I am a Ph. D. candidate in the Department of Statistics and my supervisor is Esther Ruiz. My interest topics are Econometrics and Time Series Econometrics, Methodology and Applications.
Job Market Paper
"Bootstrap prediction mean squared errors
of unobserved states based on the Kalman filter with estimated parameters"
(2009). (With Esther Ruiz)
Abstract: In the context of linear state space models with Gaussian
errors and known parameters, the Kalman lter generates best linear unbiased
predictions of the un- derlying components together with their corresponding
prediction mean squared errors (PMSE). However, in practice, the lter is run
by substituting some parameters of the model by consistent estimates. In
these circumstances, the PMSEs obtained from the lter do not take into
account the parameter uncertainty and, consequently, they underestimate the
true PMSEs. In this paper, we propose two new bootstrap procedures to obtain
PMSE of the unobserved states based on ob- taining replicates of the
underlying states conditional on the information available at each moment of
time. By conditioning on the available information, we simplify the
procedure with respect to alternative bootstrap proposals previously
available in the literature. Furthermore, we show that the new procedures
proposed in this paper have better nite sample properties than their
alternatives. To illustrate the results we implement the proposed procedure
for estimating the PMSE of the several key unobservable US macroeconomic
variables, i.e. the output gap, the non accelerating in ation rate of
unemployment (NAIRU), the core in ation and the long-run investment rate. In
particular, we will analyze how taking into ac- count the parameter
uncertainty may change the prediction intervals constructed for those
unobservable macroeconomic variables.
Publications
"Bootstrap
Prediction Intervals in State Space Models" (2009). Journal of Time Series
Analysis, Vol 30, 167-178. (With E. Ruiz)
Abstract: Prediction intervals in State Space models can be obtained by
assuming Gaussian innovations and using the prediction equations of the
Kalman filter, where the true parameters are substituted by consistent
estimates. This approach has two limitations. First, it does not
incorporate the uncertainty due to parameter estimation. Second, the
Gaussianity assumption of future innovations may be inaccurate. To
overcome these drawbacks, Wall and Stoffer (2002) propose to obtain
prediction intervals by using a bootstrap procedure that requires the
backward representation of the model. Obtaining this representation
increases the complexity of the procedure and limits its implementation to
models for which it exists. The bootstrap procedure proposed by Wall and
Stoffer (2002) is further complicated by fact that the intervals are
obtained for the prediction errors instead of for the observations. In
this paper, we propose a bootstrap procedure for constructing prediction
intervals in State Space models that does not need the backward
representation of the model and is based on obtaining the intervals
directly for the observations. Therefore, its application is much simpler,
without loosing the good behavior of bootstrap prediction intervals. We
study its finite sample properties and compare them with those of the
standard and the Wall and Stoffer (2002) procedures for the Local Level
Model. Finally, we illustrate the results by implementing the new
procedure to obtain prediction intervals for future values of a real time
series.
“A Parametric Estimation of Personal Income Distribution in Argentina
Using the Dagum Model”. Special Issue of the Journal of the Inter-American
Statistical Institute (IASI), “Estadística” Volume 55, Numbers 164-165,
2003. (With H. Gertel, R. Giuliodori, P. Auerbach),
Abstract: The paper seeks to apply the Dagum generating model of income
distribution functions to study the location and shape parameters of a
sample distribution function of individual income receivers belonging to
Greater Córdoba, its evolution in 1992-2000, and the impact that the
persistent increase in unemployment exerted on inequality. A comparative
analysis of income distribution between the Capital region of Argentina,
represented by the Greater Buenos Aires, and the rest of the country,
reflected in the analysis of Greater Córdoba, is included.
Article in Edited Books
Analysis of the Short Term Impact of the Argentine Social Assistance Program `Plan Jefes y Jefas' on Income Inequality Applying the Dagum Decomposition Analysis of the Gini Ratio (Working Paper), 2008, with H. Gertel and R. Giuliodori, in Gianni Betti and Achille Lemmi (eds.), Advances on Income Inequality and Concentration Measures, Routledge, UK.
Working Papers
"La contribución de la educación y la diferenciación por sexo en las medidas
de desigualdad del ingreso". Publications of the Universidad de Belgrano,
working paper Nº 111, Buenos Aires, Argentina, 2003. (With H. Gertel and R.
Giuliodori)
"Un ejercicio de descomposición del Coeficiente de Gini para la distribución
del ingreso entre poblaciones con diferente nivel de escolaridad de
Argentina. Año 2002". 2002. (With H. Gertel and R. Giuliodori)
"Does Schooling Contribute to Increase Individuals´ Chances to Access The
More Affluent Income Groups?". 2002. (With H. Gertel and R. Giuliodori)
"Unemployment and income distribution analysis. New evidences using a Dagum
parametric income distribution model". 2001. (With H. Gertel, R. Giuliodori
and P. Auerbach)
"Evaluating equality using parametric income Distribution models. An
exploration of alternative effects using a Dagun Parametric
income distribution model". 2001. (With H. Gertel, R. Giuliodori and P.
Auerbach).
Works in Progress
"Bootstrapping Stochastic
Volatility Processes". (With
E. Ruiz)
"Determining the Source of Heteroscedasticity in Conditional Heteroscedastic
Unobserved Component Models". (With E. Ruiz)