We study both theoretically and empirically option prices on firms undergoing a cash merger offer. To estimate the merger's success probability, we use a Markov Chain Monte Carlo (MCMC) method using a state space representation of our model. Our estimated probability measure has significant predictive power for the merger outcome even after controlling for variables used in the merger literature. As predicted by the model, a graph of the target firm's implied volatility against the strike price has a kink at the offer price, and the kink's magnitude is proportional to the merger's success probability.

In an empirical study of cash mergers since 1996, we find that the equity options on target firms display a pronounced smile pattern in their implied volatilities which gets more pronounced when the merger success probability gets higher. We propose an arbitrage-free model to analyze option prices for firms undergoing a cash merger attempt. Our formula matches well the observed merger volatility smile. Furthermore, as predicted by the model, we show empirically that the merger volatility smile has a kink at the offer price, and that the magnitude of the kink is proportional to the merger success probability.

This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for heteroskedasticity and spatial correlation (spatial HAC). We provide distributions of commonly used test statistics under “fixed-b” asymptotics, in which HAC smoothing parameters are proportional to the sample size. Under this sequence, spatial HAC estimators are not consistent but converge to nondegenerate limiting random variables that depend on the HAC smoothing parameters, the HAC kernel, and the shape of the spatial region in which the data are located. We illustrate the performance of the “fixed-b” approximation in the spatial context through a simulation example.

This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t 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 size. Using a small number of groups is analogous to ‘fixed-b’ asymptotics of Kiefer and Vogelsang, 2002, Kiefer and Vogelsang, 2005 (KV) for heteroskedasticity and autocorrelation consistent inference. We provide simulation evidence that demonstrates that the procedure substantially outperforms conventional inference procedures.

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 the data are grouped on multiple levels where groups are defined by some observable external classification. We consider “group effects” estimators, where individual specific parameters are assumed common across groups at some level. We provide conditions under which group effects estimates of common parameters are asymptotically unbiased and normal. The conditions suggest a tradeoff between two sources of bias, one due to incidental parameters and the other due to misspecification of unobserved heterogeneity.

We consider estimation of nonlinear panel data models with individual specific fixed effects. Estimation of these models is complicated since estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of all model parameters. We present a penalized objective function that reduces the bias in the resulting point estimates. The penalty function is simple to construct and requires no modification for models with multiple individual specific parameters. We illustrate the approach through a series of simulations that suggest the approach is effective in reducing bias and in an empirical study of insider trading activity.

In this article, we consider identification and estimation of average marginal effects in a correlated random effects model without imposing 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. We leave the functional form of the index function unrestricted subject to smoothness conditions. We present identification results for this model and consider estimation of the marginal effects of interest. We illustrate the approach through a brief empirical example, which considers the relationship between insider trading activity and trading volume.

When a cash merger is announced but not completed, there are two main sources of uncertainty related to the target company: the probability of success and the price conditional on the deal failing. We propose an arbitrage-free option pricing formula that focuses on these sources of uncertainty. We test our formula in a study of all cash mergers between 1996 and 2008 which have suciently liquid options traded on the target company. The estimated success probability is a good predictor of the deal outcome. Our option formula for cash mergers does significantly better than the Black‐ Scholes formula and produces a volatility smile close to the one observed in practice. In particular, we provide an explanation for the kink in the volatility smile and show that the kink increases with the probability of deal success.

InLeachman 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 where fiscal budgeting institutions are strong. Additionally, we find that in conjunction with fiscal strength, greater degrees of federalismpositivelyaffect intertemporal budget management.

In this paper, we consider identication in a correlated random eects model for panel data. We assume that the likelihood for each individual in the panel is known up to a nite dimensional common parameter and an individual specic parameter. We allow the distribution of unobserved individual specic eects to depend on observed explanatory

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 interpreted as penalized maximum likelihood in a frequentist setting. We illustrate the construction and use of the bias-reducing priors in simple examples and a simulation study.

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 presence of such mechanisms. We find that only Norway and the United Kingdom exhibit policy responses that are consistent with our criteria.(JEL H6, E62, C22)

Traditional affine models of the term structure are eminently tractable, but suffer from empirical difficulties. Random field models offer great flexibility in fitting the data, but are widely considered non-implementable unless they are approximated by a low-dimensional system. I develop a state-space estimation framework where both random field and affine models can be estimated by MCMC using the same panel of forward rate data. I find that random field models are much better able to fit the patterns of volatility and correlation in a long historical sample of U.S. Treasury forward rates.