Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test

We analyze the quality of macroeconomic survey forecasts. Recent findings indicate that they are anchoring biased. This irrationality would challenge the results of a wide range of empirical studies, e.g., in asset pricing, volatility clustering or market liquidity, which rely on survey data to capture market participants' expectations. We contribute to the existing literature in two ways. First, we show that the cognitive bias is a statistical artifact. Despite highly significant anchoring coefficients a bias adjustment does not improve forecasts' quality. To explain this counterintuitive result we take a closer look at macroeconomic analysts' information processing abilities. We find that analysts benefit from the use of an extensive information set, neglected in the anchoring bias test. Exactly this information advantage drives the misleading anchoring bias test results. Second, we find that the superior information aggregation capabilities enable analysts to easily outperform sophisticated timeseries forecasts and therefore survey forecasts should clearly be favored. --macroeconomic announcements,efficiency of forecasts,anchoring bias,rationality of analysts

Nonparametric estimation of conditional beta pricing models

We propose a new procedure to estimate and test conditional beta pricing models which allows for flexibility in the dynamics of assets' covariances with risk factors and market prices of risk (MPR). The method can be seen as a nonparametric version of the two-pass approach commonly employed in the context of unconditional models. In the first stage, conditional covariances are estimated nonparametrically for each asset and period using the time-series of previous data. In the second stage, time-varying MPR are estimated from the cross-section of returns and covariances, using the entire sample and allowing for heteroscedastic and cross-sectionally correlated errors. We prove the desirable properties of consistency and asymptotic normality of the estimators. Finally, an empirical application to the term structure of interest rates illustrates the method and highlights several drawbacks of existing parametric models

Benchmarking of patents: An application of GAM methodology

The present article reexamines some of the issues regarding the benchmarking of patents using the NBER data base on U.S. patents by generalizing a parametric citation model and by estimating it using GAM methodology. The main conclusion is that the estimated effects differ considerably from sector to sector, and the differences can be estimated nonparametrically but not by the parametric dummy variable approach.USPTO, patent benchmarking, GAM

Nonparametric estimation betas in the Market Model

In this study an alternative nonparametric estimator to the Fama and MacBeth approach for the CAPM estimation is proposed. Betas and risk premiums are estimated simultaneously in order to increase the explanatory power of the proxy for betas. A data driven method is proposed for selecting the smoothness degrees, which are directly related to the subsample sizes. Based on this relation, the traditional estimator is obtained as a particular case. Contrary to the results obtained in other studies our empirical evidence for Spanish market data is favorable to the CAPM.smoothed rolling, betas, CAPM

Nonparametric estimation of conditional beta pricing models

We propose a new procedure to estimate and test conditional beta pricing models which allows for flexibility in the dynamics of assets' covariances with risk factors and market prices of risk (MPR). The method can be seen as a nonparametric version of the two-pass approach commonly employed in the context of unconditional models. In the first stage, conditional covariances are estimated nonparametrically for each asset and period using the time-series of previous data. In the second stage, time-varying MPR are estimated from the cross-section of returns and covariances, using the entire sample and allowing for heteroscedastic and cross-sectionally correlated errors. We prove the desirable properties of consistency and asymptotic normality of the estimators. Finally, an empirical application to the term structure of interest rates illustrates the method and highlights several drawbacks of existing parametric models.Kernel estimation, Locally stationary processes, Time-varying coefficients, Conditional asset pricing models

Tracer-Independent Approaches to Stratosphere-Troposphere Exchange and Tropospheric Air Mass Composition

Two transport processes are examined. The first addresses the interaction between the stratosphere and the troposphere. We perform the first analyses of stratosphere-troposphere exchange using one-way flux distributions; diagnostics are illustrated in both idealized and comprehensive contexts. By partitioning the one-way flux across the thermal tropopause according to stratospheric residence time τ and the regions where air enters and exits the stratosphere, the one-way flux is quantified robustly without being rendered ill-defined by the short-τ eddy-diffusive singularity. Diagnostics are first computed using an idealized circulation model that has topography only in the Northern Hemisphere (NH) and is run under perpetual NH winter conditions; suitable integrations are used to determine the stratospheric mean residence time and the mass fraction of the stratosphere in any given residence-time interval. For the idealized model we find that air exiting the stratosphere in the winter hemisphere has significantly longer mean residence times than air exiting in the summer hemisphere because the winter hemisphere has a deeper circulation and stronger eddy diffusion. The complicated response of mean residence times to increased topography underlines the fact that flux distributions capture the integrated advective-diffusive tropopause-to-tropopause transport, and not merely advection by the residual-mean circulation. Extending one-way flux distributions to non-stationary flow we quantify the seasonal ventilation of the stratosphere using the state-of-the-art GEOSCCM general circulation model subject to fixed present-day climate forcings. From the one-way flux distributions, we determine the mass of the stratosphere that is in transit from the tropical tropopause back to the troposphere, partitioned according to stratospheric residence time and exit location. We find that poleward of 45N, the cross-tropopause flux of air that has resided in the stratosphere three months or less is 34 ± 10 % larger for air that enters the stratosphere in July compared to air that enters in January. During late summer and early fall the stratosphere contains about six times more air of tropical origin that is destined to exit poleward of 45S/N in both hemispheres, after an entry-to-exit residence time of six months or less, than is the case during other times of year. We find that 51 ± 1 % and 39 ± 2 % of the stratospheric air mass of tropical origin, annually averaged and integrated over all residence times, exits poleward of 10N/S in the NH and SH, respectively, with most of the mass exiting downstream of the Pacific and Atlantic storm tracks. The mean residence time of this air is found to be ~ 5.1 years in the NH and ~ 5.7 years in the SH. The second transport process addresses new diagnostics of tropospheric transport. We introduce rigorously defined air masses as a diagnostic of tropospheric transport in the context of an idealized model. The fractional contribution from each air mass partitions air at any given point according to either where it was last in the planetary boundary layer (PBL), or where it was last in contact with the tropopause. The utility of these air-mass fractions in isolating the climate change signature on transport alone is demonstrated for the climate of a dynamical-core circulation model and its response to a specified heating. For an idealized warming that produces dynamical responses that are typical of end-of-century comprehensive model projections, changes in air-mass fractions are order 10% and reveal the model's climate change in tropospheric transport: poleward shifted jets and surface intensified eddy kinetic energy lead to more efficient stirring of air out of the midlatitude boundary layer, suggesting that in the future there may be increased transport of industrial pollutants to the Arctic upper troposphere. Correspondingly, air is less efficiently mixed away from the subtropical boundary layer. The air-mass fraction that had last stratosphere contact at midlatitudes increases all the way to the surface, in part due to increased isentropic eddy transport across the tropopause. A weakened Hadley circulation leads to decreased interhemispheric transport in the model's future climate

The Graphic Arts Industry in Mexico - Training and Educational Aspects

THE GRAPHIC ARTS INDUSTRY IN MEXICO - TRAINING AND EDUCATIONAL ASPECTS Purpose of the Study: The general purpose of the study was to identify the training/educational needs existing in the graphic arts industry in Mexico and to make an assessment of the present situation and how those needs are being met. Specific purposes were to determine how the existing training/educating plans and programs are dealing with the influx of new technology being imported; to examine the type of new technology being purchased, by which sectors and the reasons for capital and equipment expenditures by these sectors; to analyse what problems are inherent in the acquisition of new technology, mainly in the training and retraining of operating personnel; and to inquire on the need for a centralized technical organization which could serve as a liaison between technological changes occurring in the industry and the adaptation of those changes to the Mexican training/educational environment. Procedure: For the purposes of this study, the procedures used were within the framework of conventional research methods. Primary sources directly connected with Mexico were obtained through the initial mailing of questionnaires to Mexican firms and the subsequent field research conducted by personal interviews and obtaining data from governmental and other sources. In addition, surveys of related literature were conducted in the United States through several libraries and government institutions which were consulted for related materials. Findings: The most important findings were: 1. Training/educating facilities in Mexico for the graphic arts need to be increased. Enlargement and improvement of existing programs were deemed important in preparing the work force. 2. Training of workers in the graphic arts industry has traditionally been done through on-the-job training; this approach continues today. 3. New technology is being acquired throughout the industry. Labor-saving and quality considerations were the main factors in acquisition. 4. Manufacturers/suppliers initial training at the time of installation of new equipment was judged deficient in duration and content. 5. There is an increased need for information exchange regarding new equipment, techniques, procedures, etc. 6. The creation of a centralized technical center and the need for such a center to combine training and technological transfer was deemed necessary by all respondents. 7. Training/capacitating efforts are increasingly being focused on the Law of Capacitation and Training which makes the training of workers compulsory and the full responsibility of the owners. 8. More training efforts must be made to upgrade/update instructors\u27 preparation. Conclusions: The following conclusions were derived from the study: 1. More in-depth studies are needed to study the effects of technological change on level of skill needed, occupation redundancy and creation, retraining and employment perspectives. 2. Coordination of present training programs should help in preventing duplication of efforts and in meeting capacitating needs more effectively. 3. The problem of appropriate technology remains real to Mexico; its large labor force requires a labor-intensive approach to employment, counter to the present acquisition of new technology. 4. A better general education will be essential for trainees coming into the industry to enable them to cope with fast changes in technology. 5. There is a need for more technical centers for the graphic arts equipped with modern equipment where a theoretical-practical approach can be instituted

El papel de las mujeres en el proceso de democratización de Túnez

El presente trabajo de investigación pretende hacer un análisis de lo acontecido en Túnez desde finales del 2010, haciendo especial hincapié en el papel de las mujeres tanto en la revolución, como en la era post-Ben Alí. Para ello se tratará de identificar los espacios de participación de mujeres en el proceso de democratización y el acceso a la toma de decisiones, así como analizar la situación actual, las negociaciones que se están llevando a cabo, las estructuras que se están creando y los y las agentes que están configurando el futuro próximo de Túnez, visibilizando el papel que están jugando las ciudadanas tunecinas. A pesar del papel protagonista de las mujeres en los días de la revolución, éstas tienen que combatir en varios frentes para hacer prevalecer sus derechos, afianzar lo conquistado y continuar avanzando hacia una ciudadanía plena. El rechazo al estado moderno que predicaban los antiguos dirigentes del país, el triunfo en las elecciones de un partido islamista, que aunque se muestra moderado presenta entre sus miembros secciones más conservadoras, el resurgimiento de grupos salafistas y la falta de confianza de los partidos políticos en sus compañeras mujeres como lideresas son, entre otras, amenazas que deben afrontar las mujeres tunecinas, que corren el riesgo de que se les robe el protagonismo en esta revolución

Macroeconomic predictions – Three essays on analysts' forecast quality

Macroeconomic expectation data are of great interest to different agents due to their importance as central input factors in various applications. To name but a few, politicians, capital market participants, as well as academics, incorporate these forecast data into their decision processes. Consequently, a sound understanding of the quality properties of macroeconomic forecast data, their quality determinants, as well as potential ways to improve macroeconomic predictions is desirable. This thesis consists of three essays on the quality of analysts’ forecasts. The first essay deals with macroeconomic forecast quality on the consensus level, while the second one investigates individual analysts’ predictions and their quality determinants. In the third essay a bottom-up approach is introduced to derive macroeconomic forecasts from analysts’ predictions at the microeconomic level. It is generally assumed that macroeconomic consensus forecasts provide a reasonable approximation of market participants’ expectations regarding upcoming macroeconomic releases. Research areas in which these expectation data are a central input to isolate the unanticipated news component of a given announcement include studies analyzing the price impact of macroeconomic news in bond markets (e.g., Balduzzi et al., 2001; Gilbert et al., 2010), stock markets (e.g., Boyd et al., 2005; Cenesizoglu, 2011) as well as in foreign exchange markets (e.g., Andersen et al., 2003; Evans and Lyons, 2008). Furthermore, these forecast data are used to study market co-movement (e.g., Albuquerque and Vega, 2009), market volatility (e.g., Beber and Brandt, 2008; Brenner et al., 2009), changes in market liquidity (e.g., Brandt and Kavajecz, 2004; Pasquariello and Vega, 2007, 2009) as well as bond and equity risk premiums (e.g., Savor and Wilson, 2012; Dicke and Hess, 2012). It appears reasonable to assume that macroeconomic consensus forecasts represent market participants’ expectations properly. So far available studies on forecast rationality at the consensus level largely test for general quality properties. They commonly find no evidence of systematic or persistent inefficiencies. In contrast to these previous studies, Campbell and Sharpe (2009) test for a specific behavioral inefficiency, the anchoring bias, first documented by Tversky and Kahneman (1974) in psychological experiments. Transferred to the context of macroeconomic forecasts, anchoring means that analysts put too much importance on last months’ data and therefore underweight meanwhile released relevant information. This behavior implies a false incorporation of all available information into their forecasts. Consequently, a correction, i.e., the efficient use of the entire available information set would yield forecast improvements. Our analysis reveals a counter-intuitive result: We find strong statistical significance for anchoring in most macroeconomic forecast series, but applying a look-ahead bias free estimation and adjustment procedure leads to no systematic forecast improvements. Therefore, our results question the economical significance of the anchoring bias. To provide an explanation for the disconnection of statistical and economical significance, we decompose the anchoring bias test statistic and find that the test is biased itself. While the test assumes a univariate information environment, it neglects the possibility that analysts may provide superior forecasts by using a more comprehensive information set than just the univariate time series itself. Our empirical as well as our simulation results strongly support this explanation for a broad range of macroeconomic series. Our analysis contributes to different strands of literature. First, our results directly add to the scarce literature analyzing the efficiency of macroeconomic survey forecasts by showing that informational advantages of analysts, i.e., the incorporation of related macroeconomic data, enable them to outperform mechanically generated time series forecasts. Furthermore, our results provide motivation for other research areas, such as studies analyzing equity analysts’ outputs, to control for a larger information set, for instance by including earnings information of related companies or information about overall business conditions. Second, our findings strongly support the assumption that macroeconomic survey forecasts represent a reasonable proxy measure for the anticipated information component in macroeconomic releases and consequently justify their use in the above mentioned research areas. Furthermore, our results highlight the danger to test for cognitive biases in a time series context which were previously only tested in controlled experiments. Especially when experiments are conducted in a highly regulated informational setting, i.e., when information given to test participants has to be strictly controlled for, as in anchoring bias experiments, it is questionable whether a direct transfer in a time series setting is possible at all. Future studies analyzing cognitive biases in time series frameworks have to consider carefully whether informational constraints might drive the results and lead to false conclusions. The first essay provides strong evidence for the quality of macroeconomic forecasts at the consensus level, the second essay deals with individual macroeconomic forecasts and analyzes why certain analysts provide better forecasts then others. In particular, we focus on the association between the idiosyncratic predictability of a given macroeconomic indicator and the relation between analyst characteristics and macroeconomic forecast accuracy. Obviously, there might be quality differences on the individual analyst level, i.e., there are more and less precise macroeconomic analysts. Exploiting these quality differences is a desirable task, because academics would obtain better proxy measures for market participants’ expectations, and for investors an information advantage should translate into higher profits. We argue that if an indicator’s idiosyncratic predictability is low, i.e., the series is almost not predictable, for instance due to information constrains and very volatile processes, then analysts’ forecast performance is rather random than systematic because skills cannot take effect. In contrast, if a macroeconomic indicator has a high idiosyncratic predictability, then analysts with certain characteristics benefit from their abilities and skills, and generate more precise forecasts than less skilled analysts. Accordingly, for the unpredictable indicators the relation between analyst characteristics and forecast accuracy should be less pronounced than for the predictable ones. Consequently, we hypothesize that the idiosyncratic predictability of a certain macroeconomic indicator has to be taken into account whenever the relation between analyst characteristics and forecast accuracy is analyzed. So far there is only contradictory evidence concerning differences in individual forecast quality of macroeconomic analysts. While some studies provide evidence for different forecast quality among individual macroeconomic analysts (e.g. Zarnowitz, 1984; McNees, 1987; Zarnowitz and Braun, 1993; Kolb and Stekler, 1996; Brown et al., 2008) other articles come to the opposite conclusion (e.g. Stekler, 1987; Ashiya, 2006). Despite this disagreement, the relation between macroeconomic forecast accuracy differences and analyst characteristics has not been analyzed so far, although the extensive strand of literature analyzing the association of equity analyst characteristics and earnings per share forecast accuracy (e.g. Clement, 1999; Clement and Tse, 2005; Brown and Mohammad, 2010) provides a sound framework for an analysis. Most importantly, we find that model performance heavily depends on the idiosyncratic predictability of macroeconomic indicators. With decreasing idiosyncratic predictability the relevance of analyst characteristics for forecast accuracy diminishes for some characteristics and disappears for others. In terms of economic significance we find substantial differences between macroeconomic indicators with high and low idiosyncratic predictability. Consequently, our results show that the idiosyncratic predictability of a given forecast target has to be taken into account when the association between analyst characteristics and forecast accuracy is analyzed. Our findings have implications for different research areas. Most importantly we directly add to the literature analyzing individual macroeconomic analysts’ forecast performance. We provide evidence that the idiosyncratic predictability of an indicator has to be taken into account if the relation between analyst characteristics and forecast accuracy is analyzed. Differentiation among analysts is only very limited if the figure to be forecasted is virtually unpredictable, because analysts do not benefit from their abilities and experiences. Systematic forecast accuracy differences arise if the forecast target is predictable at all and more skilled analysts have the opportunity to differentiate themselves form less skilled ones based on superior skills. Since there are differences in the predictability of company earnings our framework is transferable. Analogous to our findings for macroeconomic analysts, we expect that idiosyncratic predictability plays an equally important role analyzing the association between equity analysts’ characteristics and their earnings per share forecast performance, i.e., for company earnings with higher idiosyncratic predictability we expect higher heterogeneity in forecast accuracy which can be explained by analyst characteristics. The first two essays provide evidence that macroeconomic predictions are in general of high quality as they incorporate rationally information from various sources. Besides the previously analyzed macroeconomic forecasts, agents such as politicians and employers, also heavily rely on other information, for example, on coincident and leading macroeconomic indicators. Determining the current state of the economy and obtaining sound projections about future overall macroeconomic developments plays an important role in their decision processes. Coincident and leading macroeconomic indicators incorporate a large set of macroeconomic variables as well as stock and bond market measures, e.g., returns and interest rate spreads. However, there is no evidence about how expectations at the microeconomic level relate to expectations at the macroeconomic level. Consequently, an aggregate of microeconomic expectation data, i.e., individual company expectations, are not included in coincident and leading macroeconomic indicators so far. To overcome this shortcoming we introduce a bottom-up approach that aggregates individual company expectations to derive macroeconomic content. Since the development of the entire economy is closely related to the development of its individual parts, among them individual companies, aggregated company information must contain macroeconomic information. Unfortunately, there is no database containing managements’ expectations, however, we use equity analysts’ outputs as proxy measure. Equity analysts’ information sets comprise public macroeconomic-, industry- and company-specific content as well as non-public company-specific information (Grossman and Stiglitz, 1980) and is therefore arguably the best available proxy for managements’ expectations. Regarding the choice of the best analyst’s output we use recommendation changes instead of earnings per share (EPS) changes, because recommendations comprise more information. Besides the one year earnings estimate, recommendations also contain a series of future earnings expectations as well as interest rate and risk premium expectations. We show that aggregated recommendation changes as proxy measure for changing company outlooks have predictive power for overall economic developments. Our results provide evidence that aggregated recommendation changes, which approximate changing expectations about individual companies’ economic prospects, have predictive power for future macroeconomic developments of about one year. Controlling for other well established macroeconomic predictors our results remain robust indicating that our measure contains additional independent information. Consequently, it seems promising to include our new predictor into the set of macroeconomic predictors in future applications. Additionally, we find that EPS changes have no predictive power lending support to our assumption that more forward looking information, as included in recommendation changes, is required if one attempts to forecast future macroeconomic developments. Furthermore, our findings provide the missing link between previous studies showing that aggregated analyst outputs have predictive power for overall stock market developments (Howe et al., 2009) and those showing that the stock market leads the real economy (Stock and Watson, 1998). Our results support the notion that changes in expectations about future company performance rationally determine asset values in advance of overall economic activity changes providing the explanation why stock markets lead the real economy. Overall, the three essays in this thesis advance different strands of literature. We show that macroeconomic consensus forecasts are a reliable proxy measure for market participants’ expectations. Furthermore, our results provide strong evidence that it is dangerous to transfer psychological experiments into time series frameworks without appropriately controlling the informational environment. Additionally, we show that the idiosyncratic predictability of a given forecast objective, i.e. whether a forecast task is satisfyingly feasible at all, has to be taken into account whenever the association between analyst characteristics and forecast accuracy is analyzed. Macroeconomic analysts do only benefit from their superior skills compared to their competitors if the macroeconomic series is idiosyncratically predictable. For unpredictable series, forecast accuracy is rather random than systematic, because superior skills do not systematically translate in better forecasts. Finally, we show that the aggregation of forecasts on the microeconomic level, i.e., company expectations, is a promising approach to extract macroeconomic information. Overall, we conclude that macroeconomic analysts are very efficient information processors and play an important role as intermediaries in financial markets

Teoría de la educación y conocimiento práctico. Sobre la racionalidad práctica de la acción educativa.

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