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Discussion of “Transparency in Structural Research” by Isaiah Andrews, Matthew Gentzkow, and Jesse Shapiro Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2020-09-28 Stéphane Bonhomme
(2020). Discussion of “Transparency in Structural Research” by Isaiah Andrews, Matthew Gentzkow, and Jesse Shapiro. Journal of Business & Economic Statistics: Vol. 38, No. 4, pp. 723-725.
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Thoughts on “Transparency in Structural Research” Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2020-09-28 Christopher Taber
(2020). Thoughts on “Transparency in Structural Research”. Journal of Business & Economic Statistics: Vol. 38, No. 4, pp. 726-727.
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Discussion on “ Transparency in Structural Research” by I. Andrews, M. Gentkow and J. Shapiro Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2020-09-28 Elie Tamer
Abstract We provide a complementary approach to global sensitivity analysis that should be useful for empirical work in economics.
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Rejoinder Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2020-09-28 Isaiah Andrews, Matthew Gentzkow, Jesse M. Shapiro
(2020). Rejoinder. Journal of Business & Economic Statistics: Vol. 38, No. 4, pp. 731-731.
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Editorial Collaborators Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2020-09-28
(2020). Editorial Collaborators. Journal of Business & Economic Statistics: Vol. 38, No. 4, pp. 951-954.
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Transparency in Structural Research Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2020-08-25 Isaiah Andrews, Matthew Gentzkow, Jesse M. Shapiro
Abstract We propose a formal definition of transparency in empirical research and apply it to structural estimation in economics. We discuss how some existing practices can be understood as attempts to improve transparency, and we suggest ways to improve current practice, emphasizing approaches that impose a minimal computational burden on the researcher. We illustrate with examples.
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Inference in Approximately Sparse Correlated Random Effects Probit Models With Panel Data Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-12-20 Jeffrey M. Wooldridge, Ying Zhu
Abstract We propose a simple procedure based on an existing “debiased” l1-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number of cross-sectional observations. Our method is computationally simple and does not suffer from the incidental
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Debiased Inference of Average Partial Effects in Single-Index Models: Comment on Wooldridge and Zhu Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-12-20 David A. Hirshberg, Stefan Wager
(2020). Debiased Inference of Average Partial Effects in Single-Index Models: Comment on Wooldridge and Zhu. Journal of Business & Economic Statistics: Vol. 38, No. 1, pp. 19-24.
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Rejoinder Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-12-20 Jeffrey M. Wooldridge, Ying Zhu
(2020). Rejoinder. Journal of Business & Economic Statistics: Vol. 38, No. 1, pp. 25-26.
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Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care. Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2016-03-16 Amanda Kowalski
Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable
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Editorial Collaborators Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-10-07
(2019). Editorial Collaborators. Journal of Business & Economic Statistics: Vol. 37, No. 4, pp. 771-774.
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Forecast Error Variance Decompositions with Local Projections Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-07-18 Yuriy Gorodnichenko, Byoungchan Lee
Abstract We propose and study properties of an estimator of the forecast error variance decomposition in the local projections framework. We find for empirically relevant sample sizes that, after being bias-corrected with bootstrap, our estimator performs well in simulations. We also illustrate the workings of our estimator empirically for monetary policy and productivity shocks. KEYWORDS: Forecast
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Minimum Contrast Empirical Likelihood Inference of Discontinuity in Density* Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-07-05 Jun Ma, Hugo Jales, Zhengfei Yu
Abstract This article investigates the asymptotic properties of a simple empirical-likelihood-based inference method for discontinuity in density. The parameter of interest is a function of two one-sided limits of the probability density function at (possibly) two cut-off points. Our approach is based on the first-order conditions from a minimum contrast problem. We investigate both first-order and
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Bounds on Average and Quantile Treatment Effects on Duration Outcomes Under Censoring, Selection, and Noncompliance Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-26 German Blanco, Xuan Chen, Carlos A. Flores, Alfonso Flores-Lagunes
Abstract We consider the problem of assessing the effects of a treatment on duration outcomes using data from a randomized evaluation with noncompliance. For such settings, we derive nonparametric sharp bounds for average and quantile treatment effects addressing three pervasive problems simultaneously: self-selection into the spell of interest, endogenous censoring of the duration outcome, and noncompliance
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A Smooth Nonparametric, Multivariate, Mixed-Data Location-Scale Test Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-25 Jeffrey S. Racine, Ingrid Van Keilegom
Abstract A number of tests have been proposed for assessing the location-scale assumption that is often invoked by practitioners. Existing approaches include Kolmogorov–Smirnov and Cramer–von Mises statistics that each involve measures of divergence between unknown joint distribution functions and products of marginal distributions. In practice, the unknown distribution functions embedded in these
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Multivariate Stochastic Volatility Model With Realized Volatilities and Pairwise Realized Correlations Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-25 Yuta Yamauchi, Yasuhiro Omori
Abstract Although stochastic volatility and GARCH (generalized autoregressive conditional heteroscedasticity) models have successfully described the volatility dynamics of univariate asset returns, extending them to the multivariate models with dynamic correlations has been difficult due to several major problems. First, there are too many parameters to estimate if available data are only daily returns
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Bayesian Forecasting of Many Count-Valued Time Series Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-25 Lindsay R. Berry, Mike West
Abstract We develop and exemplify application of new classes of dynamic models for time series of nonnegative counts. Our novel univariate models combine dynamic generalized linear models for binary and conditionally Poisson time series, with dynamic random effects for over-dispersion. These models estimate dynamic regression coefficients in both binary and nonzero count components. Sequential Bayesian
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Matching Using Sufficient Dimension Reduction for Causal Inference Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-24 Wei Luo, Yeying Zhu
ABSTRACT To estimate causal treatment effects, we propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Compared with the original covariates and the propensity score, which are commonly used for matching in the literature, the reduced covariates are nonparametrically estimable and are effective in imputing the missing potential outcomes, under
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Nonparametric Estimation of Search Costs for Differentiated Products: Evidence from Medigap Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-03 Haizhen Lin, Matthijs R. Wildenbeest
Abstract This article develops a method to estimate search frictions as well as preference parameters in differentiated product markets. Search costs are nonparametrically identified, which means our method can be used to estimate search costs in differentiated product markets that lack a suitable search cost shifter. We apply our model to the U.S. Medigap insurance market. We find that search costs
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Treatment Effects With Heterogeneous Externalities Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-06-03 Tiziano Arduini, Eleonora Patacchini, Edoardo Rainone
Abstract This article proposes a new method for estimating heterogeneous externalities in policy analysis when social interactions take the linear-in-means form. We establish that the parameters of interest can be identified and consistently estimated using specific functions of the share of the eligible population. We also study the finite sample performance of the proposed estimators using Monte
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Comparing Possibly Misspecified Forecasts Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-31 Andrew J. Patton
Abstract Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters
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Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-31 Adam McCloskey
Abstract This article specializes the critical value (CV) methods that are based upon (refinements of) Bonferroni bounds, introduced by McCloskey to a problem of inference after consistent model selection in a general linear regression model. The post-selection problem is formulated to mimic common empirical practice and is applicable to both cross-sectional and time series contexts. We provide algorithms
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A Stochastic Volatility Model With Realized Measures for Option Pricing Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-31 Giacomo Bormetti, Roberto Casarin, Fulvio Corsi, Giulia Livieri
Abstract Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical
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Partial Identification of Economic Mobility: With an Application to the United States Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-28 Daniel L. Millimet, Hao Li, Punarjit Roychowdhury
Abstract The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing
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Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-28 Siddhartha Chib, Xiaming Zeng
(2020). Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework. Journal of Business & Economic Statistics: Vol. 38, No. 4, pp. 771-783.
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Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-22 Clifford Lam, Pedro C.L. Souza
Abstract Spatial econometric models allow for interactions among variables through the specification of a spatial weight matrix. Practitioners often face the risk of misspecification of such a matrix. In many problems a number of potential specifications exist, such as geographic distances, or various economic quantities among variables. We propose estimating the best linear combination of these specifications
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Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right” Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-08 Giuseppe De Luca, Jan R. Magnus, Franco Peracchi
Abstract– We establish a link between the approaches proposed by Oster (2019 Oster, E. (2019), “Unobservable Selection and Coefficient Stability: Theory and Evidence,” Journal of Business and Economic Statistics, 37(2). DOI: 10.1080/07350015.2016.1227711.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Pei, Pischke, and Schwandt (2019 Pei, Z., Pischke, J.-S., and Schwandt, H. (2019)
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The Pricing of Tail Risk and the Equity Premium: Evidence From International Option Markets Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-06 Torben G. Andersen, Nicola Fusari, Viktor Todorov
Abstract We explore the pricing of tail risk as manifest in index options across international equity markets. The risk premium associated with negative tail events displays persistent shifts, unrelated to volatility. This tail risk premium is a potent predictor of future returns for all the indices, while the option-implied volatility only forecasts the future return variation. Hence, compensation
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Local Parametric Estimation in High Frequency Data Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-06 Yoann Potiron, Per Mykland
Abstract We give a general time-varying parameter model, where the multidimensional parameter possibly includes jumps. The quantity of interest is defined as the integrated value over time of the parameter process Θ=T−1∫0Tθt*dt. We provide a local parametric estimator (LPE) of Θ and conditions under which we can show the central limit theorem. Roughly speaking those conditions correspond to some uniform
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Dynamic Vector Mode Regression Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-05-01 Gordon C. R. Kemp, Paulo M. D. C. Parente, J. M. C. Santos Silva
(2020). Dynamic Vector Mode Regression. Journal of Business & Economic Statistics: Vol. 38, No. 3, pp. 647-661.
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A New Class of Change Point Test Statistics of Rényi Type Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-04-30 Lajos Horváth, Curtis Miller, Gregory Rice
(2020). A New Class of Change Point Test Statistics of Rényi Type. Journal of Business & Economic Statistics: Vol. 38, No. 3, pp. 570-579.
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Term Structures of Inflation Expectations and Real Interest Rates Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-04-02 S. Borağan Aruoba
Abstract I use a statistical model to combine various surveys to produce a term structure of inflation expectations—inflation expectations at any horizon—and an associated term structure of real interest rates. Inflation expectations extracted from this model track realized inflation quite well, and in terms of forecast accuracy, they are at par with or superior to some popular alternatives. The real
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External Validity in Fuzzy Regression Discontinuity Designs Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-04-02 Marinho Bertanha, Guido W. Imbens
Abstract Fuzzy regression discontinuity designs identify the local average treatment effect (LATE) for the subpopulation of compliers, and with forcing variable equal to the threshold. We develop methods that assess the external validity of LATE to other compliance groups at the threshold, and allow for identification away from the threshold. Specifically, we focus on the equality of outcome distributions
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The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-04-02 Ariella Kahn-Lang, Kevin Lang
Abstract We use the exchange between Kearney/Levine and Jaeger/Joyce/Kaestner on 16 and Pregnant to reexamine the use of DiD as a response to the failure of nature to properly design an experiment for us. We argue that (1) any DiD paper should address why the original levels of the experimental and control groups differed, and why this would not impact trends, (2) the parallel trends argument requires
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Heterogeneity in Expectations, Risk Tolerance, and Household Stock Shares: The Attenuation Puzzle Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-04-02 John Ameriks, Gábor Kézdi, Minjoon Lee, Matthew D. Shapiro
Abstract This article jointly estimates the relationship between stock share and expectations and risk preferences. The survey allows individual-level, quantitative estimates of risk tolerance and of the perceived mean, and variance of stock returns. These estimates have economically and statistically significant association for the distribution of stock shares with relative magnitudes in proportion
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Heterogeneity and Unemployment Dynamics Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-27 Hie Joo Ahn, James D. Hamilton
Abstract Many previous articles have studied the contribution of inflows and outflows to the cyclical variation in unemployment, but ignored the critical role of unobserved heterogeneity across workers. This article develops new estimates of unemployment inflows and outflows that allow for unobserved heterogeneity as well as direct effects of unemployment duration on unemployment-exit probabilities
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A Smooth Transition Finite Mixture Model for Accommodating Unobserved Heterogeneity Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-27 Eelco Kappe, Wayne S. DeSarbo, Marcelo C. Medeiros
Abstract While the smooth transition (ST) model has become popular in business and economics, the treatment of unobserved heterogeneity within these models has received limited attention. We propose a ST finite mixture (STFM) model which simultaneously estimates the presence of time-varying effects and unobserved heterogeneity in a panel data context. Our objective is to accurately recover the heterogeneous
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Empirical likelihood for high frequency data Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-27 Lorenzo Camponovo, Yukitoshi Matsushita, Taisuke Otsu
Abstract This paper introduces empirical likelihood methods for interval estimation and hypothesis testing on volatility measures in some high frequency data environments. We propose a modified empirical likelihood statistic that is asymptotically pivotal under infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. The proposed statistic
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Real-Time Macroeconomic Forecasting With a Heteroscedastic Inversion Copula Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-11 Rubén Loaiza-Maya, Michael Stanley Smith
Abstract There is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables. In multivariate time series, this can be achieved with a copula model, where both serial and cross-sectional dependence is captured by a copula function, and the margins are nonparametric. Yet most existing copulas cannot capture heteroscedasticity well, which is a feature of many economic
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Time Series Seasonal Adjustment Using Regularized Singular Value Decomposition Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-11 Wei Lin, Jianhua Z. Huang, Tucker McElroy
(2020). Time Series Seasonal Adjustment Using Regularized Singular Value Decomposition. Journal of Business & Economic Statistics: Vol. 38, No. 3, pp. 487-501.
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Implications of Return Predictability for Consumption Dynamics and Asset Pricing Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-11 Carlo A. Favero, Fulvio Ortu, Andrea Tamoni, Haoxi Yang
Abstract Two broad classes of consumption dynamics—long-run risks and rare disasters—have proven successful in explaining the equity premium puzzle when used in conjunction with recursive preferences. We show that bounds a-là Gallant, Hansen, and Tauchen that restrict the volatility of the stochastic discount factor by conditioning on a set of return predictors constitute a useful tool to discriminate
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A Comparison of Two Quantile Models With Endogeneity Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-08 Kaspar Wüthrich
Abstract This article studies the relationship between the two most-used quantile models with endogeneity: the instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen 2005 Chernozhukov, V., and Hansen, C. (2005), “An IV Model of Quantile Treatment Effects,” Econometrica, 73, 245–261.[Crossref], [Web of Science ®] , [Google Scholar]) and the local quantile treatment effects (LQTE)
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Somewhere Between Utopia and Dystopia: Choosing From Multiple Incomparable Prospects Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-02-08 Gordon Anderson, Thierry Post, Yoon-Jae Whang
Abstract In many fields of decision making, choices have to be made from multiple alternatives, but stochastic dominance rules do not yield a complete ordering due to incomparability of some or all of the prospects. For ranking incomparable prospects, a “Utopia Index” measuring the proximity to a lower envelope of integrated distribution functions is proposed. Economic interpretations in terms of Expected
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Stationary Points for Parametric Stochastic Frontier Models Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2019-01-29 William C. Horrace, Ian A. Wright
Abstract Stationary point results on the normal–half-normal stochastic frontier model are generalized using the theory of the Dirac delta, and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and “wrong skew” results are derived or simulated for common parametric
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Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-15 Dean R. Hyslop, Wilbur Townsend
ABSTRACT This article analyzes earnings dynamics and measurement error using a matched longitudinal sample of individuals’ survey and administrative earnings. In line with previous literature, the reported differences are characterized by both persistent and transitory factors. Estimating a model consistent with past results, survey errors are mean-reverting when administrative reports are assumed
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A New Approach to Identifying the Real Effects of Uncertainty Shocks Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-09 Minchul Shin, Molin Zhong
ABSTRACT This article introduces the use of the sign restrictions methodology to identify uncertainty shocks. We apply our methodology to a class of vector autoregression models with stochastic volatility that allow volatility fluctuations to impact the conditional mean. We combine sign restrictions on the conditional mean and conditional second moment impulse responses to identify financial and macro
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Detecting Structural Differences in Tail Dependence of Financial Time Series Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-09 Carsten Bormann, Melanie Schienle
ABSTRACT An accurate assessment of tail inequalities and tail asymmetries of financial returns is key for risk management and portfolio allocation. We propose a new test procedure for detecting the full extent of such structural differences in the dependence of bivariate extreme returns. We decompose the testing problem into piecewise multiple comparisons of Cramér–von Mises distances of tail copulas
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The Role of Jumps in Volatility Spillovers in Foreign Exchange Markets: Meteor Shower and Heat Waves Revisited Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-09 Jérôme Lahaye, Christopher Neely
ABSTRACT This article extends the literature on geographic (heat waves) and intertemporal (meteor showers) foreign exchange volatility transmission to characterize the role of jumps and cross-rate propagation. We employ multivariate heterogenous autoregressive (HAR) models to capture the quasi-long memory properties of volatility and both Shapley–Owen R2’s and portfolio optimization exercises to quantify
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Double-Question Survey Measures for the Analysis of Financial Bubbles and Crashes Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-09 M. Hashem Pesaran, Ida Johnsson
ABSTRACT This article proposes a new double-question survey whereby an individual is presented with two sets of questions; one on beliefs about current asset values and another on price expectations. A theoretical asset pricing model with heterogeneous agents is advanced and the existence of a negative relationship between price expectations and asset valuations is established, and is then tested using
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Extreme Quantile Estimation for Autoregressive Models Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-05 Deyuan Li, Huixia Judy Wang
ABSTRACT A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop
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A Stochastic Frontier Model with Endogenous Treatment Status and Mediator Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-05 Yi-Ting Chen, Yu-Chin Hsu, Hung-Jen Wang
Government policies are frequently used to promote productivity. Some policies are designed to enhance production technology, while others are meant to improve production efficiency. An important issue to consider when designing and evaluating policies is whether a mediator is required or effective in achieving the desired final outcome. To better understand and evaluate the policies, we propose a
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Is a Normal Copula the Right Copula? Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-05 Dante Amengual, Enrique Sentana
ABSTRACT We derive computationally simple and intuitive expressions for score tests of Gaussian copulas against generalized hyperbolic alternatives, including symmetric and asymmetric Student t, and many other examples. We decompose our tests into third and fourth moment components, and obtain one-sided Likelihood Ratio analogs, whose standard asymptotic distribution we provide. Our Monte Carlo exercises
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Words are the New Numbers: A Newsy Coincident Index of the Business Cycle Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-05 Leif Anders Thorsrud
(2020). Words are the New Numbers: A Newsy Coincident Index of the Business Cycle. Journal of Business & Economic Statistics: Vol. 38, No. 2, pp. 393-409.
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HAR Inference: Recommendations for Practice Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-02 Eben Lazarus, Daniel J. Lewis, James H. Stock, Mark W. Watson
ABSTRACT The classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of work on how to improve heteroscedasticity- and autocorrelation-robust (HAR) inference in time series regression. This literature finds that using a larger-than-usual truncation parameter to estimate the long-run variance, combined with Kiefer-Vogelsang (2002, 2005) fixed-b critical values, can substantially
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Discussion of Lazarus, Lewis, Stock, and Watson, “HAR Inference: Recommendations for Practice” Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-02 Kenneth D. West
(2018). Discussion of Lazarus, Lewis, Stock, and Watson, “HAR Inference: Recommendations for Practice”. Journal of Business & Economic Statistics: Vol. 36, No. 4, pp. 560-562.
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Comment on "HAR Inference: Recommendations for Practice" by E. Lazarus, D. J. Lewis, J. H. Stock and M. W. Watson Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-02 Ulrich K. Müller
(2018). Comment on 'HAR Inference: Recommendations for Practice' by E. Lazarus, D. J. Lewis, J. H. Stock and M. W. Watson. Journal of Business & Economic Statistics: Vol. 36, No. 4, pp. 563-564.
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Comment on "HAR Inference: Recommendations for Practice" Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-02 Timothy J. Vogelsang
(2018). Comment on 'HAR Inference: Recommendations for Practice' Journal of Business & Economic Statistics: Vol. 36, No. 4, pp. 569-573.
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HAR Inference: Recommendations for Practice Rejoinder Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-11-02 Eben Lazarus, Daniel J. Lewis, James H. Stock, Mark W. Watson
(2018). HAR Inference: Recommendations for Practice Rejoinder. Journal of Business & Economic Statistics: Vol. 36, No. 4, pp. 574-575.
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Two-Step Estimation of Incomplete Information Social Interaction Models With Sample Selection Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-10-29 Tadao Hoshino
ABSTRACT This article considers linear social interaction models under incomplete information that allow for missing outcome data due to sample selection. For model estimation, assuming that each individual forms his/her belief about the other members’ outcomes based on rational expectations, we propose a two-step series nonlinear least squares estimator. Both the consistency and asymptotic normality
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Estimating and Testing Nonlinear Local Dependence Between Two Time Series Journal of Business & Economic Statistics (IF 3.0) Pub Date : 2018-10-29 Virginia Lacal, Dag Tjøstheim
ABSTRACT The most common measure of dependence between two time series is the cross-correlation function. This measure gives a complete characterization of dependence for two linear and jointly Gaussian time series, but it often fails for nonlinear and non-Gaussian time series models, such as the ARCH-type models used in finance. The cross-correlation function is a global measure of dependence. In