The effect of a treatment on the hazard rate of a duration outcome may depend on the elapsed time since treatment. In addition, treatment effects may be heterogeneous across agents. The former gives rise to duration dependence of the treatment effect, whereas unobserved heterogeneity gives rise to spurious duration dependence of the observable hazard rate. We develop a model allowing for duration dependence and unobserved heterogeneity in the treatment effect. The model incorporates a Timing of Events model and allows for selectivity on unobservables. We prove identification, exploiting variation in the timing of treatment and outcome. In the application we analyze the effects of the Swedish vocational employment training program on the individual transition rate from unemployment to work. We demonstrate the appropriateness of the approach by studying the enrollment process. The data cover the population and include multiple unemployment spells for many individuals. The results indicate a large, significantly positive effect on exit to work shortly after exiting the program. The effect at the individual level diminishes after some weeks. When taking account of the time spent in the program, the effect on the mean unemployment duration is small. Copyright © 2012 John Wiley & Sons, Ltd.

This paper investigates the extent to which the high macroeconomic volatility experienced in the classical Gold Standard era of US history can be attributed to the monetary policy regime per se as distinct from other shocks. For this purpose, we estimate a small dynamic stochastic general equilibrium model for the classical Gold Standard era. We use this model to conduct a counterfactual experiment to assess whether a monetary policy conducted on the basis of a Taylor rule characterizing the Great Moderation data would have led to different outcomes for macroeconomic volatility and welfare in the Gold Standard era. The counterfactual Taylor rule significantly reduces inflation volatility, but at the cost of higher real-money and interest-rate volatility. Output volatility is very similar. The end result is no welfare improvement. Copyright © 2012 John Wiley & Sons, Ltd.

Evidence of lengthy half-lives for real exchange rates in the presence of a high degree of exchange rate volatility has been considered as one of the most puzzling empirical regularities in international macroeconomics. This paper suggests that the measure of half-life used in the literature might be problematic and proposes alternative measures with desirable properties. Their focus on the cumulative effects of the shocks distinguishes them from the measures used in the literature. An empirical analysis of bilateral US dollar real exchange rates employing the alternative half-life measure produces results consistent with theory and indicates that the PPP puzzle is less pronounced than initially thought. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents a model that provides conditions under which a causal interpretation can be given to the association between childhood parental employment and subsequent educational attainments of children. The key parameter comes from theconditional demand function for children's future earning capacity. Its identification rests on having data on siblings and assumptions about the timing of parents' knowledge of their children's endowments. In addition to sibling differences, the useof a fixed-effects instrumental-variables estimator identifies the parameter under weaker conditions. Empirical analysis informed by the model reveals a negative and significant effect on the child's educational attainment of the months of the mother's full-time employment when the child was aged 0–5. The effect of the mother's part-time employment is smaller and less well determined, but again negative. These results suggest that the substitution effect of the mother's employment dominates the income effects. Stronger adverse effects are found for children of less-educated mothers. Copyright © 2012 John Wiley & Sons, Ltd.

We propose a class of observation-driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of nonlinear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time-varying mean. In addition, our approach can lead to new formulations of observation-driven models. We illustrate our framework by introducing new model specifications for time-varying copula functions and for multivariate point processes with time-varying parameters. We study the models in detail and provide simulation and empirical evidence. Copyright © 2012 John Wiley & Sons, Ltd.

We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first-difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women. Copyright © 2011 John Wiley & Sons, Ltd.

This paper investigates the determinants of credit spread changes in euro-denominated bonds. We adopt a factor model framework, inspired by the credit risk structural approach, as credit spread changes can be easily viewed as an excess return on corporate bonds over Treasury bonds. We try to assess the relative importance of market and idiosyncratic factors as an explanation of movements in credit spreads. We adopt a heterogeneous panel with a multifactor error model and propose a two-step estimation procedure, which yields consistent estimates of unobserved factors. The analysis is carried out with a panel of monthly redemption yields on a set of corporate bonds for a time span of 3 years. Our results suggest that the euro corporate market is driven by observable and unobservable factors. The unobservable factors are identified through a consistent estimation of individual and common observable effects. The empirical results suggest that an unobserved common factor has a significant role in explaining the systematic changes in credit spreads. However, in contrast to evidence regarding US credit spread changes, it cannot be identified as a market factor. Copyright © 2011 John Wiley & Sons, Ltd.

Long-horizon predictive regressions in finance pose formidable econometric problems when estimated using available sample sizes. Hodrick in 1992 proposed a remedy that is based on running a reverse regression of short-horizon returns on the long-run mean of the predictor. Unfortunately, this only allows the null of no predictability to be tested, and assumes stationary regressors. In this paper, we revisit long-horizon forecasting from reverse regressions, and argue that reverse regression methods avoid serious size distortions in long-horizon predictive regressions, even when there is some predictability and/or near unit roots. Meanwhile, the reverse regression methodology has the practical advantage of being easily applicable when there are many predictors. We apply these methods to forecasting excess bond returns using the term structure of forward rates, and find that there is indeed some return forecastability. However, confidence intervals for the coefficients of the predictive regressions are about twice as wide as those obtained with the conventional approach to inference. We also include an application to forecasting excess stock returns. Copyright © 2011 John Wiley & Sons, Ltd.

This paper traces the yearly response of gross domestic product growth—both aggregated and disaggregated into its agricultural and non-agricultural components—to four types of natural disasters: droughts, floods, earthquakes, and storms. The paper uses a methodological approach based on pooling the experiences of various countries over time. It consists of vector autoregressions in the presence of endogenous variables and exogenous shocks (VARX), applied to a panel of cross-country and time series data. The analysis finds heterogeneous effects on a variety of dimensions. First, the effects of natural disasters are stronger on developing than on advanced countries. Second, not all natural disasters are alike in terms of the growth response they induce, and some can even have positive effects on economic growth. Third, severe disasters often carry much worse effects than moderate effects do. Fourth, the timing of the growth response varies with both the type of natural disaster and the sector of economic activity. Copyright © 2011 John Wiley & Sons, Ltd.

This paper models for the first time a spatial process in local tax policies in the presence of centrally imposed fiscal limitations. Focusing on the frequently encountered case of a tax rate cap, we evaluate three empirical approaches to the analysis of spatially dependent limited tax policies: (i) a Bayesian spatial approach for censored dependent variables; (ii) a Tobit corner solution model augmented with a spatial lag; (iii) a spatial discrete hazard model. The evidence arising from an investigation of severely state-constrained local vehicle taxes in Italy suggests that ignoring tax limitations can lead to substantial underestimation of inter-jurisdictional fiscal interaction. Copyright © 2011 John Wiley & Sons, Ltd.

In the spatial model of voting, voters choose the candidate closest to them in the ideological space. Recent work by Degan and Merlo in 2009 shows that it is falsifiable on the basis of individual voting data in multiple elections. We show how to tackle the fact that the model only partially identifies the distribution of voting profiles and we give a formal revealed preference test of the spatial voting model in three national elections in the USA, and strongly reject the spatial model in all cases. We also construct confidence regions for partially identified voter characteristics in an augmented model with unobserved valence dimension, and identify the amount of voter heterogeneity necessary to reconcile the data with spatial preferences. Copyright © 2011 John Wiley & Sons, Ltd.

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Data-based restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators. Copyright © 2011 John Wiley & Sons, Ltd.

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases factor methods have been traditionally used, but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic dataset containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Typically, we find that the simple Minnesota prior forecasts well in medium and large VARs, which makes this prior attractive relative to computationally more demanding alternatives. Our empirical results show the importance of using forecast metrics based on the entire predictive density, instead of relying solely on those based on point forecasts. Copyright © 2011 John Wiley & Sons, Ltd.

Detection of structural change is a critical empirical activity, but continuous ‘monitoring’ for changes in real time raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices. Copyright © 2011 John Wiley & Sons, Ltd.

This paper measures the effect of Napster on record sales. I treat the introduction of Napster as a technological event that only Internet users experienced, and use a difference-in-differences (DD) approach. Because of potential compositional changes in Internet users, I examine identifying assumptions for the DD estimator under compositional changes and develop a test for identifying restrictions. To address potential bias due to compositional changes, I extend DD matching estimators to the case of two-variate propensity scores. I find evidence suggesting that file sharing is likely to explain 20% of total sales decline, which is driven by households with children aged 6–17. Copyright © 2011 John Wiley & Sons, Ltd.

To increase the exit from welfare, benefit recipients in the municipality of Rotterdam were exposed to various financial incentives. Once their benefit spell exceeded one year, welfare recipients were entitled to a re-employment bonus if they found a job that lasted at least 6 months. However, they could also be punished for noncompliance with eligibility requirements and face a sanction, i.e. a temporary reduction of their benefits. We find that the financial sticks in the form of benefit sanctions were effective in stimulating the exit from welfare, while the financial carrots in the form of re-employment bonuses were not. Copyright © 2011 John Wiley & Sons, Ltd.

This paper takes a novel approach to detect the latent currency portfolio of Chinese foreign exchange reserves and the underlying portfolio management strategies during 2000 and 2007. Based on a portfolio accounting identity and the budget constraint of the Chinese central bank's holding of foreign assets, the monthly growth rate of reserves can be decomposed into monthly rate of return, valuation effects of exchange rates, and monthly net purchase rate. The valuation effect reveals the value share of each currency. Bayesian inference is adopted to estimate the state-space model with a mixture of Gaussian distributions. The results show that China significantly and dramatically diversified its reserves out of the US dollar in 2002: both the euro's value and quantity shares increased from 5% to more than 20%. By the end of 2007, China held about (at most) 67.3% of its reserves in the US dollar, 22% in the euro, 2.5% in the Japanese yen, 4.7% in the Australian dollar, and 3.5% in the British pound. The average annual rate of return was about 3%. Copyright © 2011 John Wiley & Sons, Ltd.

We estimate the effect of cash transfers given to youth conditional on high school attendance on the labor supply decisions and academic performance of youth. We exploit differences in the size of the total transfer received based on timing of birth to identify the causal effects of interest. Specifically, individuals born late in a quarter receive a larger total transfer than comparable individuals born early in the following quarter. We find that the transfer increases the labor market participation of youth and the number of months worked. The estimated effect is larger for individuals from low-income families. The results suggest that some youths are borrowing constrained. Since we find no evidence of corresponding effects on academic performance, alleviating the constraint appears only to affect consumption decisions and not human capital investment. Copyright © 2011 John Wiley & Sons, Ltd.

Consistent with two models of imperfect competition in the labor market—the efficient bargaining model and the monopsony model—we provide two extensions of a microeconomic version of Hall's framework for estimating price-cost margins. We show that both product and labor market imperfections generate a wedge between factor elasticities in the production function and their corresponding shares in revenue, which can be characterized by a ‘joint market imperfections parameter’. Using an unbalanced panel of 10,646 French firms in 38 manufacturing industries over the period 1978–2001, we can classify these industries into six different regimes depending on the type of competition in the product and the labor market. By far the most predominant regime is one of imperfect competition in the product market and efficient bargaining in the labor market (IC-EB), followed by a regime of imperfect competition in the product market and perfect competition or right-to-manage bargaining in the labor market (IC-PR), and by a regime of perfect competition in the product market and monopsony in the labor market (PC-MO). For each of these three predominant regimes, we assess within-regime firm differences in the estimated average price-cost mark-up and rent sharing or labor supply elasticity parameters, following the Swamy methodology to determine the degree of true firm dispersion. To assess the plausibility of our findings in the case of the dominant regime (IC-EB), we also relate our industry and firm-level estimates of price-cost mark-up and extent of rent sharing to industry characteristics and firm-specific variables respectively. Copyright © 2011 John Wiley & Sons, Ltd.

Semiparametric varying-coefficient models have become a common fixture in applied data analysis. Existing approaches, however, presume that those variables affecting the coefficients are continuous in nature (or that there exists at least one such continuous variable) which is often not the case. Furthermore, when all variables affecting the coefficients are categorical/discrete, theoretical underpinnings cannot be obtained as a special case of existing approaches and, as such, requires a separate treatment. In this paper we use kernel-based methods that place minimal structure on the underlying mechanism governing parameter variation across categorical variables while providing a consistent and efficient approach that may be of interest to practitioners. One area where such models could be particularly useful is in settings where interactions among the categorical and real-valued predictors consume many (or even exhaust) degrees of freedom for fully parametric models (which is frequently the case in applied settings). Furthermore, we demonstrate that our approach behaves optimally when in fact there is no variation in a model's coefficients across one or more of the categorical variables (i.e. the approach pools over such variables with a high probability). An illustrative application demonstrates potential benefits for applied researchers. Copyright © 2011 John Wiley & Sons, Ltd.

This paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. Copyright © 2011 John Wiley & Sons, Ltd.

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real-time out-of-sample exercise using a time-varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time-varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.

We consider testing distributional assumptions by using moment conditions. A general class of moment conditions satisfied under the null hypothesis is derived and connected to existing moment-based tests. The approach is simple and easy to implement, yet reasonably powerful. In addition, we provide moment tests that are robust against parameter estimation error uncertainty in the general case which includes the case of serial correlation. In particular, we consider the location-scale model for which we derive robust moment tests, regardless of the forms of the conditional mean and variance. We study in detail the Student and inverse Gaussian distributions. Simulation experiments are conducted to assess the finite sample properties of the tests. We provide two empirical examples on foreign exchange rates by testing the Student distributional assumption of T-GARCH daily returns and on daily realized variance by testing the inverse Gaussian distributional assumption. Copyright © 2011 John Wiley & Sons, Ltd.

This paper shows that foreign aid has a significant positive average effect on real per capita gross domestic product (GDP) growth if, and only if, the quantitatively large negative reverse causal effect of per capita GDP growth on foreign aid is adjusted for in the growth regression. Instrumental variables estimates show that a 1 percentage point increase in GDP per capita growth decreased foreign aid by over 4%. Adjusting for this quantitatively large, negative reverse causal effect of economic growth on foreign aid shows that a 1% increase in foreign aid increased real per capita GDP growth by around 0.1 percentage points. Copyright © 2011 John Wiley & Sons, Ltd.

The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in nonlinear dynamic random-effects panel data models. The characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance and robustness are not fully known as of yet. In this paper we investigate the performance and robustness of this method in comparison with an ideal case in which the initial values are known constants; the worst scenario is based on an exogenous initial values assumption, and the Heckman's reduced-form approximation method, which is widely used in the literature. The dynamic random-effects probit and Tobit (type I) models are used as working examples. Various designs of the Monte Carlo experiments and two further empirical illustrations are provided. The results suggest that the Wooldridge method works very well only for the panels of moderately long duration (longer than 5–8 periods). Heckman's reduced-form approximation is suggested for short panels (shorter than 5 periods). It is also found that all the methods tend to perform equally well for panels of long duration (longer than 15–20 periods). Copyright © 2011 John Wiley & Sons, Ltd.

We investigate peer group effects in laboratory experiments based on Milgrom and Roberts' (1982, Econometrica50: 443–459) entry limit pricing game. We generalize Heckman's (1981, in Structural Analysis of Discrete Data with Econometric Applications. MIT Press: Cambridge, MA) dynamic discrete-choice panel data models by introducing time-lagged social interactions, using the unbiased GHK simulator to implement the computationally cumbersome maximum likelihood estimation. We find that subjects' decisions are significantly influenced by past decisions of peers on several dimensions, including potential entrants' choices and strategic play of like-type monopolists. The proposed model and estimation method may be applicable to other experiments where peer group effects are likely to play an important role. Copyright © 2011 John Wiley & Sons, Ltd.

In the linear instrumental variables model, we provide theoretical and Monte Carlo evidence for the size distortion of a two-stage hypothesis test that uses a test of overidentifying restrictions (OR) in the first stage. We derive a lower bound for the asymptotic size of the two-stage test. The lower bound is given by the asymptotic size of a test that rejects the null hypothesis when two conditions are met: the test of OR used in the first stage does not reject and the test in the second stage rejects. This lower bound can be as large as 1 − ε_P, where ε_P is the pretest nominal size, for a parameter space that allows for local non-exogeneity of the instruments but rules out weak instruments. Copyright © 2011 John Wiley & Sons, Ltd.

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative dataset. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model-based clustering approach. The statistical challenge in our application comes from the difficulty in extending distance-based clustering approaches to the problem of identifying groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, which is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model. Copyright © 2011 John Wiley & Sons, Ltd.

Barro and McCleary (2003, Religion and economic growth across countries. American Journal of Sociology68: 760–781) is a key research contribution in the new literature exploring the macroeconomic effects of religious beliefs. This paper represents an effort to evaluate the strength of their claims. We evaluate their results in terms of replicability and robustness. Overall, their analysis generally meets the standard of statistical replicability, though not perfectly. On the other hand, we do not find that their results are robust to changes in their baseline statistical specification. When model-averaging methods are employed to integrate information across alternative statistical specifications, little evidence survives that religious variables help to predict cross-country income differences. Copyright © 2011 John Wiley & Sons, Ltd.

This paper addresses the question of the selection of multivariate generalized autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix forecasting accuracy, with a particular focus on relatively large-scale problems. We consider 10 assets from the New York Stock Exchange and compare 125 models based 1-, 5- and 20-day-ahead conditional variance forecasts over a period of 10 years using the model confidence set (MCS) and the superior predictive ability (SPA) tests. Model performance is evaluated using four statistical loss functions which account for different types and degrees of asymmetry with respect to over-/under-predictions. When considering the full sample, MCS results are strongly driven by short periods of high market instability during which multivariate GARCH models appear to be inaccurate. Over relatively unstable periods, i.e. the dot-com bubble, the set of superior models is composed of sophisticated specifications such as orthogonal and dynamic conditional correlation (DCC), both with leverage effect in the conditional variances. However, unlike the DCC models, our results show that the orthogonal specifications tend to underestimate the conditional variance. Over calm periods, a simple assumption like constant conditional correlation and symmetry in the conditional variances cannot be rejected. Finally, during the 2007–2008 financial crisis, accounting for non-stationarity in the conditional variance process generates superior forecasts. The SPA test suggests that, independently from the period, the best models do not provide significantly better forecasts than the DCC model of Engle (2002, Journal of Business and Economic Statistics 20: 339–350) with leverage in the conditional variances of the returns. Copyright © 2011 John Wiley & Sons, Ltd.

We build and estimate an equilibrium model of the term structure of interest rates based on a recursive utility specification. We contrast it with an arbitrage-free model, where prices of risk are estimated freely without preference constraints. In both models, nominal bond yields are affine functions of macroeconomic state variables. The equilibrium model accounts for the tent-shaped pattern and magnitude of coefficients from predictive regressions of excess bond returns on forward rates and the hump-shaped pattern in the term structure of volatilities, while the reduced-form no-arbitrage model does not account for these important features of the yield curve. Copyright © 2011 John Wiley & Sons, Ltd.

We propose an alternative measure of the degree to which income status is transmitted from one generation to another. Our indicator of intergenerational income mobility is based on a random coefficient model, which allows for variation in intergenerational mobility across families due to multiplicative unobserved family-specific characteristics. This alternative measure suggests that intergenerational income persistence is typically stronger than when intergenerational income mobility is measured using the standard elasticity of sons' income with respect to fathers' income. Copyright © 2011 John Wiley & Sons, Ltd.

When evaluating the effectiveness of a policy, most studies assume that the policy effect begins with the date of enactment. However, there often exists an endogenous policy lag, due to information acquisition and the cost of adjustment. Meanwhile, the policy impact may be a gradual implementation from one level to another, instead of a one-time shift behavior. To account for these issues when evaluating the Californian under-age drunk driving laws, this paper adopts two econometric techniques: the multiple structural change methodology and the smooth transition method. The methods detect two effective policy changes and also reveal the existence of policy lags. In addition, ignoring these lags leads to severely biased estimates of policy effects. A long transition period is identified for the first under-age drunk driving policy, while an abrupt transition is found for the other. In summary, the paper shows that the two econometric techniques complement each other and will be useful for policy evaluation. Copyright © 2011 John Wiley & Sons, Ltd.

This paper develops methods for stochastic search variable selection (currently popular with regression and vector autoregressive models) for vector error correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model. Copyright © 2011 John Wiley & Sons, Ltd.

This paper deals with different concepts of income elasticities of demand for a heterogeneous population and the relationship between individual and aggregate elasticities. In general, the aggregate elasticity is not equal to the mean of individual elasticities. The difference depends on the heterogeneity of the population and is quantified by a covariance term. Sign and magnitude of this term are determined by an empirical analysis based on the UK Family Expenditure Survey. It is shown that the relevant quantities can be identified from cross-sectional data and, without imposing restrictive structural assumptions, can be estimated by nonparametric techniques. It turns out that the aggregate elasticity significantly overestimates the mean of individual elasticities for many commodity groups. Copyright © 2011 John Wiley & Sons, Ltd.

We introduce a new framework, Realized GARCH, for the joint modeling of returns and realized measures of volatility. A key feature is a measurement equation that relates the realized measure to the conditional variance of returns. The measurement equation facilitates a simple modeling of the dependence between returns and future volatility. Realized GARCH models with a linear or log-linear specification have many attractive features. They are parsimonious, simple to estimate, and imply an ARMA structure for the conditional variance and the realized measure. An empirical application with Dow Jones Industrial Average stocks and an exchange traded index fund shows that a simple Realized GARCH structure leads to substantial improvements in the empirical fit over standard GARCH models that only use daily returns. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a simple-to-implement panel data method to evaluate the impacts of social policy. The basic idea is to exploit the dependence among cross-sectional units to construct the counterfactuals. The cross-sectional correlations are attributed to the presence of some (unobserved) common factors. However, instead of trying to estimate the unobserved factors, we propose to use observed data. We use a panel of 24 countries to evaluate the impact of political and economic integration of Hong Kong with mainland China. We find that the political integration hardly had any impact on the growth of the Hong Kong economy. However, the economic integration has raised Hong Kong's annual real GDP by about 4%. Copyright © 2011 John Wiley & Sons, Ltd.

This paper estimates food Engel curves using data from the first wave of the Survey on Health, Aging and Retirement in Europe (SHARE). Our statistical model simultaneously takes into account selectivity due to unit and item nonresponse, endogeneity problems, and issues related to flexible specification of the relationship of interest. We estimate both parametric and semiparametric specifications of the model. The parametric specification assumes that the unobservables in the model follow a multivariate Gaussian distribution, while the semiparametric specification avoids distributional assumptions about the unobservables. Copyright © 2011 John Wiley & Sons, Ltd.

We derive a quantity-based structural gravity equation system in which both trade flows and error terms are cross-sectionally correlated. This system can be estimated using techniques borrowed from the spatial econometrics literature. To illustrate our methodology, we apply it to a well-known Canada–US trade dataset. We find that border effects between the USA and Canada are smaller than suggested by previous studies: about 7.5 for Canadian provinces and about 1.3 for US states. Hence controlling directly for cross-sectional interdependence among both trade flows and error terms reduces measured border effects by capturing ‘multilateral resistance’. Copyright © 2010 John Wiley & Sons, Ltd.

To study preferences, respondents to a survey are usually asked to select their most preferred option from a set. Preferences can be estimated more efficiently if respondents are asked to rank all alternatives. When some respondents are unable to perform the ranking task, using the complete ranking may lead to a substantial bias. We introduce a model which endogenously describes the ranking capabilities of individuals. Estimated preferences based on this model are more efficient when at least some individuals are able to rank more than one item, and they do not suffer from biases due to ranking inabilities of respondents. Copyright © 2010 John Wiley & Sons, Ltd.

A new procedure is proposed for modelling nonlinearity of a smooth transition form, by allowing the transition variable to be a weighted function of lagged observations. This function depends on two unknown parameters and requires specification of the maximum lag only. Nonlinearity testing for this specification uses a search over a plausible set of weight function parameters, combined with bootstrap inference. Finite-sample results show that the recommended wild bootstrap heteroskedasticity-robust testing procedure performs well, for both homoskedastic and heteroskedastic data-generating processes. Forecast comparisons relative to linear models and other nonlinear specifications of the smooth transition form confirm that the new WSTR model delivers good performance. Copyright © 2010 John Wiley & Sons, Ltd.

We test for the presence of time-varying parameters (TVP) in the long-run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean-reverting models. We also propose residual-based diagnostic tests and examine out-of-sample forecasts. In-sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is too restrictive to conclusively fit the data. Out-of-sample analysis suggests a random-walk specification for oil price, and TVP models for both real-time forecasting in the case of gas and long-run forecasting in the case of coal. Copyright © 2010 John Wiley & Sons, Ltd.

This paper analyzes the predictive content of the term structure components level, slope, and curvature within a dynamic factor model of macroeconomic and interest rate data. Surprise changes of the three components are identified using sign restrictions, and their macroeconomic underpinnings are studied via impulse response analysis. The curvature factor is found to carry predictive information both about the future evolution of the yield curve and the macroeconomy. In particular, unexpected increases of the curvature factor precede a flattening of the yield curve and announce a significant decline of output more than 1 year ahead. Copyright © 2010 John Wiley & Sons, Ltd.

We introduce a blocking and regularization approach to estimate high-dimensional covariances using high-frequency data. Assets are first grouped according to liquidity. Using the multivariate realized kernel estimator of Barndorff-Nielsen et al. (2010), the covariance matrix is estimated block-wise and then regularized. The performance of the resulting blocking and regularization (‘RnB’) estimator is analyzed in an extensive simulation study mimicking the liquidity and market microstructure features of the S&P 1500 universe. The RnB estimator yields efficiency gains for varying liquidity settings, noise-to-signal ratios and dimensions. An empirical application of estimating daily covariances of the S&P 500 index confirms the simulation results. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a simple benchmark for monetary policy. Assuming the true model of the economy is unknown, it is based on an unrestricted vector autoregression (VAR). The key result is that instead of deriving optimal policy using the original VAR equations as the constraint, when no restriction is placed on the correlation structure of the VAR disturbances, the constraint should be formed from a transformation of the VAR. This method is applied to the USA, 1964–2009. Significant welfare gains are found compared with actual policy and using a Taylor rule. Incorporating a zero interest rate lower bound lowers output and inflation. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we propose a Bayesian estimation and forecasting procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, yielding predictive densities as a by-product. We show that the posterior model probabilities provide a convenient model selection criterion in discriminating between alternative causal and noncausal specifications. As an empirical application, we consider US inflation. The posterior probability of noncausality is found to be high—over 98%. Furthermore, the purely noncausal specifications yield more accurate inflation forecasts than alternative causal and noncausal AR models. Copyright © 2010 John Wiley & Sons, Ltd.

This paper develops a Bayesian method for quantile regression for dichotomous response data. The frequentist approach to this type of regression has proven problematic in both optimizing the objective function and making inferences on the parameters. By accepting additional distributional assumptions on the error terms, the Bayesian method proposed sets the problem in a parametric framework in which these problems are avoided. To test the applicability of the method, we ran two Monte Carlo experiments and applied it to Horowitz's (1993) often studied work-trip mode choice dataset. Compared to previous estimates for the latter dataset, the method proposed leads to a different economic interpretation. Copyright © 2010 John Wiley & Sons, Ltd.

We construct a copula from the skew t distribution of Sahu et al. (2003). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete-valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose a Bayesian approach that overcomes all these problems. The computations are undertaken using a Markov chain Monte Carlo simulation method which exploits the conditionally Gaussian representation of the skew t distribution. We employ the approach in two contemporary econometric studies. The first is the modelling of regional spot prices in the Australian electricity market. Here, we observe complex non-Gaussian margins and nonlinear inter-regional dependence. Accurate characterization of this dependence is important for the study of market integration and risk management purposes. The second is the modelling of ordinal exposure measures for 15 major websites. Dependence between websites is important when measuring the impact of multi-site advertising campaigns. In both cases the skew t copula substantially outperforms symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modelling tool when coupled with Bayesian inference. Copyright © 2010 John Wiley & Sons, Ltd.

This paper examines a flexible way to model empirically discrete data outcomes using ‘hazard rate’ decompositions. It presents a general data-generating mechanism based on potential outcomes to describe why the approach should work for almost any discrete distribution. Monte Carlo evidence indicates that these models estimate well the impacts of covariates on expected counts when the data follow a Poisson distribution. With data from more complex processes, these estimators continue to perform well. Since most economic count outcomes arise from occurrence-dependent behavioral processes, using flexibly estimated distributions should reduce the dependence of results on convenient but invalid assumptions. Copyright © 2010 John Wiley & Sons, Ltd.

The purpose of this paper is to build a dynamic structural model of educational choices in which cognitive skills shape decisions. The model is estimated by maximum likelihood using cohort data where individuals are observed from birth to the middle of their working life. These data are unique in that they include cognitive skills test scores collected as early as age 7. We then investigate how alternative policies foster educational enrolment. In particular, we simulate the effect of two subsidies different in the timing of disbursement. The first consists of grants assigned directly to individuals aged between 16 and 18. The second is assigned to the parents earlier on, when the cohort is still in its childhood. The latter subsidy affects cognitive skills accumulation and in turn educational choices. Our results suggest that a grant fosters enrolment at the lowest cost but the parental income subsidy generates more welfare as measured by a class of social welfare functions. Nevertheless, these differences in costs and welfare are small. Overall, the results reinforce the view that government investments in cognitive skill accumulation during childhood are worthwhile. However, the results also indicate that such investments should be well structured to ensure a high return. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a simple extension of nonparametric estimation methods for nonlinear budget-set models derived in Blomquist and Newey (2002) to censored dependent variables. The nonparametric method is applied to estimate female labor supply elasticities using data on married women from the 1985 and 1989 waves of the Panel Study of Income Dynamics, exploiting the substantial variation in budget sets caused by the Tax Reform Act of 1986 as a source of identification. The estimated wage elasticities from this new method are 0.56 overall and 0.27 on the intensive margin. The income elasticity estimates are close to − 0.67 overall and − 0.13 on the intensive margin. Compared with the linear labor supply model, the estimated elasticities are usually larger for the nonparametric specifications that account for nonlinear budget sets. Copyright © 2010 John Wiley & Sons, Ltd.

We apply a global vector autoregressive (GVAR) model to the analysis of inflation, output growth and global imbalances among a group of 33 countries (26 regions). We account for structural instability by use of country-specific intercept shifts, the timings of which are identified taking into account both statistical evidence and our knowledge of historic economic conditions and events. Using this model, we compute both central forecasts and scenario-based probabilistic forecasts for a range of events of interest, including the sign and trajectory of the balance of trade, the achievement of a short-term inflation target, and the incidence of recession and slow growth. The forecasting performance of the GVAR model in relation to the ongoing financial crisis is quite remarkable. It correctly identifies a pronounced and widespread economic contraction accompanied by a marked shift in the net trade balance of the Eurozone and Japan. Moreover, this promising out-of-sample forecasting performance is substantiated by a raft of statistical tests which indicate that the predictive accuracy of the GVAR model is broadly comparable to that of standard benchmark models over short horizons and superior over longer horizons. Hence we conclude that GVAR models may be a useful forecasting tool for institutions operating at both the national and supra-national levels. Copyright © 2010 John Wiley & Sons, Ltd.

Some studies have shown that body mass index (BMI), weight (kg)/height (m)², has a negative (or no) effect on wage. But BMI representing obesity is a tightly specified function of weight and height, and there is a room for weight given height (i.e. obesity given height) to better explain wage when the tight specification gets relaxed. In this paper, we address the question of weight effect on wage given height, employing two-wave panel data for white females and adopting a semi-linear model consisting of a nonparametric function of weight and height and a linear function of the other regressors. We find that there is no weight effect on wage up to the average weight, beyond which a large negative effect kicks in. Linear BMI models give the incorrect impression of the presence of a ‘wage gain’ by becoming slimmer than the average and of a ‘wage loss’ that is less than what it actually is when going above the average. Copyright © 2010 John Wiley & Sons, Ltd.

We consider the estimation of nonlinear models with mismeasured explanatory variables, when information on the marginal distribution of the true values of these variables is available. We derive a semi-parametric MLE that is shown to be consistent and asymptotically normally distributed. In a simulation experiment we find that the finite sample distribution of the estimator is close to the asymptotic approximation. The semi-parametric MLE is applied to a duration model for AFDC welfare spells with misreported welfare benefits. The marginal distribution of the correctly measured welfare benefits is obtained from an administrative source. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents a model for the heterogeneity and dynamics of the conditional mean and conditional variance of individual wages. A bias-corrected likelihood approach, which reduces the estimation bias to a term of order 1/T², is used for estimation and inference. The small-sample performance of the proposed estimator is investigated in a Monte Carlo study. The simulation results show that the bias of the maximum likelihood estimator is substantially corrected for designs calibrated to the data used in the empirical analysis, drawn from the PSID. The empirical results show that it is important to account for individual unobserved heterogeneity and dynamics in the variance, and that the latter is driven by job mobility. The model also explains the non-normality observed in log-wage data. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper we develop estimation techniques and a specification test for the validity of instrumental variables allowing for conditionally heteroskedastic disturbances. We propose modified two-stage least squares (2SLS) and modified 3SLS procedures where the conditional heteroskedasticity is taken into account, which are natural extensions of the traditional 2SLS and 3SLS estimators and which achieve a lower variance. We recommend the use of these modified 2SLS and 3SLS procedures in practice instead of alternative estimators like limited-information maximum likelihood/full-information maximum likelihood, where the non-existence of moments leads to extreme values, and also for ease of computation. It is shown theoretically and with simulation that in some cases 2SLS, 3SLS and our modified 2SLS and 3SLS procedures can have very severe biases (including the weak instruments case), and we present bias correction procedures to apply in practice along the lines of Flores-Lagunes (2007). Our new estimation procedures can also be used to extend the test for weak instruments of Stock and Yogo (2005) and to allow for conditional heteroskedasticity. Finally, we show the usefulness of our estimation procedures with an application to the demand and supply of fish. Copyright © 2010 John Wiley & Sons, Ltd.

We use Bayesian methods to estimate changes in US post-war monetary policy in the Smets and Wouters model. We perform the estimations by allowing for a break in monetary policy at the time of Volcker's appointment as chairman. This enables us to capture changes in the monetary policy regime introduced by Volcker during the Volcker–Greenspan period. We find support for the assumption that monetary policy in the Volcker–Greenspan period performed optimally under commitment. Our estimation strategy allows us to estimate the preferences of the US Federal Reserve in the Volcker–Greenspan period, where the main objective of policy appears to be inflation, followed by interest rate stabilization, output growth and interest rate smoothing. We find that the Great Moderation of output growth is explained by a combination of two factors: the decrease in the volatility of the structural shocks and the improved monetary policy conduct. Inflation Stabilization, however, is mainly due to the change in monetary policy that took place at the beginning of Volcker's mandate. Copyright © 2010 John Wiley & Sons, Ltd.

We consider the estimation of a sample selection model that exhibits spatial autoregressive errors (SAE). Our methodology is motivated by a two-step strategy where in the first step we estimate a spatial probit model and in the second step (outcome equation) we include an estimated inverse Mills ratio (IMR) as a regressor to control for selection bias. Since the appropriate IMR under SAE depends on a parameter from the second step, both steps are jointly estimated employing the generalized method of moments. We explore the finite sample properties of the estimator using simulations and provide an empirical illustration. Copyright © 2010 John Wiley & Sons, Ltd.

Banking technology is typically characterized by multiple inputs and multiple outputs that are associated with various attributes, such as different types of deposits, loans, number of accounts, classes of employees and location of branches. These quality differentials in inputs and outputs are mostly ignored in empirical studies. These omissions make the practical value of productivity studies in organizations like banks questionable because quality is a key component of performance. This paper proposes using hedonic aggregator functions (as a tool of aggregating inputs and outputs with quality attributes) within an input distance function framework and analyzes the impact of banking deregulation on efficiency and total factor productivity (TFP) change in the Indian banking industry using panel data for the period 1996–2005. Empirical results indicate that banks have improved their efficiency (from 61% in 1996 to 72% in 2005) during the post-deregulation period, and the gain in efficiency of state-owned banks has surpassed that of private banks. Improvement in capital base, as indicated by increased capital adequacy ratio, played an important role in ushering efficiency gain. The return to scale estimate suggests that state-owned banks are operating far above their efficient scale and cost savings can be obtained by reducing their size of operations. Overall, TFP growth was above 3.5% annually. Both technical progress and technical efficiency change consistently played an important role in shaping TFP growth. Copyright © 2010 John Wiley & Sons, Ltd.

We examine whether expert appraisals provided to bidders before major art auctions are unbiased indicators of value. Despite a strong grounding in theory, this aspect of optimal auction design has been frequently challenged in previous empirical research, particularly in the market for fine art. We adopt a valuation benchmark that incorporates sellers' reserve prices as well as high bids, and recognize censoring of works that fail to make reserve. Although the auction houses never divulge reserve prices, we exploit the fact that they can be observed indirectly via their impact on buy-in rates. Using the set of French Impressionist paintings brought to auction from 1985 to 2001, we estimate the distribution of reserve prices, establish their link to a proper valuation benchmark, and isolate the selection bias due to bought-in works on the perceived market value of fine art works. After controlling for the impact of reserve prices, and considering all works brought to auction, we find no evidence of bias in the experts' pre-sale estimates. Copyright © 2010 John Wiley & Sons, Ltd.

In the fixed-effects stochastic frontier model an efficiency measure relative to the best firm in the sample is universally employed. This paper considers a new measure relative to the worst firm in the sample. We find that estimates of this measure have smaller bias than those of the traditional measure when the sample consists of many firms near the efficient frontier. Moreover, a two-sided measure relative to both the best and the worst firms is proposed. Simulations suggest that the new measures may be preferred depending on the skewness of the inefficiency distribution and the scale of efficiency differences. Copyright © 2010 John Wiley & Sons, Ltd.

We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation process, it can be viewed as a generalization of multivariate stochastic volatility models. Despite the complexity of the model, the decoupling of marginals and dependence parameters facilitates estimation. We propose estimation in two steps, where first the parameters of the marginal distributions are estimated, and then those of the copula. Parameters of the latent processes (volatilities and dependence) are estimated using efficient importance sampling. We discuss goodness-of-fit tests and ways to forecast the dependence parameter. For two bivariate stock index series, we show that the proposed model outperforms standard competing models. Copyright © 2010 John Wiley & Sons, Ltd.

The effect of preferential trade agreements (PTAs) on trade flows is subject to model uncertainty stemming from the diverse and even contradictory effects suggested by the theoretical PTA literature. The existing empirical literature has produced remarkably disparate results and the wide variety of empirical approaches reflects the uncertainty about the ‘correct’ set of explanatory variables that ought to be included in the analysis. To account for the model uncertainty that surrounds the validity of the competing PTA theories, we introduce Bayesian model averaging (BMA) to the PTA literature. Statistical theory shows that BMA successfully incorporates model uncertainty in linear regression analysis by minimizing the mean squared error, and by generating predictive distributions with optimal predictive performance. Once model uncertainty is addressed as part of the empirical strategy, we find strong evidence of trade creation, trade diversion, and open bloc effects. Our results are robust to a range of alternative empirical specifications proposed by the recent PTA literature. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a test to check the specification of models with unobserved individual effects integrated out by quadrature and also a simple way of increasing the flexibility of this type of model. The results of a Monte Carlo study and an application using a well-known dataset illustrate the finite sample properties of the proposed methods and their implementation in practice. Copyright © 2010 John Wiley & Sons, Ltd.

A widely applied method for differentiated product demand estimation, introduced by Berry, Levinsohn and Pakes in 1995, is founded on matching observed and theoretical market shares of products. In this paper, we allow for discrete consumer tastes and derive an equivalent matching occurring in the consumer type space. The equivalence between the two formulations expresses a duality between market shares and consumer types. In applications where a large number of products and a small number of consumer types is natural, the dual formulation introduced in this paper is computationally more efficient than the primal. Indeed, simulation exercises show that the dual method can be significantly faster. Copyright © 2010 John Wiley & Sons, Ltd.

In a recent article Newey and Windmeijer (Generalized method of moments with many weak moment conditions. Econometrica 2009; 77(3): 687–719) propose a new variance estimator for generalized empirical likelihood. In Monte Carlo examples they show that t-statistics based on the new variance estimator have nearly correct size. I have replicated their Monte Carlo simulations and in addition used the new variance estimator to re-estimate Angrist and Krueger's (Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics 1991; 106(4): 979–1014) returns to education. Copyright © 2011 John Wiley & Sons, Ltd.