Search

In this paper, we study the asymptotic behavior of specification tests in conditional moment restriction models under first‐order local identification failure with dependent data. More specifically, we obtain conditions under which the conventional specification test for conditional moment restrictions retains its standard normal limit when first‐order local identification fails but global identification is still attainable. In the process, we derive some novel intermediate results that include extending the first‐ and second‐order local identification framework to models defined by conditional moment restrictions, establishing the rate of convergence of the GMM estimator and characterizing the asymptotic representation for degenerate U‐statistics under strong mixing dependence. Importantly, the specification test is robust to first‐order local identification failure regardless of the number of directions in which the Jacobian of the conditional moment restrictions is degenerate and remains valid even if the model is first‐order identified. [ABSTRACT FROM AUTHOR]

This paper studies inference in randomized controlled trials with multiple treatments, where treatment status is determined according to a "matched tuples" design. If there are |D| possible treatments, then by a matched tuples design, we mean an experimental design where units are sampled i.i.d. from the population of interest, grouped into "homogeneous" blocks of size |D|, and finally, within each block, exactly one individual is randomly assigned to each of the |D| treatments. We first study estimation and inference for matched tuples designs in the general setting where the parameter of interest is a vector of linear contrasts over the collection of average potential outcomes for each treatment. Parameters of this form include standard average treatment effects used to compare one treatment relative to another, but also include parameters that may be of interest in the analysis of factorial designs. We first establish conditions under which a sample analog estimator is asymptotically normal and construct a consistent estimator of its corresponding asymptotic variance. Combining these results establish the asymptotic exactness of tests based on these estimators. In contrast, we show that, for two common testing procedures based on t‐tests constructed from linear regressions, one test is generally conservative while the other is generally invalid. We go on to apply our results to study the asymptotic properties of what we call "fully‐blocked" 2K factorial designs, which are simply matched tuples designs applied to a full factorial experiment. Leveraging our previous results, we establish that our estimator achieves a lower asymptotic variance under the fully‐blocked design than that under any stratified factorial design, which stratifies the experimental sample into a finite number of "large" strata. A simulation study and empirical application illustrate the practical relevance of our results. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to identification of the semiparametric multinomial choice model with fixed effects. The framework employed is the semiparametric version of the traditional multinomial logit with the fixed‐effects model (Chamberlain (1980)). This semiparametric multinomial choice model places no restrictions on either the joint distribution of the random utility disturbances across choices or their within group (or across time) correlations. We show that a novel within‐group comparison leads to a set of conditional moment inequalities. Our main finding shows that the derived conditional moment inequalities yield the sharp identified set for the random utility covariate index, while avoiding the incidental parameter problem. Specializing this result to the binary choice case shows that Manski (1987)'s conditional moment inequalities still lead to sharp bounds without restrictions on covariates. [ABSTRACT FROM AUTHOR]

This paper studies inference for the realized Laplace transform (RLT) of volatility in a fixed‐span setting using bootstrap methods. Specifically, since standard wild bootstrap procedures deliver inconsistent inference, we propose a local Gaussian (LG) bootstrap, establish its first‐order asymptotic validity, and use Edgeworth expansions to show that the LG bootstrap inference achieves second‐order asymptotic refinements. Moreover, we provide new Laplace transform‐based estimators of the spot variance as well as the covariance, correlation, and beta between two semimartingales, and adapt our bootstrap procedure to the requisite scenario. We establish central limit theory for our estimators and first‐order asymptotic validity of their associated bootstrap methods. Simulations demonstrate that the LG bootstrap outperforms existing feasible inference theory and wild bootstrap procedures in finite samples. Finally, we illustrate the use of the new methods by examining the coherence between stocks and bonds during the global financial crisis of 2008 as well as the COVID‐19 pandemic stock sell‐off during 2020, and by a forecasting exercise. [ABSTRACT FROM AUTHOR]

Structural quantitative work in international economics typically models trade costs as a log‐linear function of exogenous trade‐policy variables. We propose a structural approach that allows for a nonparametric relationship and for treating tariff and nontariff trade‐policy variables as potentially endogenous. The data reject the assumption of log‐linearity of trade costs in both tariff‐ and nontariff‐policy variables. We assess the effects of a unilateral increase of US tariffs on Chinese imports by 10 percentage points and document that the estimated effects on real bilateral trade‐flow changes would be substantially underestimated by standard approaches. [ABSTRACT FROM AUTHOR]

This paper provides estimation and inference methods for conditional average treatment effects (CATE) characterized by a high‐dimensional parameter in both homogeneous cross‐sectional and unit‐heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable with explanatory variables. The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross‐fitted residuals. This step uses a novel generic cross‐fitting method that we design for weakly dependent time series and panel data. This method "leaves out the neighbors" when fitting nuisance components, and we theoretically power it by using Strassen's coupling. As a result, we can rely on any modern machine learning method in the first step, provided it learns the residuals well enough. Second, we construct an orthogonal (or residual) learner of CATE—the lasso CATE—that regresses the outcome residual on the vector of interactions of the residualized treatment with explanatory variables. If the complexity of CATE function is simpler than that of the first‐stage regression, the orthogonal learner converges faster than the single‐stage regression‐based learner. Third, we perform simultaneous inference on parameters of the CATE function using debiasing. We also can use ordinary least squares in the last two steps when CATE is low‐dimensional. In heterogeneous panel data settings, we model the unobserved unit heterogeneity as a weakly sparse deviation from Mundlak's (1978) model of correlated unit effects as a linear function of time‐invariant covariates and make use of L1‐penalization to estimate these models. We demonstrate our methods by estimating price elasticities of groceries based on scanner data. We note that our results are new even for the cross‐sectional (i.i.d.) case. [ABSTRACT FROM AUTHOR]

Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results. [ABSTRACT FROM AUTHOR]

This paper develops nonparametric methods for welfare‐analysis of economic changes in the common setting of multinomial choice. The results cover (a) simultaneous price‐change of multiple alternatives, (b) introduction/elimination of an option, (c) changes in choice‐characteristics, and (d) choice among nonexclusive alternatives. In these cases, Marshallian consumer surplus becomes path‐dependent, but Hicksian welfare remains well‐defined. We demonstrate that under completely unrestricted preference‐heterogeneity and income‐effects, the distributions of Hicksian welfare are point‐identified from structural choice‐probabilities in scenarios (a), (b), and only set‐identified in (c), (d). In program‐evaluation contexts, our results enable the calculation of compensated‐effects, that is, the program's cash‐equivalent and resulting deadweight‐loss. They also facilitate a theoretically justified cost‐benefit comparison of interventions targeting different outcomes, for example, a tuition‐subsidy and a health‐product subsidy. Welfare analyses under endogeneity is briefly discussed. An application to data on choice of fishing‐mode illustrates the methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate what can be learned about average counterfactual outcomes as well as average treatment effects when it is assumed that treatment response functions are smooth. We obtain a set of new partial identification results for both the average treatment response and the average treatment effect. In particular, we find that the monotone treatment response and monotone treatment selection bound of Manski and Pepper, 2000 can be further tightened if we impose the smoothness conditions on the treatment response. Since it is unknown in practice whether the imposed smoothness restriction is met, it is desirable to conduct a sensitivity analysis with respect to the smoothness assumption. We demonstrate how one can carry out a sensitivity analysis for the average treatment effect by varying the degrees of smoothness assumption. We illustrate our findings by reanalyzing the return to schooling example of Manski and Pepper, 2000 and also by measuring the effect of the length of job training on the labor market outcomes. [ABSTRACT FROM AUTHOR]

This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and functionals of h0. First, we derive sup‐norm convergence rates for computationally simple sieve NPIV (series two‐stage least squares) estimators of h0 and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup‐norm rates of estimating h0 and its derivatives, and show that the sieve NPIV estimator can attain the minimax rates when h0 is approximated via a spline or wavelet sieve. Our optimal sup‐norm rates surprisingly coincide with the optimal root‐mean‐squared rates for severely ill‐posed problems, and are only a logarithmic factor slower than the optimal root‐mean‐squared rates for mildly ill‐posed problems. Third, we use our sup‐norm rates to establish the uniform Gaussian process strong approximations and the score bootstrap uniform confidence bands (UCBs) for collections of nonlinear functionals of h0 under primitive conditions, allowing for mildly and severely ill‐posed problems. Fourth, as applications, we obtain the first asymptotic pointwise and uniform inference results for plug‐in sieve t‐statistics of exact consumer surplus (CS) and deadweight loss (DL) welfare functionals under low‐level conditions when demand is estimated via sieve NPIV. Our real data application of UCBs for exact CS and DL functionals of gasoline demand reveals interesting patterns and is applicable to other goods markets. [ABSTRACT FROM AUTHOR]

ABSTRACT The empirical financial literature reports evidence of mean reversion in stock prices and the absence of out-of-sample return predictability over horizons shorter than 10 years. Anecdotal evidence suggests the presence of mean reversion in stock prices and return predictability over horizons longer than 10 years, but thus far, there is no empirical evidence confirming such anecdotal evidence. The goal of this paper is to fill this gap in the literature. Specifically, using 141 years of data, this paper begins by performing formal tests of the random walk hypothesis in the prices of the real S&P Composite Index over increasing time horizons of up to 40 years. Although our results cannot support the conventional wisdom that the stock market is safer for long-term investors, our findings speak in favor of the mean reversion hypothesis. In particular, we find statistically significant in-sample evidence that past 15-17 year returns are able to predict the future 15-17 year returns. This finding is robust to the choice of data source, deflator, and test statistic. The paper continues by investigating the out-of-sample performance of long-horizon return forecasting based on the mean-reverting model. These latter tests demonstrate that the forecast accuracy provided by the mean-reverting model is statistically significantly better than the forecast accuracy provided by the naive historical-mean model. Moreover, we show that the predictive ability of the mean-reverting model is economically significant and translates into substantial performance gains. [ABSTRACT FROM AUTHOR]

This paper considers estimation and inference for heterogeneous counterfactual effects with high‐dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux (2009)) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy, which counterfactually extends the duration of exposures to the Job Corps training program, will be effective especially for the targeted subpopulations of lower potential wage earners. [ABSTRACT FROM AUTHOR]

This paper presents a procedure for recovering causal coefficients from selected samples that uses random forests, a popular machine‐learning algorithm. This proposed method makes few assumptions regarding the selection equation and the distribution of the error terms. Our Monte Carlo results indicate that our method performs well, even when the selection and outcome equations contain the same variables, as long as the selection equation is nonlinear. The method can also be used when there are many variables in the selection equation. We also compare the results of our procedure with other parametric and semiparametric methods using real data. [ABSTRACT FROM AUTHOR]

This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then nonparametric weak instruments are defined as a sequence of reduced‐form functions where the associated rank shrinks to zero. The problem of weak instruments is characterized as concurvity, which motivates the introduction of a regularization scheme. The paper proposes a penalized series estimation method to alleviate the effects of weak instruments and shows that it achieves desirable asymptotic properties. A data‐driven procedure is proposed for the choice of the penalization parameter. The findings of this paper provide useful implications for empirical work. To illustrate them, Monte Carlo results are presented and an empirical example is given in which the effect of class size on test scores is estimated nonparametrically. [ABSTRACT FROM AUTHOR]

This paper is concerned with estimation of functionals of a latent weight function that satisfies possibly high‐dimensional multiplicative moment conditions. Main examples are functionals of stochastic discount factors in asset pricing, missing data problems, and treatment effects. We propose to estimate the latent weight function by an information theoretic approach combined with the ℓ1‐penalization technique to deal with high‐dimensional moment conditions under sparsity. We study asymptotic properties of the proposed method and illustrate it by a theoretical example on treatment effect analysis and empirical example on estimation of stochastic discount factors. [ABSTRACT FROM AUTHOR]

This paper studies the evolution of regional income disparities in Europe. Besides using a more complete data set that offers a more detailed look at the evolution of regional incomes in Western Europe than previous studies, it is the first to shed empirical light on regional income differences and their evolution in Eastern Europe during the transition phase from communism towards EU membership by means of a (spatial) Markov chain analysis. Regional income disparities in Western Europe are found to be decreasing over time and less persistent than reported in earlier studies. In the case of Eastern Europe some regions are likely to fall behind in terms of GDP per capita whereas a substantial number of other regions will be able to (slowly) catch up with their Western neighbours. Moreover in Western Europe localized regional conditions appear to be a main determinant of the observed income differences, whereas in Eastern Europe country-specific factors are of bigger importance. [ABSTRACT FROM AUTHOR]

This paper introduces the appropriate econometric methodology and statistical tests to evaluate the spatial and sectoral stability of the labour productivity convergence process of 145 European regions over 1975–2000. Our results display significant σ-convergence only in aggregate labour productivity and in the services sectors among peripheral regions. We also show that omitting spatial effects leads to biased measures of σ-convergence. We then estimate a pooled β-convergence model including spatial autocorrelation and sectoral differentiation. Spatial and sectoral stability tests reveal which sectors display a convergence process and in which regime they are located. Resumen Este artículo presenta los test estadísticos y la metodología econométrica apropiados para evaluar la estabilidad espacial y sectorial del proceso de convergencia de la productividad laboral de 145 regiones europeas del 1975–2000. Nuestros resultados muestran una sigma-convergencia significativa solamente en la productividad laboral agregada y en el sector servicios entre regiones periféricas. También mostramos que la omisión de efectos espaciales resulta en medidas sesgadas de la sigma-convergencia. A continuación estimamos un modelo de beta-convergencia combinada que incluye autocorrelación espacial y diferenciación sectorial. Los test de estabilidad espacial y sectorial revelan los sectores que muestran un proceso de convergencia y en qué régimen se hallan ubicados. [ABSTRACT FROM AUTHOR]

This paper evaluates the impact of structural funds on the convergence process between 145 European regions over 1989–1999. The presence of spillover effects is investigated with spatial econometric methods, which assess the impact of the funds on the targeted region and its neighbours. We also control the potential endogeneity problem in the estimation of their impact. Our estimation results indicate that significant convergence takes place, but that the funds have no impact on it. Simulation experiments show how investments targeted to the peripheral regions never spill over to their neighbours, which calls for a reconsideration of current regional policy tools. Resumen. Este artículo evalúa el impacto de los fondos estructurales en el proceso de convergencia entre 145 regiones europeas durante el periodo 1989–1999. La presencia de efectos derrame ( spill over) se investigó mediante métodos econométricos espaciales, que evalúan el impacto de los fondos en la región objetivo y sus vecinas. También controlamos el problema de endogeneidad potencial en la estimación del impacto. Los resultados de nuestra estimación indican la presencia significativa de una convergencia, pero que los fondos no tienen un impacto sobre la misma. Experimentos de simulación muestran que las inversiones dirigidas a las regiones periféricas nunca derraman ( spill over) sobre sus vecinas, lo cual pide reconsiderar las herramientas actuales de política regional. [ABSTRACT FROM AUTHOR]

This paper proposes a nonparametric approach for estimation of stochastic production functions with categorical and continuous variables, and then develops procedures that allow for inference on production risk. The estimation is based on the kernel method and the inference is based on a bootstrapping approach. We establish the asymptotic properties of our proposed estimator. Monte Carlo simulation results suggest that our proposed nonparametric procedure is more robust and outperforms other existing parametric and nonparametric methods. In addition, we empirically illustrate the proposed nonparametric approach using long‐run corn production data from university field trials in Wisconsin that examines the performance of genetically modified corn varieties. Specifically, the proposed nonparametric procedure is used to empirically examine the production risk effects of categorical genetically modified variety variables and a continuous planting density variable. [ABSTRACT FROM AUTHOR]

This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment—as well as mixture of discrete and continuous treatment—under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile, and asymmetric least squares causal effect of treatment as special cases. For this general framework, we first derive the semiparametric efficiency bound for the causal effect of treatment, extending the existing bound results to a wider class of models. We then propose a generalized optimization estimator for the causal effect with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we establish the consistency and asymptotic normality of the proposed estimator of the causal effect and show that the estimator attains the semiparametric efficiency bound, thereby extending the existing literature on efficient estimation of causal effect to a wider class of applications. Finally, we discuss estimation of some causal effect functionals such as the treatment effect curve and the average outcome. To evaluate the finite sample performance of the proposed procedure, we conduct a small‐scale simulation study and find that the proposed estimation has practical value. In an empirical application, we detect a significant causal effect of political advertisements on campaign contributions in the binary treatment model, but not in the continuous treatment model. [ABSTRACT FROM AUTHOR]

Strategic interaction parameters characterize the impact of actions of one economic agent on the payoff of another economic agent, and are of great interest in both theoretical and empirical work. In this paper, by considering econometric models involving simultaneous discrete systems of equations, we study how the information available to economic agents regarding other economic agents can influence whether or not these strategic information parameters can be inferred from the observed actions. We consider two extreme cases: the complete information case where the information sets of participating economic agents coincide and the incomplete information case where each agent's payoffs are privately observable. We find that in models with complete information, the strategic interaction parameters are more difficult to recover than they are in incomplete information models. We show this by exploring the Fisher information (from standard statistics literature) for the strategic interaction parameters in each of these models. Our findings are that in complete information models, the statistical (Fisher) information for the interaction parameters is zero, implying the difficulty in recovering them from data. In contrast, for incomplete information models, the Fisher information for the interaction parameters is positive, indicating that not only can these parameters be relatively easy to recover from data, but standard inference can be conducted on them. This finding is illustrated in two cases: treatment effect models (expressed as a triangular system of equations) and static game models. [ABSTRACT FROM AUTHOR]

ABSTRACT: This paper examines co‐movement between stock returns and changes in 10‐year government bond yields as well as flight‐to‐quality behaviour in G7 countries. We conduct the wavelet squared coherence analysis to explore the dynamics in both time and frequency domain. Our results provide evidence of positive co‐movements, which vary over time and across investment horizon. The higher co‐movement is found to be more concentrated in the lower frequency bands. We further analyse the dynamic nature of the scale‐dependent wavelet correlations and find that the correlations are highly volatile and significantly increase across different time scales during the episodes of equity market turbulence. The increase in correlations reflects flights from stocks to safer bond investments as a result of dramatic changes in investor sentiment and risk aversion at times of market stress. [ABSTRACT FROM AUTHOR]

This paper examines affluence and poverty interdependence across 185 countries and its evolution over 1969–2014. To estimate affluence and poverty interdependence and derive tail interdependences the tail copulae are applied to multivariate density function. The tail coefficients are estimated in the non‐parametric way as in Schmidt and Stadtmüller (2006). The estimates show, that poverty is less interdependent and continue to decrease, while affluence has asymptotically high global dependence, meaning a higher global dependence on and sensitivity to the well‐being of the affluent countries. The received results derive the pattern of the extreme interdependence and can help to identify poverty and affluence spill‐over across countries and regions and calculate the average sensitivity of a country to these phenomena on the global level and can potentially help in poverty reduction strategies within the Sustainable Development Goal by the United Nations. [ABSTRACT FROM AUTHOR]

The key assumption in regression discontinuity analysis is that the distribution of potential outcomes varies smoothly with the running variable around the cutoff. In many empirical contexts, however, this assumption is not credible; and the running variable is said to be manipulated in this case. In this paper, we show that while causal effects are not point identified under manipulation, one can derive sharp bounds under a general model that covers a wide range of empirical patterns. The extent of manipulation, which determines the width of the bounds, is inferred from the data in our setup. Our approach therefore does not require making a binary decision regarding whether manipulation occurs or not, and can be used to deliver manipulation‐robust inference in settings where manipulation is conceivable, but not obvious from the data. We use our methods to study the disincentive effect of unemployment insurance on (formal) reemployment in Brazil, and show that our bounds remain informative, despite the fact that manipulation has a sizable effect on our estimates of causal parameters. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate‐adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a new model selection test for the statistical comparison of semi/non‐parametric models based on a general quasi‐likelihood ratio criterion. An important feature of the new test is its uniformly exact asymptotic size in the overlapping nonnested case, as well as in the easier nested and strictly nonnested cases. The uniform size control is achieved without using pretesting, sample‐splitting, or simulated critical values. We also show that the test has nontrivial power against all n‐local alternatives and against some local alternatives that converge to the null faster than n. Finally, we provide a framework for conducting uniformly valid post model selection inference for model parameters. The finite sample performance of the nondegenerate test and that of the post model selection inference procedure are illustrated in a mean‐regression example by Monte Carlo. [ABSTRACT FROM AUTHOR]

This paper studies the inference problem of an infinite‐dimensional parameter with a shape restriction. This parameter is identified by arbitrarily many unconditional moment equalities. The shape restriction leads to a convex restriction set. I propose a test of the shape restriction, which controls size uniformly and applies to both point‐identified and partially identified models. The test can be inverted to construct confidence sets after imposing the shape restriction. Monte Carlo experiments show the finite‐sample properties of this method. In an empirical illustration, I apply the method to ascending auctions held by the US Forest Service and show that imposing shape restrictions can significantly improve inference. [ABSTRACT FROM AUTHOR]

This paper proposes a new bivariate competing risks model in which both durations are the first passage times of dependent Lévy subordinators with exponential thresholds and multiplicative covariates effects. Our specification extends the mixed proportional hazards model, as it allows for the time‐varying heterogeneity represented by the unobservable Lévy processes and it generates the simultaneous termination of both durations with positive probability. We obtain nonparametric identification of all model primitives given competing risks data. A flexible semiparametric estimation procedure is provided and illustrated through the analysis of a real dataset. [ABSTRACT FROM AUTHOR]

Triangular systems with nonadditively separable unobserved heterogeneity provide a theoretically appealing framework for the modeling of complex structural relationships. However, they are not commonly used in practice due to the need for exogenous variables with large support for identification, the curse of dimensionality in estimation, and the lack of inferential tools. This paper introduces two classes of semiparametric nonseparable triangular models that address these limitations. They are based on distribution and quantile regression modeling of the reduced form conditional distributions of the endogenous variables. We show that average, distribution, and quantile structural functions are identified in these systems through a control function approach that does not require a large support condition. We propose a computationally attractive three‐stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. These results establish the validity of the bootstrap for three‐stage estimators of structural functions, and lead to simple inference algorithms. We illustrate the implementation and applicability of all our methods with numerical simulations and an empirical application to demand analysis. [ABSTRACT FROM AUTHOR]

Economic disparities between the regions of the European Union are of constant concern both for policy and economic research. In this paper, we examine whether there are overlapping trends of regional development in the EU: overall convergence, on the one hand, and persistent or even increasing spatial concentration (agglomeration), on the other. Kernel density estimation, Markov chain analysis and cross-sectional regressions provide evidence that convergence of regional per-capita income in the EU15 has become considerably stronger in the 1990s. The reduction of income disparities, however, is a phenomenon between nations but not between regions within the EU countries. European integration (and possibly European regional policy) fosters the catching-up of lagging countries but at the same time forces towards agglomeration of economic activities tend to increase disparities within the EU member states. Resumen. Las disparidades económicas entre regiones de la Unión Europea son una preocupación constante para la investigación relacionada con políticas y con economía. En este artículo examinamos si existen tendencias superpuestas de desarrollo regional en la UE: convergencia en términos generales, por un lado, y por otro una concentración espacial (aglomeración) continua o incluso en aumento. La estimación por núcleos ( kernel) de la función de densidad, el análisis mediante cadenas de Markov y las regresiones transversales aportan pruebas de que la convergencia de ingresos regionales per cápita de la UE15 se ha fortalecido considerablemente en la década de los noventa. La reducción en la disparidad de ingresos, sin embargo, es un fenómeno entre naciones pero no entre regiones dentro de países de la UE. La integración europea (y quizá la política regional europea) fomenta la puesta al día de los países rezagados pero al mismo tiempo las fuerzas conducentes a la aglomeración de actividades económicas tienden a aumentar las disparidades dentro de los estados miembros de la UE. [ABSTRACT FROM AUTHOR]

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences Assumption used for identifying the Average Treatment Effect on the Treated (ATT). Identification of the QTT is more complicated than the ATT though because it depends on the unknown dependence (or copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. To address this issue, we introduce a new Copula Stability Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available, the missing dependence can be recovered, and the QTT is identified. We use our method to estimate the effect of increasing the minimum wage on quantiles of local labor markets' unemployment rates and find significant heterogeneity. [ABSTRACT FROM AUTHOR]

This paper studies inference in randomized controlled trials with covariate‐adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni, Canay, and Shaikh (2018), covariate‐adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve "balance" within each stratum. Importantly, in contrast to Bugni, Canay, and Shaikh (2018), we not only allow for multiple treatments, but further allow for the proportion of units being assigned to each of the treatments to vary across strata. We first study the properties of estimators derived from a "fully saturated" linear regression, that is, a linear regression of the outcome on all interactions between indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity‐consistent estimator of the asymptotic variance are invalid in the sense that they may have limiting rejection probability under the null hypothesis strictly greater than the nominal level; on the other hand, tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact in the sense that they have limiting rejection probability under the null hypothesis equal to the nominal level. For the special case in which the target proportion of units being assigned to each of the treatments does not vary across strata, we additionally consider tests based on estimators derived from a linear regression with "strata fixed effects," that is, a linear regression of the outcome on indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity‐consistent estimator of the asymptotic variance are conservative in the sense that they have limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level, but tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact, thereby generalizing results in Bugni, Canay, and Shaikh (2018) for the case of a single treatment to multiple treatments. A simulation study and an empirical application illustrate the practical relevance of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components over time. We apply the identification and estimation method to the earnings dynamics of U.S. men using the Panel Survey of Income Dynamics (PSID). The results show that the marginal distributions of permanent and transitory earnings components are more dispersed, more skewed, and have fatter tails than the normal and that earnings mobility is much lower than for the normal. We also find strong evidence for the existence of higher‐order ARMA processes in the transitory component, which lead to much different estimates of the distributions of and earnings mobility in the permanent component, implying that misspecification of the process for transitory earnings can affect estimated distributions of the permanent component and estimated earnings dynamics of that component. Thus our flexible model implies earnings dynamics for U.S. men different from much of the prior literature. [ABSTRACT FROM AUTHOR]

This paper develops a dynamic model of consumer search that, despite placing very little structure on the dynamic problem faced by consumers, allows us to exploit intertemporal variation in price distributions to estimate the distribution from which consumer search costs are initially drawn. We show that static approaches to estimating this distribution may suffer from dynamic sample selection bias. This can happen if consumers are forward‐looking and delay their purchases in a way that systematically depends on their individual search costs. We consider identification of the population search cost distribution using only price data and develop estimable nonparametric upper and lower bounds on the distribution function, as well as a nonlinear least squares estimator for parametric models. We also consider the additional identifying power of weak, theoretical assumptions such as monotonicity of purchase probabilities in search costs. We apply our estimators to analyze the online market for two widely used econometrics textbooks. Our results suggest that static estimates of the search cost distribution are biased upwards, in a distributional sense, relative to the true population distribution. We illustrate this and other forms of bias in a small‐scale simulation study. [ABSTRACT FROM AUTHOR]

In this paper, I study identification of a nonseparable model with endogeneity arising due to unobserved heterogeneity. Identification relies on the availability of binary proxies that can be used to control for the unobserved heterogeneity. I show that the model is identified in the limit as the number of proxies increases. The argument does not require an instrumental variable that is excluded from the outcome equation nor does it require the support of the unobserved heterogeneity to be finite. I then propose a nonparametric estimator that is consistent as the number of proxies increases with the sample size. I also show that, for a fixed number of proxies, nontrivial bounds on objects of interest can be obtained. Finally, I study two real data applications that illustrate computation of the bounds and estimation with a large number of items. [ABSTRACT FROM AUTHOR]

In this paper, we suggest and analyze a new class of specification tests for random coefficient models. These tests allow to assess the validity of central structural features of the model, in particular linearity in coefficients, generalizations of this notion like a known nonlinear functional relationship, or degeneracy of the distribution of a random coefficient, that is, whether a coefficient is fixed or random, including whether an associated variable can be omitted altogether. Our tests are nonparametric in nature, and use sieve estimators of the characteristic function. We provide formal power analysis against global as well as against local alternatives. Moreover, we perform a Monte Carlo simulation study, and apply the tests to analyze the degree of nonlinearity in a heterogeneous random coefficients demand model. While we find some evidence against the popular QUAIDS specification with random coefficients, it is not strong enough to reject the specification at the conventional significance level. [ABSTRACT FROM AUTHOR]

The objective of the paper is to analyse the local determinants of innovation in a small European metropolitan region. First, we examine the extent to which geographic space is a determinant of innovation for five intra-regional units. Second, we investigate whether innovation is dependent on accessibility to the mean centre. In both cases we examine innovation propensity and innovation output using microdata from the Community Innovation Survey carried out in Luxembourg. The paper provides evidence of a link between the effects on innovation at the intra-regional level of firms' profiles and agglomeration externalities. [ABSTRACT FROM AUTHOR]

This paper studies an identification problem that arises when clinicians seek to personalize patient care by predicting health outcomes conditional on observed patient covariates. Let y be an outcome of interest and let (x=k, w=j) be observed patient covariates. Suppose a clinician wants to choose a care option that maximizes a patient's expected utility conditional on the observed covariates. To accomplish this, the clinician needs to know the conditional probability distribution P(y|x=k, w=j). It is common to have a trustworthy evidence‐based risk assessment that predicts y conditional on a subset of the observed covariates, say x, but not conditional on (x, w). Then the clinician knows P(y|x=k) but not P(y|x=k, w=j). Research on the ecological inference problem studies partial identification of P(y|x, w) given knowledge of P(y|x) and P(w|x). Combining this knowledge with structural assumptions yields tighter conclusions. A psychological literature comparing actuarial predictions and clinical judgments has concluded that clinicians should not attempt to subjectively predict patient outcomes conditional on covariates that are not utilized in evidence‐based risk assessments. I argue that formalizing clinical judgment through analysis of the identification problem can improve risk assessments and care decisions. [ABSTRACT FROM AUTHOR]

It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual‐level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined data set. We demonstrate the conditions on the observable marginal distributions of data in individual data sets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in a semiparametric setting such as ours. Provided that the (nonparametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a “de‐anonymization” of the previously “anonymized” information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined data set allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire data set, and thus point inference requires the use of the most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the “disclosive” nature of policies implemented by the data‐driven companies (such as internet services companies and medical companies using individual patient records for policy decisions). [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the 2006 to 2007 time period to determine the extent to which the release of the Federal Reserve minutes affects equity volatility and returns for 2832 individual firms. Using intraday data, we find that equity returns are essentially unaffected by FOMC minutes releases. We do find evidence of volatility effects, in that conditional volatility is lower prior to the minutes release and higher after the minutes release on release days, relative to a “control” day one week prior to the release date. These differences manifest at the 2:00–2:05pm interval, and generally dissipate within 15min. Consistent with previous literature, we also find evidence of both industry-specific and firm size effects in our data. Finally, we see that volatility is higher (lower) when the minutes are released after the Federal Reserve engages in restrictive (expansionary) monetary policy. Our results are robust to a variety of different definitions of the “control” dates, as well as differing industry definitions. [Copyright &y& Elsevier]

Characteristics of inflation play a key role in policy formulation and market analysis. Several studies have analyzed inflation persistence and reached diverging conclusions. In this paper, we investigate the dynamics of inflation persistence using fractionally integrated processes and find that there has been a clear decline in inflation persistence in the United States over the past two decades. We also show that the presence of fractional integration in inflation successfully explains previous diverging results. Lastly, we provide some international comparisons to examine the extent to which there has been a commensurate decline in inflation persistence in the other G7 economies. [ABSTRACT FROM AUTHOR]

Productivity studies that use proxy variable estimators routinely specify single‐output production functions despite most firms producing multiple outputs. This is usually accomplished by aggregating the firm's outputs using total revenue. Such a formulation rarely provides an adequate representation of the multiproduct firm's production: (a) it cannot identify technological cross‐output trade‐offs along the production possibilities frontier, and (b) it unrealistically assumes perfect output substitutability that, if misspecified, produces biased estimates. We develop a new methodology for estimating multiproduct production functions that addresses these limitations without requiring information beyond that which is available in most datasets and showcase it on Norwegian dairy‐and‐beef farming. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper applies nonparametric decision tree models to the analysis of financial leverage decisions. This approach presents three appealing features: (i) the relationship between leverage and explanatory variables is not predetermined but is derived from information provided by the data, (ii) the models respect the fractional nature of leverage ratios, and (iii) each covariate is allowed to influence in different ways the financial leverage decisions of firms automatically assigned to different groups. Based on a data set of Portuguese firms, decision trees are used to tackle both classification (the decision to issue debt) and regression (the decision on the amount of debt to be issued, conditional on using debt) problems. It is found that: (i) two-part models are the most appropriate specification for explaining the overall amount of debt used by firms, (ii) there are no drastic differences between the results produced by tree and parametric models, although some divergences may arise, and (iii) tree models suggest relationships between covariates and leverage that parametric models fail to capture, especially when the sample size is small. [ABSTRACT FROM AUTHOR]

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set. [ABSTRACT FROM AUTHOR]

This paper analyses the determinants of regional economic growth in the European Union adopting a non-parametric approach. Although the local-linear kernel estimator applied does not explicitly take into account the spatial dimension of the data, it is found to be consistent in our context. In addition, the geographically weighted regression turns out to be less efficient. We obtain evidence of a non-linear relationship between regional growth and its determinants in the form of parameter heterogeneity and threshold effects. These non-linearities mainly affect the initial productivity of labour, the human capital endowment and, as a novelty, the level of infrastructures. [ABSTRACT FROM AUTHOR]

ABSTRACT Given its significant policy implications, the nexus between public expenditures and economic growth has been the subject of an extensive and often emotive theoretical and empirical debate. In this paper, a semiparametric model is used to explore the link between GDP and defence and non-defence government spending in the USA over the period 1929-2009. Evidence reported herein indicates that the latter represents a greater stimulus vis-à-vis the former. [ABSTRACT FROM AUTHOR]

We bound the distribution of treatment effects under plausible and testable assumptions on the joint distribution of potential outcomes, namely that potential outcomes are mutually stochastically increasing. We show how to test the empirical restrictions implied by those assumptions. The resulting bounds substantially sharpen bounds based on classical inequalities. We apply our method to estimate bounds on the distribution of effects of attending a Knowledge is Power Program (KIPP) charter school on student achievement, and find that a substantial majority of students' math achievement benefited from attendance, especially those who would have fared poorly in a traditional classroom. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we consider the case of finite time dimension in the panel stationarity tests with structural breaks. By fixing T, the finite sample properties of the tests for both micro ( T small and N large) and macro (both T and N large) panel data are generally greatly improved. More importantly, the derivation of the tests for finite T and , as opposed to joint asymptotic where N and simultaneously, avoids the imposition of the rate condition making the test valid for any ( T, N) blend. Four models corresponding to the usual combination of breaks are considered. The asymptotic distributions of the test are derived under the null hypothesis and are shown to be normally distributed. Their moments for T fixed are derived analytically employing Ghazal's corollary 1. The case with unknown breaks is also considered. The proposed tests have generally empirical sizes that are very close to the nominal size. The Monte Carlo simulations show that the power of the test statistics increases substantially with N and T. [ABSTRACT FROM AUTHOR]

In this article a semiparametric spatial Durbin model is employed to analyse the growth behaviour of 155 European regions in the period 1988–2000. This specification combines the semiparametric approach with the usual parametric spatial econometric technique to accommodate both spatial dependence and nonlinearities as suggested by recent neoclassical growth models with spatial technological interdependence. The results provide evidence of nonlinearities in the effect of initial per capita incomes and human capital investments. Moreover, the specification used allows identifying the effect of the interaction between the characteristics (initial conditions and structural variables) of each region and those of its neighbours. Finally, it shows some indication of global spillovers across-country and local spatial spillovers from domestic neighbours. Resumen En este artículo se emplea un modelo semiparamétrico espacial de Durbin para analizar el comportamiento del crecimiento de 155 regiones europeas durante el periodo 1988–2000. Esta especificación combina el enfoque semiparamétrico con técnicas econométricas espaciales paramétricas habituales para reconciliar la dependencia espacial y las no linealidades, tal y como sugieren los modelos de crecimiento neoclásicos recientes con interdependencia tecnológica espacial. Los resultados ofrecen pruebas de no linealidades en el efecto de ingresos per capita iniciales e inversiones en capital humano. Además, la especificación utilizada permite utilizar el efecto de la interacción entre las características (condiciones iniciales y variables estructurales) de cada región y las de sus vecinos. Finalmente, da una idea de spillovers (efectos de derrame) globales entre países y spillovers espaciales locales de regiones vecinas dentro del país. [ABSTRACT FROM AUTHOR]

We analyse the relationship between regional unemployment and labour productivity in Europe, basing our empirical analysis on the predictions of a Neary-type General Oligopolistic Equilibrium trade model with efficiency-wages. Using semiparametric and dynamic panel data estimators and controlling for other factors, we give evidence of a nonlinear relationship between productivity and regional unemployment in Europe: with a level of productivity smaller than a certain threshold, this relationship is negative, while no relation occurs in the case of higher productivity regions. This evidence proves an important role of a wage-floor (induced by efficiency wages and exacerbated by institutional factors) under which the productivity gap cannot be compensated by a wage gap. Resumen. Analizamos la relación entre el desempleo regional y la productividad laboral en Europa, tomando como base de nuestro análisis empírico las predicciones de un modelo de comercio general oligopolístico de equilibrio tipo Neary con salarios eficientes. Mediante el uso de estimadores semiparamétricos y datos de panel dinámicos y control de otros factores, proporcionamos evidencia de una relación no lineal entre la productividad y el desempleo regional en Europa: con un nivel de productividad por debajo de un cierto valor umbral, esta relación es negativa, mientras que no existe esta relación en el caso de regiones con productividad mas elevada. Esta evidencia prueba el papel importante de una base salarial (inducida por salarios eficientes y exacerbada por factores institucionales) por debajo de la cual la diferencia de productividad no puede ser compensada por una diferencia salarial. [ABSTRACT FROM AUTHOR]