using a maximum likelihood approach that allows for arbitrary patterns of missing data our indices highlight important similarities and differences in business cycles across msas while a number of msas experience sizable recessions during the national recessions of the early 1990s and early 2000s other msas escape recessions altogether during one or both of these periods nearly all msas suffer relatively deep recessions near the recent great recession but we still find significant differences in the depth of recent metro recessions we relate the severity of metro recessions to a variety of msa characteristics and find that msas with less educated populations and less elastic housing supplies experience significantly more severe recessions after controlling for national economic activity we also find significant evidence of dynamic spillover effects in economic activity across msas

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

an econometric characterization of business cycle dynamics with factor structure and regime switching a dynamic factor model with regime switching is proposed as an empirical characterization of business cycles the approach integrates the idea of comovements among macroeconomic variables and asymmetries of business cycle expansions and contractions the first is captured with an unobservable dynamic factor and the second by allowing the factor to switch regimes the model is estimated by maximizing its likelihood function and the empirical results indicate that the combination of these two features leads to a successful representation of the data relative to extant literature this holds for within and out of sample and for both revised and real time data copyright 1998 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

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

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

omitted variable bias and hospital costs this research examines the impact of omitted variables on the accuracy of parametric hospital cost function estimations based on quebec hospital level data we assess the effect of omitted variables resulting from incomplete data on technology and performance measurement and on tests of the cost minimizing behavior of the institution our results show that important characteristics of hospital technology such as returns to scale are extremely sensitive to omitted variable bias similarly estimates of hospital performance are poor indicators of actual performance when data are incomplete

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

value elicitation in retail and laboratory environments abstract an increasing number of studies have begun conducting economic experiments in field rather than laboratory settings we directly compare results from laboratory and field valuation experiments after controlling for unengaged bidders results indicate field valuations were greater than laboratory valuations results are discussed in the context of recent literature on commitment costs

the determinants and effects of health expenditure in developed countries previous studies of international differences in health spending have been restricted to the use of relatively small samples of cross sectional data our objective here is to re examine the results of previous work using a sample of 560 pooled time series and cross section observations results confirm the importance of gdp as a determinant of health spending with an estimated income elasticity at or around unity but also suggest that oecd countries should not be regarded as a single homogeneous group the importance of some non income variables is also confirmed although the direct effect of these factors appears to be small

combining time variation and mixed frequencies an analysis of government spending multipliers in italy summary in this paper we propose a time varying parameter vector autoregression var model with stochastic volatility which allows for estimation on data sampled at different frequencies our contribution is twofold first we extend the methodology developed by cogley and sargent drifts and volatilities monetary policies and outcomes in the post wwii u s review of economic studies 2005 8 262 302 and primiceri time varying structural vector autoregressions and monetary policy review of economic studies 2005 72 821 852 to a mixed frequency setting in particular our approach allows for the inclusion of two different categories of variables high frequency and low frequency into the same time varying model second we use this model to study the macroeconomic effects of government spending shocks in italy over the 1988 q4 2013 q3 period italy as well as most other euro area economies is characterized by short quarterly time series for fiscal variables whereas annual data are generally available for a longer sample before 1999 our results show that the proposed time varying mixed frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations second our empirical analysis suggests that government spending shocks tend to have positive effects on output in italy the fiscal multiplier which is maximized at the 1 year horizon follows a u shape over the sample considered it peaks at around 1 5 at the beginning of the sample it then stabilizes between 0 8 and 0 9 from the mid 1990s to the late 2000s before rising again to above unity during the recent crisis copyright 2015 john wiley sons ltd

an international comparison of hospital efficiency does institutional environment matter the consensus among many health economists is that no meaningful performance differences exist among for profit and non profit hospitals in the us but this topic has continued to be a matter of academic judicial and public policy interest a similar debate has ensued internationally regarding the potential efficiency gains from privatization of public enterprises in this paper we examine empirical evidence from the public highly regulated norwegian hospital sector and the private highly competitive and unregulated california hospital sector to ascertain whether institutional environment and level of market competition significantly affect the degree of productive efficiency in hospitals we compare and discuss the productive efficiency of four similar sets of hospitals operating in different institutional and competitive environments the four samples are carefully matched in the dimensions of sample size hospital size and average lengths of stay heterogeneity in output definition is used to co

hospital mergers who merges with whom merger pairings are categorized based on the ownership status teaching status hospital size caseload severity and geographical distance of merging hospitals to determine the types of hospitals that tend to merge with each other the results show that mergers between two non teaching nonprofit or for profit hospitals occur more often but that only ownership status not teaching status affects the propensity to merge after controlling for other merger pair characteristics this paper also finds that hospitals are more likely to merge with a partner of similar size and close geographical proximity but not necessarily the closest candidate however ownership status not distance between hospitals is the dominant determinant of merger pairs

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

good taste tastes good cultural capital as a determinant of organic food purchase by italian consumers evidence and policy implications abstract policy actions in support of organic food chain productions have so far focused on the supply side whereas poor attention has been paid to the demand side despite the growing interest in consumers attitudes toward organic food with the prospect of a future slowing down of the demand for organic products specific demand side policy measures are called for building on interdisciplinary research on the determinants of organic food purchases we argue that cultural capital is a relevant so far overlooked driver of organic food purchase we apply a heckman two step selection strategy approach to microdata from the latest sample annual survey by the italian national institute of statistics we find that participation in cultural activities has a positive impact on the inclination to purchase organic products to an extent that depends on the social orientation of each cultural activity some policy implications are derived

new index of coincident indicators a multivariate markov switching factor model approach abstract this paper extends the univariate markov switching unobserved component model to the multivariate markov switching factor model of coincident economic indicators an approximate ml method is developed to estimate the model the extracted markov switching factor may be interpreted as the coincident index and is comparable to the stock watson index which differs only by the markov switching component using four constituent series of the doc coincident index for the period january 1960 to june 1992 the proposed model generates recessionary and expansionary periods which are remarkably consistent with the nber chronology of the business cycles from the noisy monthly data this paper also finds that duration dependence does matter

toward a more reliable picture of the economic activity an application to argentina abstract we advocate a dynamic factor model to provide alternative measures of output data using indirect information from economic indicators we apply the method to show evidence of a significant gap between estimated and official measures of argentine gdp since 2007

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

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