Binary misclassification and identification in regression models

We study a regression model with a binary explanatory variable that is subject to misclassification errors. The regression coefficient is then only partially identified. We derive several results that relate different assumptions about the misclassification probabilities and the conditional variances to the size of the identified set.     

Instrumental variable estimation of a spatial autoregressive panel model with random effects

This paper extends the instrumental variable estimators of Kelejian and Prucha (1998) and Lee (2003) proposed for the cross-sectional spatial autoregressive model to the random effects spatial autoregressive panel data model. It also suggests an extension of the Baltagi (1981) error component 2SLS estimator to this spatial panel model.     

Measurement error in imputation procedures

We study how estimators that are used to impute consumption in survey data are inconsistent due to measurement error in consumption. Previous research suggests instrumenting consumption to overcome this problem. We show that, if additional regressors are present, then instrumenting consumption may still produce inconsistent estimators due to the likely correlation between additional regressors and measurement error. On the other hand, low correlations between additional regressors and instruments may reduce bias due to measurement error. We apply our findings by revisiting recent research that imputes consumption data from the CEX to the PSID.     

On the origin of high persistence in GARCH-models

We show that the (Baillie and Chung, 2001) minimum distance estimates of the GARCH (1,1) model induce spurious persistence in the volatility when there are structural changes in the mean of the process.     

A cautionary note on computing conditional from unconditional correlations

We show that some care is needed when inferring true unconditional correlations from observed conditional correlations, which is a frequent problem in empirical finance and elsewhere. We give a general formula for the relationship between the two and demonstrate its importance in the context of the bivariate t-distribution.     

A simple spatial dependence test robust to local and distributional misspecifications

In this paper, we derive a class of modified score tests robust to local and distributional misspecifications for testing spatial error autocorrelation and spatial lag dependence. The proposed tests are general enough to include several popular tests for the spatial dependence as special cases. Moreover, we show that the popular test statistics proposed by Burridge (1980) and Anselin et al. (1996) are robust to distributional misspecification although they are derived under normality assumption.     

Unsustainable timber harvesting, deforestation and the role of certification

Deforestation is a major environmental issue, while demand for timber products increases rapidly in the developing world. One can thus wonder whether forest harvesting is sustainable worldwide, or if demand for timber products is fulfilled with the products from deforestation. Our panel data analysis shows that countries where timber harvesting is more important tend to experience larger deforestation rates than others, giving the intuition that forest harvesting is generally not sustainable. We also show that timber certification is negatively related to deforestation and thus seems to be a good indicator of harvesting sustainability.     

Efficient realized variance, regression coefficient, and correlation coefficient under different sampling frequencies

Efficiency of the realized variance of an asset is improved by taking advantage of another asset whose return is cross-sectionally correlated with that of the asset and is less sensitive to market microstructure noises permitting higher frequency sampling than the original asset.     

Viewpoint: An extended class of instrumental variables for the estimation of causal effects

Abstract We examine how structural systems can yield observed variables instrumental in identifying and estimating causal effects. We provide an exhaustive characterization of potentially identifying conditional exogeneity relationships and demonstrate how structural relations determine exogeneity and exclusion restrictions that yield moment conditions supporting identification. This provides a comprehensive framework for constructing instruments and covariates. We introduce notions of conditioning and conditional extended instrumental variables (XIVs). These permit identification but need not be traditional instruments, as they may be endogenous. We distinguish between observed XIVs and proxies for unobserved XIVs. A main message is the importance of sufficiently specifying causal relations governing the unobservables.     

Imitation and the evolution of Walrasian behavior: Theoretically fragile but behaviorally robust

A well-known result by Vega-Redondo (1997) [18] implies that in symmetric Cournot oligopolies, imitation leads to the Walrasian outcome. We show that this result is not robust to the slightest asymmetry in costs, since every outcome where agents choose identical actions will be played some fraction of the time in the long run. We then conduct experiments to check this fragility. We obtain that, contrary to the theoretical prediction, the Walrasian outcome is a good predictor of market outcomes. Finally, we suggest a new theory based on a mix of imitation and other learning processes that explains subjects' behavior fairly well.     

Estimating Housing Price: Kriging the Mean

The price of houses and its evolution in recent years is one of the issues that citizens and, of course, political and economic authorities are worried about. In most of the developed countries the mean price (per square meter) of houses in an area is estimated by a simple average of single prices from a sample of houses that does not take into account the spatial correlation among the prices. As an alternative to this classic procedure, in this paper we propose a linear estimator of the mean price of houses, using the kriging estimator, which has been specially designed for the case of spatially correlated data in a given domain. This estimator is the best unbiased linear one and provides a more realistic estimate of the mean price of houses in the urban area we are interested in. Obviously, the modelling of the variogram function is a central point in the global estimation process.     

On the prevalence of trends in primary commodity prices

This paper applies new time series procedures to examine the Prebisch–Singer hypothesis of a secular deterioration in relative primary commodity prices. Specifically, we allow for (up to) two structural breaks in 24 price series, covering the 1900–98 period. For the majority of commodities, it is shown that the trend is not well represented by a single downward slope, but instead by a shifting trend that often changes sign over the sample period. Unlike some recent work that has also allowed for structural breaks, these results provide much less support for the Prebisch–Singer hypothesis.     

Measuring the incentives to learn in Colombia using new quantile regression approaches

This paper employs newly developed quantile regression techniques to investigate a policy that could differentially affect students' performance. The Colombian vouchers were assigned using lotteries, and were renewable as long as the students maintained satisfactory academic progress. This second aspect of the program may provide incentives for low attainment students to work harder. The evidence supports the hypothesis that incentives could account for the impact of the vouchers, including lower repetition rate. The effect of the vouchers is largest in the lower tail of the educational attainment distribution, a possibility that was conjectured by others, but has not yet been confirmed empirically. The evidence suggests that the incentive effect of the program increases weak students' test scores by at least 0.1 standard deviations, roughly the score gain associated to a half year of school learning.     

Dynamic effects of trade openness on financial development

This paper employs the Pooled Mean Group (PMG) approach of Pesaran et al. (1999) to study the dynamic effects of trade openness on financial development. The advantage of the PMG estimator over other dynamic panel econometric techniques is that it allows short-run coefficients, speeds of adjustment and error variances to vary across countries, with cross-country homogeneity restrictions only on long-run parameters. Our results spanning 88 countries over 1960–2005 show that a positive long-run relationship between trade openness and financial development coexists with a negative short-run relationship. But when splitting the data into different income or inflation groups, this finding is observed only in relatively low-income countries or high-inflation economies.     

Resource abundance and economic growth in the United States

It is a common assumption that regions within the same country converge to approximately the same steady-state income levels. The so-called absolute convergence hypothesis focuses on initial income levels to account for the variability in income growth among regions. Empirical data seem to support the absolute convergence hypothesis for US states, but the data also show that natural resource abundance is a significant negative determinant of growth. We find that natural resource abundance decreases investment, schooling, openness, and R&D expenditure and increases corruption, and we show that these effects can fully explain the negative effect of natural resource abundance on growth.     

On conditions in central limit theorems for martingale difference arrays

An alternative central limit theorem for martingale difference arrays is presented. It can be deduced from the literature but it is not stated as such. It can be very useful for statisticians and econometricians. An illustration is given in the context of ARMA models with time-dependent coefficients. This note ends with a discussion about the conditions.     

The Box-Cox transformation-of-variables in regression

The application of the Box-Cox transformation to the dependent and independent variables is discussed. Maximum likelihood and iterative GLS estimators are used and bootstrapping is carried out to compare the bootstrap sample variability with the finite sample variability (RMSE) and improve RMSE estimation. The biases of parameter estimators were shown to be substantial in small samples. The standard errors obtained from the Hessian matrix were a poor measure of the finite sample variability. Thet-ratios of the linear parameter estimators may not be normally distributed in small samples.The authors acknowledge the helpful comments of two referees.     

Monetary environments and stock returns revisited: A quantile regression approach

We investigate the impact of monetary conditions on stock market returns at different points on the return distributions. Our results reveal no association between stock returns and monetary environments at the lower quantiles. At the upper quantiles, however, we find that expansive monetary conditions lead to significantly larger stock returns. The relationship between returns and monetary conditions at the upper quantiles is also found to be asymmetric, exhibiting a monotonic increase in responsiveness at successive quantiles.     

On the equivalence of indirect inference and bootstrap bias correction for linear IV estimators

This note shows that two ways of simulation based bias correction–indirect inference and bootstrap bias correction–are equivalent for two-stage-least-squares, as well as k$k$-class estimators for the standard linear model with endogenous regressors.     

International portfolio reallocation: Diversification benefits and European monetary union

We investigate the determinants of bilateral international equity and bond portfolio reallocation across a large cross-section of countries spanning over two sample periods: 1997-2001 and 1997-2005. We find that the strongest drivers are the marginal diversification benefits arising from the pure asset component and the initial degree of underweight. This evidence suggests that global portfolio reallocations over the asset boom and bust period were determined by optimal diversification considerations. We also find that due to economic and monetary union (EMU) the weight assigned by euro area investors to investment in euro area countries increased significantly in equity and fixed income portfolios, with a trade diverting effect against the British bond market.