Susan Orbe

A nonparametric method to estimate time varying coefficients under seasonal constraints

This paper proposes a new method to estimate nonparametrically a univariate time varying coefficients model. This estimation procedure allows to incorporate both, seasonal and smoothness constraints. The resulting estimator nests as particular cases many other estimators proposed in the literature. We derive its asymptotic bounds and we also show consistency and the asymptotic distribution. Finally, we illustrate its performance by estimating the Spanish money multiplier and we provide a data driven method to compute the smoothing parameters.

An algorithm to estimate time-varying parameter SURE models under different types of restriction

A nonparametric method to estimate time-varying coefficients in seemingly unrelated regression equations models is proposed. The procedure presents two main advantages with respect to other proposals in literature. First, it allows to incorporate both cross and time-varying restrictions into the parameters. Second, the estimator is obtained in a closed form, and there is no need of an iterative method to compute its value. However, this computation requires to solve a linear system where the number of equations and coefficients increases with the sample size. This problem is overcome by using an algorithm that reduces the computation cost. The algorithm enables to solve the linear system in a recursive manner where (in each step) a lower dimensional linear system is solved and there is no increase with sample size.

Conditional Beta Pricing Models: A Nonparametric Approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.

The Knowledge-Capital Model of FDI: A Time Varying Coefficients Approach

The present article reexamines some of the issues regarding the Knowledge-Capital Model that encompasses both horizontal and vertical Foreign Direct Investment. The empirical support for this model is however mixed. This article proposes a new way of estimating coefficients by allowing them to vary over time. The estimation results obtained using data from 30 OECD countries for the period from 1982 to 2003 confirm that these coefficients cannot be considered as constant over time and that the vertical component of the Knowledge-Capital Model is relevant.

Why are there time-varying comovements in the European stock market?

ABSTRACT This paper analyzes the dynamics of pair comovements between different domestic European stock market returns (Spain, France, Germany, Switzerland and the United Kingdom) seeking to check whether there is a unique source of risk driving those dynamics. Once it is shown that the comovements are time-varying, the question is to find whether a global index such as the Euro Stoxx can be considered the main source of risk. To that end we estimate and test for time-varying global pair covariances and for time-varying remaining pair covariances once the effect of the Euro Stoxx is removed. The empirical results are obtained considering locally stationary variables, a family that includes variables with first and second time-varying moments. Under that framework time-varying means and covariances can be estimated using a spline-based procedure and Wald-type statistics can be computed to test for time-variations. A simulation study shows that the role of the mean estimation part is crucial to the good performance of the tests for second moments. The empirical results evidence that all global pair covariances for the European countries analyzed are time-varying, but also that the Euro Stoxx can be considered as the driving source of risk for these time-varying dynamics. This conclusion is very useful for modeling purpose and financial strategies. Finally, we repeat the analysis considering the Nasdaq as an alternative global index and find that it explains only a small part of the dynamics in the European pair comovements.