Speed of convergence of recursive least squares learning with ARMA perceptions

This paper fills a gap in the existing literature on least squareslearning in linear rational expectations models by studying a setup inwhich agents learn by fitting ARMA models to a subset of the statevariables. This is a natural specification in models with privateinformation because in the presence of hidden state variables, agentshave an incentive to condition forecasts on the infinite past recordsof observables. We study a particular setting in which it sufficesfor agents to fit a first order ARMA process, which preserves thetractability of a finite dimensional parameterization, while permittingconditioning on the infinite past record. We describe how previousresults (Marcet and Sargent [1989a, 1989b] can be adapted to handlethe convergence of estimators of an ARMA process in our self--referentialenvironment. We also study ``rates'' of convergence analytically and viacomputer simulation.

Shared knowledge

Agents use their knowledge on the history of the economy in orderto choose what is the optimal action to take at any given moment of time,but each individual observes history with some noise. This paper showsthat the amount of information available on the past evolution of theeconomy is an endogenous variable, and that this leads to overconcentrationof the investment, which can be interpreted as underinvestment in research.It presents a model in which agents have to invest at each period in one of$K$ sectors, each of them paying an exogenous return that follows a welldefined stochastic path. At any moment of time each agent receives an unbiasednoisy signal on the payoff of each sector. The signals differ across agents,but all of them have the same variance, which depends on the aggregate investmentin that particular sector (so that if almost everybody invests in it theperceptions of everybody will be very accurate, but if almost nobody doesthe perceptions of everybody will be very noisy). The degree of hetereogeneityacross agents is then an endogenous variable, evolving across time determining,and being determined by, the amount of information disclosed.As long as both the level of social interaction and the underlying precisionof the observations are relatively large agents behave in a very preciseway. This behavior is unmodified for a huge range of informational parameters,and it is characterized by an excessive concentration of the investment ina few sectors. Additionally the model shows that generalized improvements in thequality of the information that each agent gets may lead to a worse outcomefor all the agents due to the overconcentration of the investment that thisproduces.

Regional growth in Japan

I study the role of internal migration in income convergence acrossregions in Japan. Neoclassical theory predicts that migration should have beenan important source of convergence. Regression results, however, suggest thatmigration did not contribute to convergence. I investigate the possibilitythat this discrepancy is explained by taking into account the effects ofmigration on population composition, especially on educational attainment.I propose an empirical approach to quantify this ``educational compositioneffect''. It is shown that, although this effect did slow down convergence,its magnitude was too small to account for the discrepancy between theoryand empirics.

I just ran four million regressions

In this paper I try to move away from the Extreme Bounds method ofidentifying ``robust'' empirical relations in the economic growth literature.Instead of analyzing the extreme bounds of the estimates of the coefficientof a particular variable, I analyze the entire distribution. My claimin this paper is that, if we do this, the picture emerging from theempirical growth literature is not the pessimistic ``Nothing is Robust''that we get with the extreme bound analysis. Instead, we find that asubstantial number of variables can be found to be strongly relatedto growth.

The effect of unemployment spells on subsequent wages in Spain

In our analysis we try and recover the wage loss from unemploymentin Spain and see how it is affected by previous unemploymentexperience, unemployment duration, eligibility for unemploymentbenefits, and previous wages. We also study its variations acrossgroups. Our main conclusion is that while there is some evidencethat labour market rigidities tend to lower it, the wage loss ofdisplaced workers is remarkably high: more than 30%, that is,twice the equivalent figure for the US and France. Wages in Spainsuffer from a serious mismeasurement problems that we do our best tocontrol, so that our results are less robust than the ones thatwould be obtained with better data sets. However, they indicate a large level of wage flexibility in Spain.

Business cycle measurement with some theory

A method to evaluate cyclical models not requiring knowledge of the DGP and the exact specificationof the aggregate decision rules is proposed. We derive robust restrictions in a class of models; use someto identify structural shocks in the data and others to evaluate the class or contrast sub-models. Theapproach has good properties, even in small samples, and when the class of models is misspecified. Themethod is used to sort out the relevance of a certain friction (the presence of rule-of-thumb consumers)in a standard class of models.

Voting after the bombing: Can terrorist attacks change the outcome of democratic elections?

Economists have recently turned their attention to the effects of terrorism. One much debated effect of terrorist attacks is its impact on the results of democratic elections. We use the electoral consequences of the terrorist attacks of the 11-M in Madrid to analyze this issue. We consider this particular experiment since the attack took place only three days before the 2004 Congressional Election, which allows the use of credible identification criteria. In particular, we use the advance voting by Spanish residents abroad, who cast their vote before the terrorist attack, to identify the effect of the bombing. We exploit this macabre natural experiment to run a difference-in-differences estimation using data on three consecutive Congressional elections. Our empirical results indicate that a terrorist attack can have a large impact on the outcome of democratic elections.