The Effect of Protest Votes on the Estimates of WTP for Use Values of Recreational Sites

Contingent valuation studies are often characterized by a considerable number of protest responses, which may cause selectivity bias on the final estimates for WTP. Sample selection models can detect and – if necessary – correct selectivity bias. In economic applications where the relevant dependent variable is continuous, sample selection models are generally estimated using Heckman's 2-step method rather than the FIML estimator. Either method has its own drawback: computational complexity for the FIML method, susceptibility to collinearity problems for the 2-step method. Using data on valuation of forest resources for recreational use, we analyse the performance of the two estimators. In this application, given the presence of some collinearity, the FIML is preferred to the 2-step method. A procedure is outlined to deal with selectivity problems in similar settings.     

Approximate Whittle analysis of fractional cointegration and the stock market synchronization issue

I consider a bivariate stationary fractional cointegration system and I propose a quasi-maximum likelihood estimator based on the Whittle analysis of the joint spectral density of the regressor and errors. This allows to estimate jointly all parameters of interest of the model. I lead a Monte Carlo experiment to investigate the finite sample properties of this estimator when integration orders are less than 1/2. However, it is not so easy for practitioners to identify whether or not the observed time series are stationary. This issue is investigated by extending the numerical analysis to mean-reverting non-stationary region of the parameter space, although the proposed estimator is not theoretically designed to handle this case. The results display good finite sample properties in both cases, stationary and non-stationary. Thereby, it reveals that making a wrong decision on the stationarity of raw series does not lead to an erroneous conclusion. An application to the stock market synchronization is proposed to illustrate the empirical relevance of this estimator.     

Estimation of Censored Regression Model: A Simulation Study

We investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including Honoré estimator, Hansen’s best two-step GMM estimator, the continuously updating GMM estimator, and the empirical likelihood estimator (ELE). The latter three estimators are based on more conditional moment restrictions than the Honoré estimator, and consequently are more efficient in large samples. Although the latter three estimators are asymptotically equivalent, the last two have better finite sample performance. However, our simulation reveals that the continuously updating GMM estimator performs no better, and in most cases is worse than Honoré estimator in small samples. The reason for this finding is that the latter three estimators are based on more moment restrictions that require discarding observations. In our designs, about seventy percent of observations are discarded. The insufficiently few number of observations leads to an imprecise weighted matrix estimate, which in turn leads to unreliable estimates. This study calls for an alternative estimation method that does not rely on trimming for finite sample panel data censored regression model.     

Stability of Risk Premia in the Italian Stock Market

In this work, we estimate the arbitrage pricing theory (APT) on the Italian Stock Market using the reduced-rank regression technique recently propossed by Bekker et al. (1996). Due to its computational simplicity, this technique allows extensive empirical analysis of the properties of the estimator employed. In this work, we carry out an initial exploration of the cross-sectional stability of the risk premia estimates in relation to the stocks' sample composition. We show that, by choosing an appropriately diversified sample, some acceptable degree of stability may be obtained. We also investigate, using the bootstrap method, the small sample properties of the estimator. (J.E.L.: G11, G12).     

Semiparametric and nonparametric testing for long memory: A Monte Carlo study

The finite sample properties of three semiparametric estimators, several versions of the modified rescaled range, MRR, and three versions of the GHURST estimator are investigated. Their power and size for testing for long memory under short-run effects, joint short and long-run effects, heteroscedasticity andt-distributions are given using Monte Carlo methods. The MRR with the Bartlett window is generally robust with the disadvantage of a relatively small power. The trimmed Whittle likelihood has high power in general and is robust except for large short-run effects. The tests are applied to changes in exchange rate series (daily data) of 6 major countries. The hypothesis of no fractional integration is rejected for none of the series.     

Estimation of the preference heterogeneity within stated choice data using semiparametric varying-coefficient methods

This study proposes the use of semiparametric varying-coefficient methods to estimate the preference heterogeneity within stated choice data. Semiparametric varying-coefficient methods have the potential to overcome the disadvantages of conventional random parameter models and latent class models. For binary probit models with varying coefficients, in particular, this study proposes an easy-to-compute local iterative least squares (LILS) approach, based on the expectation–maximization algorithm. The finite sample properties of the LILS estimator are assessed using Monte Carlo experiments. In order to demonstrate the practical usefulness of semiparametric varying-coefficient methods, we present an empirical study, conducting an economic valuation of a landscape with dichotomous choice contingent valuations.     

ENGLE AND YOO THREE STEP ESTIMATION OF CONSUMERS' EXPENDITURE AND HOUSING EQUITY WITHDRAWAL

This paper reports a substantive application of Engle and Yoo's three-step estimator for cointegrated systems. Their estimator was proposed as a computationally convenient alternative to a number of FIML systems estimators. In part this estimator was developed to overcome some drawbacks of the OLS estimator of the first stage Engle-Granger cointegrating regression which, despite its widespread use, is not asymptotically efficient and does not provide (even) asympototically correct ' t ' statistics. Our application, which is of interest in its own right, is to an explanation of expenditure on nondurables and services in the UK. In formulating an empirical explanation for this variable we find it necessary to extend our framework to consider a system of dynamic error correction equations with feedbacks — or error correction mechanisms — from consumption and housing equity withdrawal.     

Using mean reversion as a measure of persistence

This paper suggests a new scalar measure of persistence together with a companion estimator, which has the advantage of not requiring the specification and estimation of a model for the series under investigation. The statistical properties of the companion estimator are established, which allow tests of hypotheses to be performed, under very general conditions. The use of the new measure is illustrated by re-evaluating persistence of inflation for the United States and the Euro Area. The conclusions for the United States do not differ significantly from what has been found in previous empirical studies. However, for the Euro Area we find evidence of a significant break occurring in 2001/2002, such that persistence becomes virtually nil for the period that follows the launch of the euro and the implementation of a common monetary policy by the European Central Bank.     

A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in this paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate- or large-sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. We provide results concerning the large and small sample properties of this estimator.     

Small sample bias in conditional sum-of-squares estimators of fractionally integrated ARMA models

This paper considers estimation of the parameters for the fractionally integrated class of processes known as ARFIMA. We consider the small sample properties of a conditional sum-of-squares estimator that is asymptotically equivalent to MLE. This estimator has the advantage of being relatively simple and can estimate all the parameters, including the mean, simultaneously. The simulation evidence we present indicates that estimation of the mean can make a considerable difference to the small sample bias and MSE of the other parameter estimates.     

An investigation into a non-linear stochastic trend model

The paper explores the empirical properties of a non-linear stochastic trend model which can be viewed as an intermediate case between a linear and a log-linear trend model. I assess the small sample distribution of the ML estimator by Monte Carlo simulations and use it to model some typical macroeconomic time series. The non-linear trend model turns out to be an important tool which warrants further analysis. I also compute impulse response functions and compare them with those obtained from a conventional linear model. First version received: August 1997/final version received: June 1998     

Simulated Non-Parametric Estimation of Dynamic Models

This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel, which considerably simplifies bandwidth selection for the purpose of implementing the estimator. Furthermore, the SNE displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods introduced in this paper are fairly simple to implement, and possess finite sample properties that are well approximated by the asymptotic theory. We illustrate these features within typical estimation problems that arise in financial economics.     

IMPACT OF PRICING STRUCTURE SELECTIVITY ON URBAN WATER DEMAND

Interest in demand management policies has intensified as residential water demand has grown in the United States. Using data from the 1984 American Water Works Association (AWWA) survey, the study here provides an empirical analysis of the differences in price elasticities of demand across water pricing block structures and examines these structures' "conservation-orientedness." However, a potential sample selection bias exists. That is, in cities where people are more interested in conservation, utility managers may be more likely to select a rate structure that they believe is conservation-oriented–an increasing block structure, for example. Managers' selectivity bias may cause research results either to understate or to overstate a particular block structure's impact on water conservation. The analysis here corrects for this selectivity bias in estimating water demand and tests whether consumers respond to average prices or to marginal prices. Correcting for selectivity bias involves an explicit analysis of the factors that influence utility managers' selection of rate structures. Estimating water demand under increasing and decreasing block structures suggests that sample selection bias remains a problem worthy of further investigation.     

A reexamination of fractional integrating dynamics in foreign currency markets

This paper reexamines foreign currency markets for evidence of fractional integration, and extends the extant literature in several important dimensions. First, we utilize a new semiparametric wavelet-based estimator, which is far superior to the more prevalent GPH estimator on the basis of mean squared error. Second, we utilize a broader and longer sample, which better facilitates the detection of long memory dynamics. Our analysis yields interesting empirical results that contrast with other recent studies. In particular, we find new evidence that a large proportion (14 out of 19) of exchange rate series display evidence of long memory, with little variation over alternative sample periods and alternative frequencies.     

A reflection principle for a random walk with implications for volatility estimation using extreme values of asset prices

In this paper, we derive a reflection principle for a random walk with the symmetric double exponential distribution. This allows us to come up with the closed form solution for the joint probability of the running maximum and the terminal value of the random walk. Based on this new theoretical result, we propose an extreme value estimator for the variance of the random walk that is not just approximately unbiased but exactly so. In simulations, we find that this estimator continues to be unbiased even when intraday mean reversion is present, as captured by the Binomial Markov Random Walk model. On the empirical side, we find that this estimator works well in a variety of global stock indices, including the S&P 500 Index, in the sense of being unbiased relative to the “usual” estimator, i.e., the sample variance of the daily returns.     

Endogeneity bias and growth regressions

The problem of regressor endogeneity stemming from reverse casuality is one that has plagued economists working in the field of empirical economic growth for some time. This paper attempts to address the relevant magnitude of this issue in the context of growth regressions based on the Solow growth model. The paper develops a method of running Monte Carlo simulations that allows us to generate simulated data that match the moments of observed real-world data typically used in such regressions while simultaneously allowing us to impose arbitrarily high correlations between the steady-state determinants of the Solow model and the unobserved residual term of the data-generating process. After running simulations that represent a wide sample of the mathematically-possible correlations, we conclude that a between estimator or a random effects estimator will deliever a lower average absolute bias across all coefficients than alternative estimators in almost all of our simulations. Conversely, estimators that use within-country variation will generate lower biases when looking solely at rates of convergence. Furthermore, we conclude that these results are robust when restricting our sample of simulations to several subsets of the assumed parameters and to changing our assumptions about country fixed-effects terms.     

A COMPUTATIONALLY PRACTICAL SIMULATION ESTIMATION ALGORITHM FOR DYNAMIC PANEL DATA MODELS WITH UNOBSERVED ENDOGENOUS STATE VARIABLES*

This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband's income on female labor supply decisions.     

Trend instrumental variable regression with an application to the US New Keynesian Phillips Curve

When a linear model suffers from endogeneity, a conventional solution is to use external instrumental variables. Sometimes, however, there are either no suitable external IVs or they are of poor quality. This paper constructs an internal instrumental variable from the time trend in the endogenous regressor without using any external IVs. We show that under some mild conditions this new trend IV estimator has desirable asymptotic properties, and also provide a robust Durbin-Wu-Hausman specification test to demonstrate the necessity of the IV method. Monte Carlo simulations show that the estimator and the test have good finite sample performance. In the end, we apply the trend IV estimator to the US New Keynesian Phillips Curve and find that it works as well as the usual external IVs in the literature.     

On exact small sample properties of ordinary ridge estimators

In this paper, we examine the exact small sample properties of the ordinary ridge estimator and the bias-corrected ordinary ridge estimator.