Bridging DSGE models and the raw data

A method to estimate DSGE models using the raw data is proposed. The approachlinks the observables to the model counterparts via a flexible specification which doesnot require the model-based component to be solely located at business cycle frequencies,allows the non model-based component to take various time series patterns, andpermits model misspecification. Applying standard data transformations induce biasesin structural estimates and distortions in the policy conclusions. The proposed approachrecovers important model-based features in selected experimental designs. Twowidely discussed issues are used to illustrate its practical use.

Calibration of stochastic volatility models via second order approximation: the Heston model case

Using a suitable Hull and White type formula we develop a methodology to obtain asecond order approximation to the implied volatility for very short maturities. Using thisapproximation we accurately calibrate the full set of parameters of the Heston model. Oneof the reasons that makes our calibration for short maturities so accurate is that we alsotake into account the term-structure for large maturities. We may say that calibration isnot "memoryless", in the sense that the option's behavior far away from maturity doesinfluence calibration when the option gets close to expiration. Our results provide a wayto perform a quick calibration of a closed-form approximation to vanilla options that canthen be used to price exotic derivatives. The methodology is simple, accurate, fast, andit requires a minimal computational cost.

Solving non-linear stochastic models by parameterizing expectations: An application to asset pricing with production

A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic models under rational expectationsis developed and its advantages and disadvantages are discussed. Thisalgorithm can, in principle, approximate the true equilibrium arbitrarilywell. Also, this algorithm works from the Euler equations, so that theequilibrium does not have to be cast in the form of a planner's problem.Monte--Carlo integration and the absence of grids on the state variables,cause the computation costs not to go up exponentially when the numberof state variables or the exogenous shocks in the economy increase. \\As an application we analyze an asset pricing model with endogenousproduction. We analyze its implications for time dependence of volatilityof stock returns and the term structure of interest rates. We argue thatthis model can generate hump--shaped term structures.

A unifying approach to the empirical evaluation of asset pricing models

Two main approaches are commonly used to empirically evaluate linear factor pricingmodels: regression and SDF methods, with centred and uncentred versions of the latter.We show that unlike standard two-step or iterated GMM procedures, single-step estimatorssuch as continuously updated GMM yield numerically identical values for prices of risk,pricing errors, Jensen s alphas and overidentifying restrictions tests irrespective of the modelvalidity. Therefore, there is arguably a single approach regardless of the factors being tradedor not, or the use of excess or gross returns. We illustrate our results by revisiting Lustigand Verdelhan s (2007) empirical analysis of currency returns.

Simple models for multi-attribute choice with many alternatives: When it does and does not pay to face tradeoffs with binary attributes

Working Paper no longer available. Please contact the author.

Adaptive learning in models with lagged variables

In this work I study the stability of the dynamics generated by adaptivelearning processes in intertemporal economies with lagged variables. Iprove that determinacy of the steady state is a necessary condition for the convergence of the learning dynamics and I show that the reciprocal is not true characterizing the economies where convergence holds. In the case of existence of cycles I show that there is not, in general, a relationship between determinacy and convergence of the learning process to the cycle. I also analyze the expectational stability of these equilibria.

In vitro and in vivo tumor models for studies of distribution of radiolabelled monoclonal antibodies and fragments.

Colon carcinoma multicellular spheroids were incubated in vitro with radiolabelled MAbs. The more rapid penetration of fragments as compared to intact MAbs was clearly demonstrated. For the study of antibody localization in tumors in vivo, the model of nude mice with ligated kidneys was used. Although very artificial, this model allowed to demonstrate that, without urinary excretion, Fab fragments accumulated more rapidly into the tumor than intact MAbs and disappeared faster from the blood. This difference was less striking for F(ab')2 fragments. In the liver a decreased accumulation of both types of fragments as compared to intact MAbs was observed. Concerning radioimmunotherapy we think that Fab fragments are not useful because of their too short half-life in the circulation and in tumor and because they will probably be too toxic for the kidneys. Intact MAbs and F(ab')2 fragments have each their advantages. Intact MAbs show highest tumor accumulation in mice without ligated kidney, however, they remain mostly on the periphery of tumor nodules, as shown by autoradiography. F(ab')2 fragments have been found to penetrate deeper into the tumor and to accumulate less in the liver. It might be therefore an advantage to combine intact MAbs with F(ab')2 fragments, so that in the tumor two different regions could be attacked whereas in normal tissues toxicity could be distributed to different organs such as to the liver with intact MAbs and to the kidney with F(ab')2 fragments.