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

the effects of taxation price control and government contracts in oligopoly and monopolistic competition abstract many government contracts with or policies towards oligopolistic sectors essentially involve private firms selling a given proportion or quantity of output to the government at a fixed price p r with the remainder being sold on the open market often this is combined with consumer rationing examples include cement and sugar in india and health housing and defence in many countries the paper investigates the effects of these schemes including sales and excise taxation on prices output and household welfare under oligopoly and monopolistic competition less government control reduced may raise prices and tax shifting can be above or below 100 percent

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

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

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

intergenerational social choice without the pareto principle a concise proof of wilson s theorem for a finite or countable set of individuals is presented a social welfare function satisfying nonimposition and arrow s independence axiom is either null or dictatorial if the set of individuals is infinite then continuity of social preference must be added to the hypothesis in which case the independence axiom alone implies that the social welfare function is either constant or dictatorial

nonlinear forecasting analysis using diffusion indexes an application to japan this paper extends the diffusion index di forecast approach of stock and watson 1998 2002 to the case of possibly nonlinear dynamic factor models when the number of series is large a two step procedure based onthe method of principal components is useful since it allows wide variety of nonlinearity in the factors the factors extracted from a large japanese data suggest some evidence of nonlinear structure furthermore both the linear and nonlinear di forecasts in japan outperform traditional time series forecasts while the linear di forecast in most cases performs as well as the nonlinear di forecast

an empirical study of credit shock transmission in a small open economy in this paper we identify and estimate the dynamic effects of foreign us and national canadian credit shocks in a small open economy we use standard credit spreads as proxies to the external finance premium our first result suggests that the us and canadian credit spreads contain substantial forecasting power for several measures of the canadian real economic activity especially during the recent financial crisis and its aftermath secondly an adverse us credit shock generates a significant and persistent economic slowdown in canada the national external finance premium rises immediately while interest rates credit aggregates output and employment indicators decline variance decomposition reveals that credit shocks have a sizeable effect on real activity measures leading indicators and credit spreads yet the unexpected shocks in domestic credit spreads are not able to generate any significant dynamic response of the real activity once we control for the us credit market conditions

forecasting us real house price returns over 1831 2013 evidence from copula models given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831 2013 this article compares the ability of various univariate copula models relative to standard benchmarks naive and autoregressive models in forecasting real us house price over the annual out of sample period of 1874 2013 based on an in sample of 1831 1873 overall our results provide overwhelming evidence in favour of the copula models normal student s t clayton frank gumbel joe and ali mikhail huq relative to linear benchmarks and especially for the student s t copula which outperforms all other models both in terms of in sample and out of sample predictability results our results highlight the importance of accounting for nonnormality and nonlinearity in the data generating process of real house price returns for the us economy for nearly two centuries of data

does the source of oil price shocks matter for south african stock returns a structural var approach abstract in this paper we investigate the dynamic relationship between different oil price shocks and the south african stock market using a sign restriction structural var approach for the period 1973 01 to 2011 07 the results show that for an oil importing country like south africa stock returns only increase with oil prices when global economic activity improves in response to oil supply shocks and speculative demand shocks stock returns and the real price of oil move in opposite directions the analysis of the variance decomposition shows that the oil supply shock contributes more to the variability in real stock prices the main conclusion is that different oil price shocks affect stock returns differently and policy makers and investors should always consider the source of the shock before implementing a policy and making investment decisions

interactions between real economic and financial sides of the us economy in a regime switching environment this objective of this study is to examine the linkages between real economic and financial variables in the united states in a regime switching environment that accounts explicitly for high volatility in the stock market and high stress in financial markets since the linearity test shows that the linear model should be rejected we employ the markov switching vecm to examine the same objective using the bayesian markov chain monte carlo method the regime dependent impulse response function rdirf highlights the increasing importance of the financial sector of the economy during stress periods the responses and their fluctuations are significantly greater in the high volatility regime than in the low volatility regime

evaluating point and density forecasts of dsge models this paper investigates the accuracy of point and density forecasts of four dsge models for inflation output growth and the federal funds rate model parameters are estimated and forecasts are derived successively from historical u s data vintages synchronized with the fed s greenbook projections point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods despite their common underlying new keynesian modeling philosophy forecasts of different dsge models turn out to be quite distinct weighted forecasts are more precise than forecasts from individual models the accuracy of a simple average of dsge model forecasts is comparable to greenbook projections for medium term horizons comparing density forecasts of dsge models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts

demand forecasting with high dimensional data the case of sku retail sales forecasting with intra and inter category promotional information in marketing analytics applications in or the modeler often faces the problem of selecting key variables from a large number of possibilities for example sku level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts but no research has yet put this well accepted concept into forecasting practice an obvious obstacle is the ultra high dimensionality of the variable space this paper develops a four steps methodological framework to overcome the problem it is illustrated by investigating the value of both intra and inter category sku level promotional information in improving forecast accuracy the method consists of the identification of potentially influential categories the building of the explanatory variable space variable selection and model estimation by a multistage lasso regression and the use of a rolling scheme to generate forecasts the success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space the empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework in general we can improve the forecasting accuracy by 12 6 percent over the model using only the sku s own predictors but of the improvements achieved 95 percent of it comes from the intra category information and only 5 percent from the inter category information the substantive marketing results also have implications for promotional category management

on limiting the market for status signals abstract in many societies individuals care how others view them in their efforts to impress such individuals engage in consumption signals which attempt to establish higher levels of status than their true status type this leads to an inefficiency which offers the possibility of a pareto improving tax policy the impacts of tax policy and benefits on the signalling equilibrium are considered

forecasting house prices for the four census regions and the aggregate us economy in a data rich environment this article considers the ability of large scale involving 145 fundamental variables time series models estimated by dynamic factor analysis and bayesian shrinkage to forecast real house price growth rates of the four us census regions and the aggregate us economy besides the standard minnesota prior we also use additional priors that constrain the sum of coefficients of the var models we compare 1 to 24 months ahead forecasts of the large scale models over an out of sample horizon of 1995 01 2009 03 based on an in sample of 1968 02 1994 12 relative to a random walk model a small scale var model comprising just the five real house price growth rates and a medium scale var model containing 36 of the 145 fundamental variables besides the five real house price growth rates in addition to the forecast comparison exercise across small medium and large scale models we also look at the ability of the optimal model i e the model that produces the minimum average mean squared forecast error f

social welfare functions for economic environments with and without the pareto principle it is shown for the case of private goods economies that every social welfare function satisfying a weak nonimposition condition and the independence of irrelevant alternatives axiom is of one of the following forms it is either null or the class of decisive coalitions is an ultrafilter or the class of anti decisive coalitions is an ultrafilter in the case of a private goods economy with finitely many traders the latter conditions imply the existence of either a dictator or anti dictator by requiring the pareto principle as well it is easily seen that the social welfare function must be dictatorial

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

testing oligopolistic behavior abstract this paper develops an empirical model of short run pricing behavior for firms in a homogeneous product oligopoly it is based on the variable profit function and related output supply and factor demand equations and is used to analyze the behavior of firms in the coffee roasting industry a parametric test of the dominant firm model is developed and applied the hypothesis of dominant firm behavior and cournot behavior are rejected the hypothesis of price taking behavior is rejected for the largest two firms in the industry but is not rejected for all other firms using the estimated pattern of firm behavior an index of overall industry performance is constructed

transitive social choice in economic environments assume a finite society a standard space of allocations of private goods and a domain of profiles of classical economic preferences if social preference is expressed as a continuous preorder then a social welfare function satisfies arrow s independence axiom if and only if it is constant or completely authoritarian an authoritarian rule is one that is either dictatorial or inversely dictatorial copyright 1992 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

a simultaneous equations model of coffee brand pricing and advertising this paper explores the relationship between a differentiated brand s market share and its price in the con text of a model that recognizes the endogeneity of the brand s advertising behavior and pricing decisions the empirical analysis suggests that general foods charged higher prices for its regular grind maxwell house coffee in geographic areas where the brand s market share was relatively large available cross sectional time series data and company documents sug gest that this empirical relationship is attributable to the preference grocery retailers have for putting dominant coffee brands on special rather than cross sectional variations in costs market concentration or consumer tastes i introduction r his paper explores the relationship between a differentiated brand s market share and its price in the context of a model that recognizes the endogeneity of the brand s advertising behav ior and pricing decisions by using price as the measure of economic performance rather than accounting profits and by recognizing that adver tising is determined simultaneously with price the empirical tests reported here avoid some of the more serious problems of structure perfor mance studies moreover by focusing on the rela tionship between a single brand s market share and price this study analyzes the unilateral mar ket power of a differentiated brand as well as the effect of market wide concentration on a brand s prices the empirical analysis focuses on the hypothe sis that the general foods corporation charged higher prices for its regular grind maxwell house coffee in geographic areas where the brand s market share was large during the 1970s we identify a statistically significant positive relation ship between maxwell house s wholesale price

persistence mean reversion and non linearities in the us housing prices over 1830 2013 abstractthe objective of this study is to provide a direct estimate of the degree of persistence of measures of nominal and real house prices for the us economy covering the longest possible annual sample of data namely 1830 2013 the estimation of the degree of persistence accommodates for non linear deterministic trends using chebyshev polynomials in time in general the results show a high degree of persistence in the series along with a component of non linear behaviour in general if we assume uncorrelated errors non linearities are observed in both nominal and real prices but this hypothesis is rejected in favour of linear models for the log transformation of the data however if autocorrelated errors are permitted non linearities are observed in all cases and mean reversion is found in the case of logged prices though given the wide confidence intervals the unit root null hypothesis cannot be rejected in these cases

causal relationship between asset prices and output in the united states evidence from the state level panel granger causality test emirmahmutoglu f bacilar m apergis n simo kengne b d chang t and gupta r causal relationship between asset prices and output in the united states evidence from the state level panel granger causality test regional studies this paper investigates the causal relationship between asset prices and output across us states using a bootstrap panel granger causality approach which allows not only for heterogeneity and cross sectional dependence to be accounted for but also interdependency between asset markets empirical results from a trivariate vector autoregression var comprising real house prices real stock prices and real per capita personal income over 1975 2012 reveal the existence of a unidirectional causality running from both asset prices to output this confirms the leading indicator property of asset prices for the real economy while also substantiating the wealth and or collateral transmission mechanism

the information content of money in forecasting euro area inflation this article contributes to the debate on the role of money in monetary policy by analysing the information content of money in forecasting euro area inflation we compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using bayesian and other estimation techniques we find that money contains relevant information for inflation in some model classes money based new keynesian dynamic stochastic general equilibrium dsge models and vector autoregressions vars incorporating money perform better than their cashless counterparts but there are also indications that the contribution of money has its limits the marginal contribution of money to forecasting accuracy is often small money adds little to dynamic factor models and it worsens forecasting accuracy of partial equilibrium models finally nonmonetary models dominate monetary models in an all out horserace

a multilevel factor model identification asymptotic theory and applications this paper studies a multilevel factor model with global and country factors the global factors affect all individuals while the country factors affect only those within each specific country a sequential procedure to identify the global and country factors separately is proposed in the initial step the global factors are estimated by canonical correlation analysis using this initial estimator the principal component estimators pces of the global and country factors are constructed it is shown that the pces estimate the spaces of the global and country factors consistently and are normally distributed in the limit several information criteria that can estimate the numbers of the country factors are proposed the number of the global factors is assumed to be known extensive simulation results demonstrate that the sequential procedure and the information criteria work well in finite samples the method of this paper is applied to 25 oecd countries to identify international business cycle it is reported that the method extracts a global factor reasonably well

do monetary indicators lead euro area inflation abstract this paper assesses based on euro area data whether the strong in sample long run link between money growth and inflation that has been highlighted by a number of recent empirical studies is exploitable to predict inflation out of sample the results from standard bivariate forecasting models suggest that no monetary nor any other single indicator significantly outperforms a simple benchmark forecast the further analysis shows that it would be premature however to discard based on such evidence the usefulness of monetary and all other indicators first it is shown that based on judgemental adjustments to monetary indicators correcting for the effects of identifiable persistent velocity shifts it appears possible to significantly improve predictive ability in real time second i find that a factor forecasting model combining monetary and economic indicators which can be regarded as a generalized quantity theory or two pillar phillips curve forecasting model delivers a fairly good and stable forecasting performance

arrovian theorems with private alternatives domains and selfish individuals abstract this paper proves that the impossibility theorems of arrow wilson and mas colell and sonnenschein hold if the individual preferences domains are hypersaturating for hypersaturating domains the set of social states has the structure of a cartesian product of sets of private alternatives individuals are selfish and the domain satisfies some further restrictions consumption sets are examples of hypersaturating domains when the individual preferences are continuous convex and monotonic but many other examples make hypersaturating domains meaningful for the analysis of problems involving private alternatives sets incidentally all the theorems hold for the case of divisible public goods

conspicuous consumption snobbism and conformism abstract we develop a model in which consumers purchase a conspicuous good in order to signal high income and thereby achieve greater social status in equilibrium the signalling value of conspicuous consumption depends in an identifiable way on the number of consumers and consumer behaviour is characterized by either snobbism or conformism the market demand curve for the conspicuous good may exhibit a positive slope if consumers are conformist we derive some unconventional policy implications concerning the taxation of luxuries and the voluntary provision of public goods

do financial variables help forecasting inflation and real activity in the euro area abstract this paper uses a large data set consisting of 447 monthly macroeconomic time series concerning the main countries of the euro area to simulate out of sample predictions of the euro area industrial production and the harmonized inflation index and to evaluate the role of financial variables in forecasting we considered two models which allow forecasting based on large panels of time series forni et al rev econom statist 82 2000 540 mimeo 2001b and stock and watson mimeo 1999 performance of both models were compared to that of a simple univariate ar model results show that multivariate methods outperform univariate methods for forecasting inflation at one three six and twelve months and industrial production at one and three months we find that financial variables do help forecasting inflation but do not help forecasting industrial production

the out of sample forecasting performance of nonlinear models of regional housing prices in the us this article provides out of sample forecasts of linear and nonlinear models of us and four census subregions housing prices the forecasts include the traditional point forecasts but also include interval and density forecasts of the housing price distributions the nonlinear smooth transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons but the linear autoregressive and nonlinear smooth transition autoregressive models perform equally at short horizons in addition we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models finally in a dynamic 25 step ex ante and interval forecasting design we once again do not find major differences between the linear and nonlinear models in sum we conclude that when forecasting regional housing prices in the united states generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecast

forecasting aggregate retail sales the case of south africa abstract forecasting aggregate retail sales may improve portfolio investors ability to predict movements in the stock prices of retail chains this paper uses 26 23 single and 3 combination forecasting models to forecast south africa s aggregate seasonal retail sales we use data from 1970 01 2012 05 with 1987 01 2012 05 as the out of sample period unlike the previous literature on retail sales forecasting we not only look at a wide array of linear and nonlinear models but also generate multi step ahead forecasts using a real time recursive estimation scheme over the out of sample period to better mimic the practical scenario faced by economic agents making retailing decisions in addition we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises by considering loss functions that overweight the forecast error in booms and recessions focusing on the results of single models alone shows that their performances differ greatly across forecast horizons and for different weighting schemes with no unique model performing the best across various scenarios however combination forecast models especially the discounted mean square forecast error method which weighs current information more than past not only produced better forecasts but were also largely unaffected by business cycles and time horizons this result along with individual nonlinear models performing better than linear models led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models that not only incorporate learning behavior but also allow the behavioral parameters of the model to be state dependent to account for regime switching behavior across alternative states of the economy

veblen effects in a theory of conspicuous consumption the authors examine conditions under which veblen effects arise from the desire to achieve social status by signaling wealth through conspicuous consumption while veblen effects cannot ordinarily arise when preferences satisfy a single crossing property they may emerge when this property fails in that case budget brands are priced at marginal cost while luxury brands though not intrinsically superior are sold at higher prices to consumers seeking to advertise wealth luxury brands earn strictly positive profits under conditions that would with standard formulations of preferences yield marginal cost pricing the authors explore factors that induce veblen effects and they investigate policy implications copyright 1996 by american economic association

do coffee roasters benefit from high prices of green coffee abstract the german coffee industry has repeatedly made the puzzling statement that it would prefer higher prices of green coffee its main input likewise it supported the maintenance of the coffee tax in this paper an oligopoly model with differentiated products is used to illustrate the possibility of profit raising cost increases the relationship between prices and costs of roasted coffee in germany is estimated and the hypothesis of overshifting is tested in addition the demand for coffee is studied however the empirical analysis does not confirm that increasing costs lead to higher profits alternative explanations for the coffee industry s statements based on long term considerations are also discussed