Estimation of relative average treatment effects with misclassification

This paper identifies and estimates the relative average treatment effect in the presence of misclassification. We propose consistent estimators based on nonparametric methods. The simulation results reported illustrate the performance of the proposed estimators.     

A Bayesian approach to misclassified binary response: female employment and intimate partner violence in urban India

We examine the effect of female employment on the odds of physical spousal violence using a Bayesian misclassification model combined with propensity score regression estimation. While a classical propensity score model finds a significant violence-provoking effect of female employment, our model finds no evidence of a significant effect. This suggests that misleading inferences are caused by falsely small standard errors in a model that does not account for uncertainties around propensity scores. Further, we confirm our misclassification model as a preferred specification using Deviance Information Criterion (DIC).     

An MPEC estimator for misclassification models

In this paper, we propose a constrained maximum likelihood estimator for misclassification models, by formulating the estimation as an MPEC (Mathematical Programming with Equilibrium Constraints) problem. Our approach improves the numerical accuracy and avoids the singularity problem. Monte Carlo simulations confirm that the proposed estimator reduces bias and standard deviation of the estimator, especially when the sample is small/medium and/or the dimension of latent variable is large.     

Threshold effects in panel data stochastic frontier models of dairy production in Canada

One of the most enduring problems in econometrics is how to properly account for heterogeneity among firms. Threshold regression models are intuitively appealing methods to deal with this issue. We consider a fixed-effect panel data stochastic frontier model (Schmidt and Sickles, 1984; Martin-Marcos and Suarez-Galvez, 2000) and, relying on Hansen (1999, 2000a), we propose an estimator that accommodates multiple thresholds. Our model assumes absence of any unmeasured time invariant heterogeneity across firms as in Greene (2005, p. 277). Slope and threshold parameters can be estimated using a within estimator combined with a grid search over the threshold parameters. Testing for threshold effects is problematic because threshold parameters are not identified under the null hypothesis, a case of the so-called Davies' problem. We apply the bootstrap procedure proposed by Hansen (1999, 2000a) to test for the presence of thresholds. An asymptotic confidence set for the threshold parameter can be obtained by inverting an LR test, using the distribution result presented in Hansen (1999, 2000a). Our empirical application features a panel of Quebec dairy farms. We use farm size as the threshold variable. The presence of a trend in the specification matters for the determination of the number of thresholds. Technical efficiency scores and rankings of farms estimated from competing model specifications are highly correlated and do not vary significantly across groups of farm sizes defined by the threshold parameter values.     

Testing for normality in linear regression models using regression and scale equivariant estimators

In this paper we provide a general solution to the problem of controlling the probability of a type I error in normality tests for the disturbances in linear regressions when using robust-regression residuals. We show that many classes of well-known robust regression estimators belong to the class of regression and scale equivariant estimators. It is these equivariance properties that are used to reduce the nuisance parameter space under the null, from which we develop Monte Carlo and Maximized Monte Carlo tests for the null of disturbance normality. Finally, we illustrate in a simulation experiment the potential power gains from using robust-regression residuals in testing this null hypothesis.     

On the use of robust regression in econometrics

A particular robust regression estimator has gained popularity among applied econometricians. We show that this estimator is inconsistent for the parameters of the conditional mean when the errors are skewed and heteroskedastic, and conclude that therefore its use cannot be generally recommended.     

Bayesian endogeneity bias modeling

We propose to model endogeneity bias using prior distributions of moment conditions. The estimator can be obtained both as a method-of-moments estimator and in a Ridge penalized regression framework. We show the estimator’s relation to a Bayesian estimator.     

An alternative bivariate zero-inflated negative binomial regression model using a copula

We propose an alternative bivariate zero-inflated negative binomial (BZINB) regression model based on a copula. The empirical result shows that the proposed model performs better than the existing BZINB models in terms of the maximum log-likelihood and the AIC.     

THE LEAD–LAG RELATION BETWEEN THE S&P500 SPOT AND FUTURES MARKETS: AN INTRADAY-DATA ANALYSIS USING A THRESHOLD REGRESSION MODEL

In this paper we examine the lead–lag interaction between the futures and spot markets of the S&P500 using the threshold regression model on intraday data. The use of threshold variables to model the changes in the regression structure with respect to different market conditions enables us to investigate the lead–lag interaction in a data-based approach and avoid stratifying the data arbitrarily. Using the basis as the threshold variable, we find that the short-selling restrictions in the spot market reduce the effect of the spot index as the leading variable. To study the effect of market-wide information on the interaction between the spot and futures markets, we use the coefficient of determination in the regression of the S&P500 on the Morgan–Stanley Composite Index-US and the Major Market Index as the threshold variable. We find that the lead effect of the futures market over the spot market is stronger when there is more market-wide information. On the other hand, the lead effect of the cash market over the futures market is weaker when there is more market-wide information. In addition, we also use the lagged 45-min return of the spot market as the threshold variable. We find that the lead effect of the spot market is stronger in periods of directionless trading than in periods of good or bad markets.     

Dynamic coalitional equilibrium

We study coalition formation processes of Konishi and Ray (2003) [27]. It is shown that an absorbing and deterministic process of coalition formation that also forms an equilibrium - satisfies a coalitional one-deviation property - does exist if one allows the process to be history dependent. All such dynamic equilibrium processes of coalition formation are characterized. Absorbing outcomes of dynamic equilibrium processes are also identified. It is shown that they always constitute a subset of the largest consistent set of Chwe (1994) [11]. A procedure that identifies a dynamic equilibrium process of coalition formation in finite time is constructed.     

Neoclassical versus Frontier Production Models? Testing for the Skewness of Regression Residuals*

The empirical literature on production and cost functions is divided into two strands. The neoclassical approach concentrates on model parameters, while the frontier approach decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose a theoretical justification for the skewness of the inefficiency term, arguing that this skewness is the key testable hypothesis of the frontier approach. We propose to test the regression residuals for skewness in order to distinguish the two competing approaches. Our test builds directly upon the asymmetry of regression residuals and does not require any prior distributional assumptions.     

Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey

In this paper, we investigate possible nonlinearities in the inflation–output relationship in Turkey for the 1980–2008 period. We first estimate a linear bivariate model for the inflation rate and output gap, and test for linearity of the estimated model against nonlinear alternatives. Linearity test results suggest that the relationship between the inflation rate and output gap is highly nonlinear. We estimate a bivariate time-varying smooth transition regression model, and compute dynamic effects of one variable on the other by generalized impulse response functions. Computed impulse response functions indicate that inflation–output relationship in Turkey during the analyzed period was regime dependent and varied considerably across time.     

The art of conversation: eliciting information from experts through multi-stage communication

We examine the strategic interaction between an informed expert and an uninformed decision maker, extending the analysis of Crawford and Sobel (Econometrica 50 (1982) 1431). We modify their model to allow for more extensive communication between the two parties and show that face-to-face communication between the expert and the uninformed decision maker followed by a written report from the expert leads to improved information transmission. In (almost) all cases, there exists an equilibrium in our modified model that ex ante Pareto dominates all of the equilibria identified by Crawford and Sobel. This remains true even if the expert's bias is so great that in their model no information would be disclosed.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

Panel data inference under spatial dependence

This paper focuses on inference based on the standard panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial autoregressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the standard panel data estimators that ignore spatial dependence can lead to misleading inference.     

The forward premium anomaly: statistical artefact or economic puzzle? New evidence from robust tests

Abstract .  Recent literature has questioned statistical inference in predictive regression with persistent regressors, suggesting a possible explanation for puzzles such as the forward premium anomaly. We therefore revisit this puzzle using three alternative econometric methods known to provide reliable inference in the presence of persistent conditioning variables. While they provide less evidence against forward rate unbiasedness than traditional predictive regression tests, we still reject using at least one method for all six currencies. Thus, while the econometric problems inherent in predictive regression likely play a role in this anomaly, we are left with an economic puzzle even after accounting for their influence.     

Nonlinear interest rate reaction functions for the UK

We empirically analyze Taylor-type equations for short-term interest rates in the United Kingdom using quarterly data from 1970Q1 to 2006Q2. Starting from strong evidence against a simple linear Taylor rule, we model nonlinearities using logistic smooth transition regression (LSTR) models. The LSTR models with time-varying parameters consistently track actual interest rate movements better than a linear model with constant parameters. Our preferred LSTR model uses lagged interest rates as a transition variable and suggests that in times of recessions the Bank of England puts more weight on the output gap and less so on inflation. A reverse pattern is observed in non-recession periods. Parameters of the model change less frequently after 1992, when an inflation target range was announced. We conclude that for the analysis of historical monetary policy, the LSTR approach is a viable alternative to linear reaction functions.     

Pricing Personal Services: An Empirical Study of Earnings in the UK Prostitution Industry

The hedonic pricing method is used to investigate the way in which the prices of prostitutes' services are determined. The data used in the analysis are extracted from an internet site, each observation being based on a report submitted by a client. The factors affecting price are identified in a regression framework, and combined with other information to provide estimates of the earnings, both aggregate and individual, for a sub‐sector of this underground service industry in the United Kingdom. Comparison of these earnings' estimates with data on earnings from alternative employment then allows us to estimate the compensating wage differential, and also to verify the theoretical prediction that prostitutes' earnings are positively related to earnings from alternative employment.     

Set coverage and robust policy

We show that confidence regions covering the identified set may be preferable to confidence regions covering each of its points in robust control applications.     

On the effects of group identity in strategic environments

We examine differences in behavior between subjects interacting with a member of either the same or different identity group in both a centipede game and a series of stag hunt games. We find evidence that subjects interacting with outgroup members are more likely to behave as though best-responding to uniform randomization of the partner. We conclude that group identity not only affects player׳s social preferences, as identified in earlier research, but also affects the decision making process, independent of changes in the utility function.