REVISION: Option Pricing and the Probability of Success of Cash Mergers
Date Posted: Jun 09, 2011
When a cash merger is announced but not yet completed, there are two key unobserved variables involved in the target company stock price: the probability of success, and the fallback price, i.e., the price conditional on merger failure. We propose an arbitrage-free model involving these two sources of uncertainty which prices European options on the target company. We empirically test our formula in a study of all cash mergers between 1996 and 2008. The formula matches well the observed volatil
New: Grouped Effects Estimators in Fixed Effects Models
Date Posted: Dec 06, 2010
We consider estimation of nonlinear panel data models with common and individual specific parameters. Fixed effects estimators are known to suffer from the incidental parameters problem, which can lead to large biases in estimates of common parameters. Pooled estimators, which ignore heterogeneity across individuals, are also generally inconsistent. We assume that individuals in our data are grouped on multiple levels. These groups may be based on some external classification (for example,
New: Inference with Dependent Data Using Cluster Covariance Estimators
Date Posted: Nov 14, 2010
This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large. The resulting limiting distributions of the t and Wald statistics are standard t and F distributions where the number of groups plays the role of sample
New: Multicointegration and Sustainability of Fiscal Practices
Date Posted: Aug 22, 2008
Using multicointegration methodology, we develop criteria for testing sustainability of fiscal budgeting processes across all states of nature. Criteria are derived from the optimal control literature where levels and rates of change of a system of variables are determinants of policy response. The appropriate policy response mechanisms are outlined and linked to the multicointegration methodology. We then test government spending and revenue systems of 15 industrialized countries for the presen
New: The Political Economy of Budget Deficits
Date Posted: Oct 22, 2007
In Leachman et al. (2005) we use the multicointegration approach to test for sustainable fiscal budgeting processes in a stochastic setting in 15 industrialized countries. In this paper, we extend the analysis in order to rank these same countries as well as an additional three, according to the degree to which their budget processes are sustainable. Rankings are related to theories regarding the political economy of budget deficits. Evidence clearly indicates that fiscal performance is better w
New: Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model
Date Posted: Sep 12, 2007
In this paper, we consider identification and estimation of average marginal effects in a correlated random coefficients model without imposing strong functional form assumptions on the structural likelihood or the mixing distribution. Identification is achieved through imposing that the mixing distribution depends on observed covariates only through an index function and the assumption that at least three time periods are available for each cross sectional unit. We leave the functional form of
New: Bias Reduction for Bayesian and Frequentist Estimators
Date Posted: Nov 07, 2006
We show that in parametric likelihood models the first order bias in the posterior mode and the posterior mean can be removed using objective Bayesian priors. These bias-reducing priors are defined as the solution to a set of differential equations which may not be available in closed form. We provide a simple and tractable data dependent prior that solves the differential equations asymptotically and removes the first order bias. When we consider the posterior mode, this approach can be interpr
A Penalty Function Approach to Bias Reduction in Non-linear Panel Models with Fixed Effects
Date Posted: Jul 29, 2005
In this paper, we consider estimation of nonlinear panel data models that include individual specific fixed effects. Estimation of these models is complicated by the incidental parameters problem; that is, noise in the estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of the common parameters due to the nonlinearity of the problem. We present a penalty for the objective function that reduces the bias in the resulting point estimates.