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on Econometrics |
| By: | Badi Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Chihwa Kao (Department of Economics, University of Connecticut); Long Liu (Department of Economics, College of Business, University of Texas at San Antonio) |
| Abstract: | This paper studies testing of shifts in a time trend panel data model with serially correlated error component disturbances, without any prior knowledge of whether the error term is sta- tionary or nonstationary. This is done in case the shift is known as well as unknown. Following Vogelsang (1997) in the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator derived in Baltagi, et al. (2014). The proposed test has a Chi-square limiting distribution and is valid for both J(O) and J(l) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations. |
| Keywords: | Non-Stationary Panels, Time Trends, Serial Correlation, Wald Type Tests |
| JEL: | C23 C3 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:213&r=all |
| By: | Xiaohong Chen; Demian Pouzo; James L. Powell |
| Abstract: | This paper considers estimation and inference for a weighted average derivative (WAD) of a nonparametric quantile instrumental variables regression (NPQIV). NPQIV is a non-separable and nonlinear ill-posed inverse problem, which might be why there is no published work on the asymptotic properties of any estimator of its WAD. We first characterize the semiparametric efficiency bound for a WAD of a NPQIV, which, unfortunately, depends on an unknown conditional derivative operator and hence an unknown degree of ill-posedness, making it difficult to know if the information bound is singular or not. In either case, we propose a penalized sieve generalized empirical likelihood (GEL) estimation and inference procedure, which is based on the unconditional WAD moment restriction and an increasing number of unconditional moments that are implied by the conditional NPQIV restriction, where the unknown quantile function is approximated by a penalized sieve. Under some regularity conditions, we show that the self-normalized penalized sieve GEL estimator of the WAD of a NPQIV is asymptotically standard normal. We also show that the quasi likelihood ratio statistic based on the penalized sieve GEL criterion is asymptotically chi-square distributed regardless of whether or not the information bound is singular. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.10100&r=all |
| By: | Xu, Yuhong (School of Economics, Singapore Management University); Yang, Zhenlin (School of Economics, Singapore Management University) |
| Abstract: | We propose score type tests for testing the existence of temporal heterogeneity in slope and spatial parameters in spatial panel data (SPD) models, allowing for the presence of individual-specific and/or time-specific fixed effects (or in general intercept heterogeneity). The SPD model with spatial lag effect is treated in detail by first considering the model with individual-specific effects only, and then extending it to the model with both individual and time specific effects. Two types of tests (naive and robust) are proposed, and their asymptotic properties are presented. These tests are then fully extended to an SPD model with both spatial lag and spatial error effects. Monte Carlo results show that the robust tests have much superior finite and large sample properties than the naive tests. Thus, the proposed robust tests provide reliable tools for identifying possible existence of temporal heterogeneity in regression and spatial coefficients. Empirical illustrations of the proposed tests are given. |
| Keywords: | Spatial panels; Fixed effects; Time-Varying Covariate Effects; Time-Varying Spatial Effects; Change Points |
| JEL: | C10 C13 C15 C21 C23 |
| Date: | 2019–01–28 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2019_005&r=all |
| By: | Marco Avarucci; Paolo Zaffaroni |
| Abstract: | This paper studies estimation of linear panel regression models with heterogeneous coefficients, when both the regressors and the residual contain a possibly common, latent, factor structure. Our theory is (nearly) efficient, because based on the GLS principle, and also robust to the specification of such factor structure because it does not require any information on the number of factors nor estimation of the factor structure itself. We first show how the unfeasible GLS estimator not only affords an efficiency improvement but, more importantly, provides a bias-adjusted estimator with the conventional limiting distribution, for situations where the OLS is affected by a first-order bias. The technical challenge resolved in the paper is to show how these properties are preserved for a class of feasible GLS estimators in a double-asymptotics setting. Our theory is illustrated by means of Monte Carlo exercises and, then, with an empirical application using individual asset returns and firms' characteristics data. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.11181&r=all |
| By: | Jiun-Hua Su |
| Abstract: | We propose a counterfactual Kaplan-Meier estimator that incorporates exogenous covariates and unobserved heterogeneity of unrestricted dimensionality in duration models with random censoring. Under some regularity conditions, we establish the joint weak convergence of the proposed counterfactual estimator and the unconditional Kaplan-Meier (1958) estimator. Applying the functional delta method, we make inference on the cumulative hazard policy effect, that is, the change of duration dependence in response to a counterfactual policy. We also evaluate the finite sample performance of the proposed counterfactual estimation method in a Monte Carlo study. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.08502&r=all |
| By: | Alain Hecq; Luca Margaritella; Stephan Smeekes |
| Abstract: | In this paper we develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test which retains the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out the effects of the variables not of interest. We conduct an extensive set of Monte-Carlo simulations to compare different ways to set up the test procedure and choose the tuning parameter. The test performs well under different data generating processes, even when the underlying model is not very sparse. Additionally, we investigate two empirical applications: the money-income causality relation using a large macroeconomic dataset and networks of realized volatilities of a set of 49 stocks. In both applications we find evidences that the causal relationship becomes much clearer if a high-dimensional VAR is considered compared to a standard low-dimensional one. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.10991&r=all |
| By: | Chiara Lattanzi; Manuele Leonelli |
| Abstract: | Inference over tails is usually performed by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. However, the choice of such threshold is critical and can affect the inferential results. Extreme value mixture models have been defined to estimate the threshold using the full dataset and to give accurate tail estimates. Such models assume that the tail behavior is constant for all observations. However, the extreme behavior of financial returns often changes considerably in time and such changes occur by sudden shocks of the market. Here we extend the extreme value mixture model class to formally take into account distributional extreme changepoints, by allowing for the presence of regime-dependent parameters modelling the tail of the distribution. This extension formally uses the full dataset to both estimate the thresholds and the extreme changepoint locations, giving uncertainty measures for both quantities. Estimation of functions of interest in extreme value analyses is performed via MCMC algorithms. Our approach is evaluated through a series of simulations, applied to real data sets and assessed against competing approaches. Evidence demonstrates that the inclusion of different extreme regimes outperforms both static and dynamic competing approaches in financial applications. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.09205&r=all |
| By: | Kelly Vosters; Jørgen Modalsli (Statistics Norway) |
| Abstract: | Intergenerational persistence estimates are susceptible to several well-documented biases arising from income measurement, and it has become standard practice to construct income measures to mitigate these. However, remaining bias can lead to a spurious grandparent coefficient estimate in multigenerational regressions, a recent focus of the mobility literature. We show with theory and simulations that even using a 30-year income average can result in a small positive spurious grandfather coefficient estimate. We further propose an IV approach, showing that it is not susceptible to this spillover bias in simplified settings and that it can provide bounds on the parameters in a more general scenario. With administrative data from Norway, we reveal a positive spillover bias in the grandfather coefficient estimates, and the combined evidence from our OLS and IV approaches suggest the preferred small positive OLS estimate could still be upward biased. |
| Keywords: | Multigenerational mobility; income mobility; measurement error; spillover bias |
| JEL: | J62 C30 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:ssb:dispap:897&r=all |
| By: | Byunghoon Kang |
| Abstract: | This paper considers the instrument selection problem in instrumental variable (IV) regression model when there is a large set of instruments with potential invalidity. I derive higher-order mean square error (MSE) approximation of two-stage least squares (2SLS), limited information maximum likelihood (LIML), Fuller (FULL) and bias-adjusted 2SLS (B2SLS) estimators with allowing for local violation of the instrument-exogeneity conditions. Based on the approximation to the higher-order MSE, I propose instrument selection criteria that are robust to potential invalidity of instruments. Furthermore, I also show the optimality results of instrument selection criteria in Donald and Newey (2001, Econometrica) under faster than N^(-1/2) locally invalid instruments specication. |
| Keywords: | Instrument selection, Invalid instruments, Many instruments, 2SLS, LIML, Fuller estimator, Bias-adjusted 2SLS |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:257105320&r=all |
| By: | Keisuke Takahata; Takahiro Hoshino |
| Abstract: | We propose a semiparametric two-stage least square estimator for the heterogeneous treatment effects (HTE). HTE is the solution to certain integral equation which belongs to the class of Fredholm integral equations of the first kind, which is known to be ill-posed problem. Naive semi/nonparametric methods do not provide stable solution to such problems. Then we propose to approximate the function of interest by orthogonal series under the constraint which makes the inverse mapping of integral to be continuous and eliminates the ill-posedness. We illustrate the performance of the proposed estimator through simulation experiments. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.09978&r=all |
| By: | Matias D. Cattaneo; Richard K. Crump; Max H. Farrell; Yingjie Feng |
| Abstract: | Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: we give an array of theoretical and practical results that aid both in understanding current practices (i.e., their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial, covariate-adjusted and smoothness-restricted extensions thereof. In particular, we highlight important methodological problems related to covariate adjustment methods used in current practice. We also discuss extensions to clustered data. Our results are illustrated with simulated and real data throughout. Companion general-purpose software packages for \texttt{Stata} and \texttt{R} are provided. Finally, from a technical perspective, new theoretical results for partitioning-based series estimation are obtained that may be of independent interest. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.09608&r=all |
| By: | Jushan Bai; Junlong Feng |
| Abstract: | We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop a new theoretical argument to establish consistency without assuming sparsity or the existence of any moments of the error matrix, so that fat-tailed continuous random errors such as Cauchy are allowed. The results are illustrated by simulations. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.08735&r=all |
| By: | Myung Hwan Seo; Sueyoul Kim; Young-Joo Kim |
| Abstract: | We develop a Stata command xthenreg to implement the first-differenced GMM estimation of the dynamic panel threshold model, which Seo and Shin (2016, Journal of Econometrics 195: 169-186) have proposed. Furthermore, We derive the asymptotic variance formula for a kink constrained GMM estimator of the dynamic threshold model and include an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. The use of the command is illustrated through a Monte Carlo simulation and an economic application. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.10318&r=all |
| By: | Arun G. Chandrasekhar; Victor Chernozhukov; Francesca Molinari; Paul Schrimpf |
| Abstract: | This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to carry an index, and to be unknown but parametrically or non-parametrically estimable functions. The identification region of the parameters of the best linear approximation is characterized via its support function, and limit theory is developed for the latter. We prove that the support function can be approximated by a Gaussian process and establish validity of the Bayesian bootstrap for inference. Because the bounds may carry an index, the approach covers many canonical examples in the partial identification literature arising in the presence of interval valued outcome and/or regressor data: not only mean regression, but also quantile and distribution regression, including sample selection problems, as well as mean, quantile, and distribution treatment effects. In addition, the framework can account for the availability of instruments. An application is carried out, studying female labor force participation using data from Mulligan and Rubinstein (2008) and insights from Blundell, Gosling, Ichimura, and Meghir (2007). Our results yield robust evidence of a gender wage gap, both in the 1970s and 1990s, at quantiles of the wage distribution up to the 0.4, while allowing for completely unrestricted selection into the labor force. Under the assumption that the median wage offer of the employed is larger than that of individuals that do not work, the evidence of a gender wage gap extends to quantiles up to the 0.7. When the assumption is further strengthened to require stochastic dominance, the evidence of a gender wage gap extends to all quantiles, and there is some evidence at the 0.8 and higher quantiles that the gender wage gap decreased between the 1970s and 1990s. |
| JEL: | C13 C31 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25593&r=all |
| By: | Tomasz R. Bielecki; Igor Cialenco; Marcin Pitera; Thorsten Schmidt |
| Abstract: | In this paper we develop a novel methodology for estimation of risk capital allocation. The methodology is rooted in the theory of risk measures. We work within a general, but tractable class of law-invariant coherent risk measures, with a particular focus on expected shortfall. We introduce the concept of fair capital allocations and provide explicit formulae for fair capital allocations in case when the constituents of the risky portfolio are jointly normally distributed. The main focus of the paper is on the problem of approximating fair portfolio allocations in the case of not fully known law of the portfolio constituents. We define and study the concepts of fair allocation estimators and asymptotically fair allocation estimators. A substantial part of our study is devoted to the problem of estimating fair risk allocations for expected shortfall. We study this problem under normality as well as in a nonparametric setup. We derive several estimators, and prove their fairness and/or asymptotic fairness. Last, but not least, we propose two backtesting methodologies that are oriented at assessing the performance of the allocation estimation procedure. The paper closes with a substantial numerical study of the subject. |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1902.10044&r=all |
| By: | Mohamed Chikhi; Claude Diebolt |
| Abstract: | This paper analyzes the cyclical behavior of CAC 40 by testing the existence of nonlinearity through a logistic smooth transition AR model with logistic smooth transition GARCH errors. We study the daily returns of CAC 40 from 1990 to 2018. We estimate several models using nonparametric maximum likelihood, where the innovation distribution is replaced by a nonparametric estimate for the density function. We find that the rate of transition and the threshold value in both the conditional mean and conditional variance are highly significant. The forecasting results show that the informational shocks have transitory effects on returns and volatility and confirm nonlinearity. |
| Keywords: | LSTAR model, LSTGARCH model, nonparametric maximum likelihood, nonlinearity, informational shocks, time series analysis. |
| JEL: | C14 C22 C58 G17 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ulp:sbbeta:2019-06&r=all |
| By: | Florens, Jean-Pierre; Simar, Léopold; Van Keilegom, Ingrid |
| Keywords: | Characteristic function; cumulant function; flexible parametric family; frontier estimation; Laguerre polynomials |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:33355&r=all |
| By: | Enache, Andrea; Florens, Jean-Pierre |
| Abstract: | The first novelty of this paper is that we show global identification of the private values distribution in a sealed-bid third-price auction model using a fully nonparametric methodology. The second novelty of the paper comes from the study of the identification and estimation of the model using a quantile approach. We consider an i.i.d. private values environment with risk-averse bidders. In the first place, we consider the case where the risk-aversion parameter is known. We show that the speed of convergence in process of our nonparametric estimator produces at the root-n parametric rate and we explain the intuition behind this apparently surprising result. Next, we consider that the risk-aversion parameter is unknown and we locally identify it using exogenous variation in the number of participants. We extend our procedure to the case where we observe only the bids corresponding to the transaction prices, and we generalize the model so as to account for the presence of exogenous variables. The methodological toolbox used to analyse identification of the third-price auction model can be employed in the study of other games of incomplete information. Our results are interesting also from a policy perspective,as some authors recommend the use of the third-price auction format for certain Internet auctions. Moreover, we contribute to the econometric literature on auctions using a quantile approach. |
| Keywords: | structural nonparametric estimation; nonlinear inverse problems; global identification; functional convergence of estimators; third-price auction mode |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:26557&r=all |
| By: | Anne Opschoor; André Lucas; Istvan Barra; Dick van Dijk |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20190013&r=all |