Gazelle companies: Growth drivers and an evolution analysis

Gazelle companies are relevant because they generate much more employment than other companies and deliver high returns to their shareholders. This paper analyzes their behavior in the years of high growth and their evolution in the following years. The main factors that explain their success are competitive advantages based on human resources, innovation, internationalization, the excellence in processes and a conservative financial policy. Nevertheless, as time goes by they can be divided in two groups: a group which continues having growth, but most of them with lower growth rates; and the rest which face great problems or even disappear. The present study identifies several key factors that explain this different evolution.

Monetary policy shocks and transmission in Italy: A VAR analysis

This paper provides updated empirical evidence about the real and nominal effects of monetary policy in Italy, by using structural VAR analysis. We discuss different empirical approaches that have been used in order to identify monetary policy exogenous shocks. We argue that the data support the view that the Bank of Italy, at least in the recent past, has been targeting the rate on overnight interbank loans. Therefore, we interpret shocks to the overnight rate as purely exogenous monetary policy shocks and study how different macroeconomic variables react to such shocks.

Generalized canonical correlation analysis of matrices with different row and column orders

A Method is offered that makes it possible to apply generalized canonicalcorrelations analysis (CANCOR) to two or more matrices of different row and column order. The new method optimizes the generalized canonical correlationanalysis objective by considering only the observed values. This is achieved byemploying selection matrices. We present and discuss fit measures to assessthe quality of the solutions. In a simulation study we assess the performance of our new method and compare it to an existing procedure called GENCOM,proposed by Green and Carroll. We find that our new method outperforms the GENCOM algorithm both with respect to model fit and recovery of the truestructure. Moreover, as our new method does not require any type of iteration itis easier to implement and requires less computation. We illustrate the methodby means of an example concerning the relative positions of the political parties inthe Netherlands based on provincial data.

The provision of relative performance feedback information: An experimental analysis of performance and happiness

This paper studies the effect of providing relative performance feedback information onindividual performance and on individual affective response, when agents are rewardedaccording to their absolute performance. In a laboratory set-up, agents perform a realeffort task and when receiving feedback, they are asked to rate their happiness, arousaland feeling of dominance. Control subjects learn only their absolute performance, whilethe treated subjects additionally learn the average performance in the session.Performance is 17 percent higher when relative performance feedback is provided.Furthermore, although feedback increases the performance independent of the content(i.e., performing above or below the average), the content is determinant for theaffective response. When subjects are treated, the inequality in the happiness and thefeeling of dominance between those subjects performing above and below the averageincreases by 8 and 6 percentage points, respectively.

Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis

Many multivariate methods that are apparently distinct can be linked by introducing oneor more parameters in their definition. Methods that can be linked in this way arecorrespondence analysis, unweighted or weighted logratio analysis (the latter alsoknown as "spectral mapping"), nonsymmetric correspondence analysis, principalcomponent analysis (with and without logarithmic transformation of the data) andmultidimensional scaling. In this presentation I will show how several of thesemethods, which are frequently used in compositional data analysis, may be linkedthrough parametrizations such as power transformations, linear transformations andconvex linear combinations. Since the methods of interest here all lead to visual mapsof data, a "movie" can be made where where the linking parameter is allowed to vary insmall steps: the results are recalculated "frame by frame" and one can see the smoothchange from one method to another. Several of these "movies" will be shown, giving adeeper insight into the similarities and differences between these methods.

What explains the Great Moderation in the US? A structural analysis

This paper investigates what has caused output and inflation volatility to fall in the USusing a small scale structural model using Bayesian techniques and rolling samples. Thereare instabilities in the posterior of the parameters describing the private sector, the policyrule and the standard deviation of the shocks. Results are robust to the specification ofthe policy rule. Changes in the parameters describing the private sector are the largest,but those of the policy rule and the covariance matrix of the shocks explain the changes most.

Implementing TURF analysis through binary linear programming

This paper introduces the approach of using Total Unduplicated Reach and Frequency analysis (TURF) to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. The results obtained through our exact algorithm are presented, and this method shows to be extremely efficient both in obtaining optimal solutions and in computing time for very large instances of the problem at hand. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

A scaled difference chi-square test statistic for moment structure analysis

A family of scaling corrections aimed to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model say ${\cal M}_0$ implies on a less restricted one ${\cal M}_1$. If $T_0$ and $T_1$ denote the goodness-of-fit test statistics associated to ${\cal M}_0$ and ${\cal M}_1$, respectively, then typically the difference $T_d = T_0 - T_1$ is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models ${\cal M}_0$ and ${\cal M}_1$. As in the case of the goodness-of-fit test, it is of interest to scale the statistic $T_d$ in order to improve its chi-square approximation in realistic, i.e., nonasymptotic and nonnormal, applications. In a recent paper, Satorra (1999) shows that the difference between two Satorra-Bentler scaled test statistics for overall model fit does not yield the correct SB scaled difference test statistic. Satorra developed an expression that permits scaling the difference test statistic, but his formula has some practical limitations, since it requires heavy computations that are notavailable in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference chi-square statistic from the scaled goodness-of-fit test statistics of models ${\cal M}_0$ and ${\cal M}_1$. A Monte Carlo study is provided to illustrate the performance of the competing statistics.

Analysis of matched matrices

We consider the joint visualization of two matrices which have common rowsand columns, for example multivariate data observed at two time pointsor split accord-ing to a dichotomous variable. Methods of interest includeprincipal components analysis for interval-scaled data, or correspondenceanalysis for frequency data or ratio-scaled variables on commensuratescales. A simple result in matrix algebra shows that by setting up thematrices in a particular block format, matrix sum and difference componentscan be visualized. The case when we have more than two matrices is alsodiscussed and the methodology is applied to data from the InternationalSocial Survey Program.

Measuring asymmetries in brand associations using correspondence analysis

Correspondence analysis is introduced in the brand associationliterature as an alternative tool to measure dominance, for theparticular case of free choice data. The method is also used to analysedifferences, or asymmetries, between brand-attribute associations whereattributes are associated with evoked brands, and brand-attributeassociations where brands are associated with the attributes. Anapplication to a sample of deodorants is used to illustrate the proposedmethodology.

From correspondence analysis to multiple and joint correspondence analysis

The generalization of simple (two-variable) correspondence analysis to more than two categorical variables, commonly referred to as multiple correspondence analysis, is neither obvious nor well-defined. We present two alternative ways of generalizing correspondence analysis, one based on the quantification of the variables and intercorrelation relationships, and the other based on the geometric ideas of simple correspondence analysis. We propose a version of multiple correspondence analysis, with adjusted principal inertias, as the method of choice for the geometric definition, since it contains simple correspondence analysis as an exact special case, which is not the situation of the standard generalizations. We also clarify the issue of supplementary point representation and the properties of joint correspondence analysis, a method that visualizes all two-way relationships between the variables. The methodology is illustrated using data on attitudes to science from the International Social Survey Program on Environment in 1993.

How important are tobacco prices in the propensity to start and quit smoking? An analysis of smoking histories from the Spanish National Health Survey

This paper analyses the effect of tobacco prices on the propensity tostart and quit smoking using a pool of the 1993, 1995 and 1997 editionsof the Spanish National Health Surveys. The estimates for severalparametric models of the hazard rate for starting and quitting suggestthat i) The public health measures applied as of 1992 have had asignificative effect on both reducing the hazard of starting andincreasing the hazard of quitting, ii) Prices have a very weak effect onthe hazard of starting in the male population and no significant effectin the female population, iii) The price floor of cigarrettes, proxiedby the average price of a pack of black cigarrettes, has a significanteffect on the quitting hazard which is robust across specifications andapplies to both men and women. The implied price elasticity of the timeup to quitting is situated around -1.4.

European research funding and regional technological capabilities: Network composition analysis

We use network and correspondence analysis to describe the compositionof the research networks in the European BRITE--EURAM program. Our mainfinding is that 27\% of the participants in this program fall into one oftwo sets of highly ``interconnected'' institutions --one centered aroundlarge firms (with smaller firms and research centers providing specializedservices), and the other around universities--. Moreover, these ``hubs''are composed largely of institutions coming from the technologically mostadvanced regions of Europe. This is suggestive of the difficulties of attainingEuropean ``cohesion'', as technically advanced institutions naturally linkwith partners of similar technological capabilities.

The new Keynesian approach to monetary policy analysis: Lessons and new directions

I discuss several lessons regarding the design and conduct of monetary policy that have emerged out of the New Keynesian research program. Those lessons include the bene.ts of price stability, the gains from commitment about future policies, the importance of nat-ural variables as benchmarks for policy, and the bene.ts of a credible anti-inflationary stance. I also point to one challenge facing NK modelling efforts: the need to come up with relevant sources of policy tradeoffs. A potentially useful approach to meeting that challenge, based on the introduction of real imperfections, is presented.