The optimal timing of initial public offerings in the course of privatization: Theory and an illustrative application

Privatization has been studied primarily from the standpoint of the post-privatization performance of state-owned firms (SOEs) or of various ways of valuing and privatizing them, but little attention has been paid to the timing of privatization. In this paper we use real options analysis to consider a situation where the government has the option to delay a planned privatization in the expectation that exogenous events may make the firm more valuable to outside investors in the future. In addition to considering the theoretical aspects of the problem, we apply our model to Taiwan's privatization program to add to the understanding of the role of option value in an actual privatization program. Our results suggest that the (mis)timing of privatization represents significant losses for the government and for investors.     

The interplay of entrepreneurial and non-entrepreneurial internationalization: an illustrative case of an Italian SME

This study describes SME internationalization as a process that combines entrepreneurial and non-entrepreneurial behaviour. We bring in insights from prior literature and use an illustrative case study of an Italian SME to demonstrate the interplay of entrepreneurial and non-entrepreneurial behaviour during the internationalization process. Our study shows that the combination of entrepreneurial and non-entrepreneurial internationalization cannot be fully explained with existing International Entrepreneurship literature or other internationalization theories, as they do not take into account the nonlinear nature of internationalization or explain why the process seems to reach a ‘glass ceiling’ beyond which it does not progress. The study concludes with a conceptual framework providing an alternative explanation. Based on our conceptual reasoning we propose that predominantly non-entrepreneurial internationalization can occur without developing a clearly defined internationalization strategy. Additionally, we argue that low commitment to internationalization prevents capability development and may lead to nonlinear internationalization. Furthermore, we suggest that family involvement may moderate a firm’s international entrepreneurial orientation, consequently leading to more non-entrepreneurial internationalization.

     

Testing non-nested semiparametric models: an application to Engel curves specification

This paper proposes a test statistic for discriminating between two partly non-linear regression models whose parametric components are non-nested. The statistic has the form of a J-test based on a parameter which artificially nests the null and alternative hypotheses. We study in detail the realistic case where all regressors in the non-linear part are discrete and then no smoothing is required on estimating the non-parametric components. We also consider the general case where continuous and discrete regressors are present. The performance of the test in finite samples is discussed in the context of some Monte Carlo experiments. The test is well motivated for specification testing of Engel curves. We provide an application using data from the 1980 Spanish Expenditure Survey. © 1998 John Wiley & Sons, Ltd.     

Bayesian semiparametric estimation of discrete duration models: an application of the dirichlet process prior

This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non‐parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline hazard. An application of the model to employment duration data from the Canadian province of New Brunswick is provided. Copyright © 2001 John Wiley & Sons, Ltd.     

Adaptive estimation of cointegrated models: simulation evidence and an application to the forward exchange market

The paper reports simulation and empirical evidence on the finite-sample performance of adaptive estimators in cointegrated systems. Adaptive estimators are asymptotically efficient, even when the shape of the likelihood function is unknown. We consider two representations of cointegrated systems—triangular cointegrating regressions and error correction models. The motivation for and advantages of adaptive estimators in such systems are discussed and their construction is described. We report results from the estimation of a forward exchange market unbiasedness regression using the adaptive and competing estimators, and provide related Monte Carlo simulation evidence on the performance of the estimators. Copyright © 1999 John Wiley & Sons, Ltd.     

Envelope condition method with an application to default risk models

We develop an envelope condition method (ECM) for dynamic programming problems – a tractable alternative to expensive conventional value function iteration (VFI). ECM has two novel features: first, to reduce the cost of iteration on Bellman equation, ECM constructs policy functions using envelope conditions which are simpler to analyze numerically than first-order conditions. Second, to increase the accuracy of solutions, ECM solves for derivatives of value function jointly with value function itself. We complement ECM with other computational techniques that are suitable for high-dimensional problems, such as simulation-based grids, monomial integration rules and derivative-free solvers. The resulting value-iterative ECM method can accurately solve models with at least up to 20 state variables and can successfully compete in accuracy and speed with state-of-the-art Euler equation methods. We also use ECM to solve a challenging default risk model with a kink in value and policy functions.     

Estimation of dynamic models with nonparametric simulated maximum likelihood

We propose an easy-to-implement simulated maximum likelihood estimator for dynamic models where no closed-form representation of the likelihood function is available. Our method can handle any simulable model without latent dynamics. Using simulated observations, we nonparametrically estimate the unknown density by kernel methods, and then construct a likelihood function that can be maximized. We prove that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. The higher-order impact of simulations and kernel smoothing on the resulting estimator is also analyzed; in particular, it is shown that the NPSML does not suffer from the usual curse of dimensionality associated with kernel estimators. A simulation study shows good performance of the method when employed in the estimation of jump-diffusion models.     

Estimation of financial agent-based models with simulated maximum likelihood

This paper proposes a general computational framework for empirical estimation of financial agent-based models, for which criterion functions have unknown analytical form. For this purpose, we adapt a recently developed nonparametric simulated maximum likelihood estimation based on kernel methods. In combination with the model developed by Brock and Hommes (1998), which is one of the most widely analysed heterogeneous agent models in the literature, we extensively test the properties and behaviour of the estimation framework, as well as its ability to recover parameters consistently and efficiently using simulations. Key empirical findings indicate the statistical insignificance of the switching coefficient but markedly significant belief parameters that define heterogeneous trading regimes with a predominance of trend following over contrarian strategies. In addition, we document a slight proportional dominance of fundamentalists over trend-following chartists in major world markets.     

Empirical implementation of nonparametric first-price auction models

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparametric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in standard nonparametric estimation. We further develop methods for automatic bandwidth selection. Finally, we discuss how to impose monotonicity in auctions with differing numbers of bidders, reserve prices, and auction-specific characteristics. Finite sample performance is examined using simulated data as well as experimental auction data.     

Semiparametric models: an evaluation

We review ten years of work on semiparametric theory in statistics on the occasion of the recently published book by Bickel, Klaassen, Ritov and Wellner.     

The discrete Kalman filter applied to linear regression models: statistical considerations and an application

In this paper we show how the Kalman filter, which is a recursive estimation procedure, can be applied to the standard linear regression model. The resulting "Kalman estimator" is compared with the classical least-squares estimator.
The applicability and (dis)advantages of the filter are illustrated by means of a case study which consists of two parts. In the first part we apply the filter to a regression model with constant parameters and in the second part the filter is applied to a regression model with time-varying stochastic parameters. The prediction-powers of various "Kalman predictors" are compared with "least-squares predictors" by using T heil 's prediction-error coefficient U.     

Autocorrelation and dynamic methodology with an application to wage determination models

A demonstration is provided of rigorous, statistical methodology whereby both the type and order of an error process can be identified in dynamic, single equation econometric models. The paper relies heavily upon maximum likelihood estimation, nested likelihood ratio tests and the overfitting or exponentially weighted procedure for model selection. An application of the methodology to a class of quarterly wage determination models is included.     

The comparative forecast performance of univariate and multivariate models: an application to real interest rate forecasting

Does the use of information on the past history of the nominal interest rates and inflation entail improvement in forecasts of the ex ante real interest rate over its forecasts obtained from using just the past history of the realized real interest rates? To answer this question we set up a univariate unobserved components model for the realized real interest rates and a bivariate model for the nominal rate and inflation which imposes cointegration restrictions between them. The two models are estimated under normality with the Kalman filter. It is found that the error-correction model provides more accurate one-period ahead forecasts of the real rate within the estimation sample whereas the unobserved components model yields forecasts with smaller forecast variances. In the post-sample period, the forecasts from the bivariate model are not only more accurate but also have tighter confidence bounds than the forecasts from the unobserved components model.     

Regime switching in stochastic models of commodity prices: An application to an optimal tree harvesting problem

This paper investigates whether a regime switching model of stochastic lumber prices is better for the analysis of optimal harvesting problems in forestry than a more traditional single regime model. Prices of lumber derivatives are used to calibrate a regime switching model, with each of two regimes characterized by a different mean reverting process. A single regime, mean reverting process is also calibrated. The value of a representative stand of trees and optimal harvesting prices are determined by specifying a Hamilton-Jacobi-Bellman Variational Inequality, which is solved for both pricing models using a implicit finite difference approach. The regime switching model is found to more closely match the behavior of futures prices than the single regime model. In addition, analysis of a tree harvesting problem indicates significant differences in terms of land value and optimal harvest thresholds between the regime switching and single regime models.     

Some cumulative logit models for panel-data analysis: an application to a dynamic analysis of personal efficacy

This paper introduces novel cumulative logit models for the panel-data analysis of transitions among ordered states of a polytomous dependent variable. The models differ from conventional cumulative logit models in that they can distinguish between covariate effects on the odds of having an upward transition and covariate effects on the odds of having a downward transition in the ordered states of the dependent variable. The new models are applied to panel data on personal efficacy and are used to identify asymmetric patterns in the effects of divorce and unemployment on changes in the level of personal efficacy. The effects of the two events on increasing the odds of having a downward transition are shown to be greater than their effects on decreasing the odds of having an upward transition. Some distinct characteristics of the effects of each event are also reported.     

Comparative statics in dynamic programming models with an application to job search

This paper presents a technique for qualitative comparative statics analysis in dynamic programming models. Let the value function υ be the fixed point of a contraction mapping which depends differentiably on some exogenous parameter θ. Ten the derivative of υ with respect to θ exists and is also the fixed point of a contraction mapping. Since this derivative is the fixed point of a contraction mapping, its qualitative properties can be investigated using mathematical induction. This comparative statics methodology is illustrated with an application to a model of job search.     

Testing the differences between the determinants of moody's and standard & poor's ratings an application of smooth simulated maximum likelihood estimation

This paper extends previous studies on bond ratings by modeling as a system of equations the determinants of a municipality's decision to obtain a bond rating and the determinants of the municipality's rating for the two major rating agencies. Our model provides a framework to examine formally the differences between the two agencies in the determinants of the ratings. We estimate the four-equation system by smooth simulated maximum likelihood estimation and then construct minimum χ² tests on cross-equation restrictions based on optimal minimum distance estimation. Self-selection is found to be important in Moody's ratings while not in those of S&P. Split ratings appear to reflect differences in both the weight attached to specific determinants of the ratings and differences in the way the bonds are classified.