nep-ecm New Economics Papers
on Econometrics
Issue of 2021‒11‒15
fifteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Bootstrap inference for panel data quantile regression By Antonio F. Galvao; Thomas Parker; Zhijie Xiao
  2. An online sequential test for qualitative treatment effects By Shi, Chengchun; Luo, Shikai; Zhu, Hongtu; Song, Rui
  3. Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption By Xuexin WANG
  4. Analytical Finite Sample Econometrics-from A.L.Nagar to Now By Yong Bao; Aman Ullah
  5. Inference on the maximal rank of time-varying covariance matrices using high-frequency data By Reiß, Markus; Winkelmann, Lars
  6. Tests for jumps in yield spreads By Winkelmann, Lars; Yao, Wenying
  7. Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses By Xuexin WANG
  8. Multiplicative Component GARCH Model of Intraday Volatility By Xiufeng Yan
  9. Two-stage multilevel latent class analysis with covariates in the presence of direct effects By Bakk, Zsuzsa; Di Mari, Roberto; Oser, Jennifer; Kuha, Jouni
  10. Autoregressive conditional duration modelling of high frequency data By Xiufeng Yan
  11. Improved regression inference using a second overlapping regression model By Peng, Liang; Einmahl, John
  12. Using the Statistical Concept of "Severity" to Assess Seemingly Contradictory Statistical Evidence (with a Particular Application to Damage Estimation) By Bönisch, Peter; Inderst, Roman
  13. A Causality-based Graphical Test to obtain an Optimal Blocking Set for Randomized Experiments By Abhishek K. Umrawal
  14. Measuring welfare, inequality and poverty with ordinal variables By Silber, Jacques; Yalonetzky, Gaston
  15. Correlation Estimation in Hybrid Systems By Baron Law

  1. By: Antonio F. Galvao; Thomas Parker; Zhijie Xiao
    Abstract: This paper develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.
    Date: 2021–11
  2. By: Shi, Chengchun; Luo, Shikai; Zhu, Hongtu; Song, Rui
    Abstract: Tech companies (e.g., Google or Facebook) often use randomized online experiments and/or A/B testing primarily based on the average treatment effects to compare their new product with an old one. However, it is also critically important to detect qualitative treatment effects such that the new one may significantly outperform the existing one only under some specific circumstances. The aim of this paper is to develop a powerful testing procedure to efficiently detect such qualitative treatment effects. We propose a scalable online updating algorithm to implement our test procedure. It has three novelties including adaptive randomization, sequential monitoring, and online updating with guaranteed type-I error control. We also thoroughly examine the theoretical properties of our testing procedure including the limiting distribution of test statistics and the justification of an efficient bootstrap method. Extensive empirical studies are conducted to examine the finite sample performance of our test procedure.
    JEL: C1
    Date: 2021–10–27
  3. By: Xuexin WANG (Xiamen University)
    Abstract: This study proposes new instrumental variable (IV) estimators for linear models by exploiting a continuum of instruments effectively. The effectiveness is attributed to the unique weighting function employed in the minimum distance objective functions, which enjoys attractive properties in relation to estimation efficiency. The proposed estimators enjoy analytical formulas, which are easily computable. The inferences drawn for these estimators are also straightforward, since their variance estimators for parameter inferences are of analytical forms. The proposed estimators are robust to weak instruments and heteroskedasticity of unknown form. Further, they are robust to high dimensionality of included and excluded exogenous variables. This approach conveniently overcomes the deficiency of conventional IV estimators in the literature on many weak instruments, where the theoretical properties of these estimators depend crucially on the interplay among an increasing number of instruments, unknown degrees of weak identification, and unknown reduced forms. Comprehensive Monte Carlo simulations reveal that the proposed estimators have excellent finite sample properties, outperforming the alternative estimators in a wide range of cases. The new estimation procedure is applied to estimate the elasticity of intertemporal substitution (EIS) in consumption, which is of central importance in macroeconomics and finance. Using the US quarterly data from the fourth quarter of 1955 to the first quarter of 2018, the estimates of EIS of our approach well exceed one and are statistically different from zero. These estimates are robust to model transformation, different sets of IVs, different data structures and data ranges.
    Keywords: Endogeneity; Heteroskedasticity of unknown form; Jackknife; Weak identification; EIS in consumption
    JEL: C12 C13 C23
    Date: 2021–11–06
  4. By: Yong Bao (Purdue University); Aman Ullah (Department of Economics, University of California Riverside)
    Abstract: Professor A.L. Nagar was a world-renowned econometrician and an international authority on finite sample econometrics with many path-breaking papers on the statistical properties of econometric estimators and test statistics. His contributions to applied econometrics have been also widely recognized. Nagar's 1959 Econometrica paper on the so-called k-class estimators, together with a later one in 1962 on the double-k-class estimators, provided a very general framework of bias and mean squared error approximations for a large class of estimators and had motivated researchers to study a wide variety of issues such as many and weak instruments for many decades to follow. This paper reviews Nagar's seminal contributions to analytical finite sample econometrics by providing historical backgrounds, discussing extensions and generalization of Nagar's approach, and suggesting future directions of this literature.
    Keywords: Nagar, finite sample econometrics, k-class estimators
    JEL: C10 C13 C18
    Date: 2021–08
  5. By: Reiß, Markus; Winkelmann, Lars
    Abstract: We study the rank of the instantaneous or spot covariance matrix ΣX(t) of a multidimensional continuous semi-martingale X(t). Given highfrequency observations X(i/n), i = 0,...,n, we test the null hypothesis rank (ΣX(t))
    Keywords: empirical covariance matrix,rank detection,signal detection rate,matrix concentration,eigenvalue perturbation,principal component analysis,factor model,term structure
    Date: 2021
  6. By: Winkelmann, Lars; Yao, Wenying
    Abstract: This paper develops high-frequency econometric methods to test for jumps in the spread of bond yields. We derive a coherent inference procedure that detects a jump in the yield spread only if at least one of the two underlying bonds displays a jump. We formalize the test as a sequential procedure in the context of an intersection union test in multiple testing and introduce a new bivariate jump test for pre-averaged intra-day returns. In an empirical application involving high-frequency data of U.S. government bonds, we contrast response patterns of term spreads and break-even in ation across monetary policy announcements, in ation, and employment news releases.
    Keywords: High-frequency data,sequential testing,news announcements,term spread,break-even inflation
    JEL: C58 C12 E43 E44
    Date: 2021
  7. By: Xuexin WANG (Xiamen University)
    Abstract: This study proposes new generalized spectral tests for multivariate Martingale Difference Hypotheses, especially suitable for high-dimensionality situations. The new tests are based on the martingale difference divergence covariance (MDD) proposed by Shao and Zhang (2014). It considers block-wise serial dependence of all lags, therefore, is consistent against general block-wise nonparametric Pitman’s local alternatives at the parametric rate n−1/2, where n is the sample size, and free of a user-chosen parameter. In order to cope with the highdimensionality in the sense that the dimension of time series is comparable to or even greater than the sample size, it is pivotal to employ a bias-reduced estimator for each individual MDD in the test statistic. Monte Carlo simulations reveal that the bias-reduced statistic generally performs better than its competitors substantially. Moreover, it is robust to heteroskedasticity of unknown forms and heavy-tails in the data generating processes. We apply our approach to test the efficient market hypothesis on the US stock market, using data sets on the monthly and daily data of portfolios sorted by industry. Our test results provide strong evidence against the efficient market hypothesis with respect to the US stock market at monthly frequency
    Keywords: Efficient Market Hypothesis; Generalized Spectral Tests; Nonintegrable Weighting Function; High-dimensionality; Bias Reduction
    JEL: C12 C22
    Date: 2021–11–06
  8. By: Xiufeng Yan
    Abstract: This paper proposes a multiplicative component intraday volatility model. The intraday conditional volatility is expressed as the product of intraday periodic component, intraday stochastic volatility component and daily conditional volatility component. I extend the multiplicative component intraday volatility model of Engle (2012) and Andersen and Bollerslev (1998) by incorporating the durations between consecutive transactions. The model can be applied to both regularly and irregularly spaced returns. I also provide a nonparametric estimation technique of the intraday volatility periodicity. The empirical results suggest the model can successfully capture the interdependency of intraday returns.
    Date: 2021–11
  9. By: Bakk, Zsuzsa; Di Mari, Roberto; Oser, Jennifer; Kuha, Jouni
    Abstract: In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed two-stage and the classical one-stage approach for models where a direct effect exists between indicators of the LC model and covariate, and the direct effect is ignored.
    Keywords: covariates; direct effect; multilevel latent class analysis; two-stage estimation
    JEL: C1
    Date: 2021–10–20
  10. By: Xiufeng Yan
    Abstract: This paper explores the duration dynamics modelling under the Autoregressive Conditional Durations (ACD) framework (Engle and Russell 1998). I test different distributions assumptions for the durations. The empirical results suggest unconditional durations approach the Gamma distributions. Moreover, compared with exponential distributions and Weibull distributions, the ACD model with Gamma distributed innovations provide the best fit of SPY durations.
    Date: 2021–11
  11. By: Peng, Liang; Einmahl, John (Tilburg University, School of Economics and Management)
    Date: 2021
  12. By: Bönisch, Peter; Inderst, Roman
    Abstract: When parties present divergent econometric evidence, the court may view such evidence as contradictory and thus ignore it completely, without conducting closer analysis. We develop a simple method for distinguishing between actual and merely apparent contradiction based on the statistical concept of the "severity" of the furnished evidence. Again using "severity", we also propose a method for reconciling divergent findings in instances of mere seeming contradiction. Our chosen application is that of damage estimation in follow-on cases.
    Keywords: Cartel damages,severity,statistical testing
    Date: 2020
  13. By: Abhishek K. Umrawal
    Abstract: Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material is not homogeneous. We formalize the problem of obtaining a statistically optimal set of covariates to be used to create blocks while performing a randomized experiment. We provide a graphical test to obtain such a set for a general semi-Markovian causal model. We also propose and provide ideas towards solving a more general problem of obtaining an optimal blocking set that considers both the statistical and economic costs of blocking.
    Date: 2021–11
  14. By: Silber, Jacques; Yalonetzky, Gaston
    Abstract: The key challenge in making distributional comparisons with ordinal data is the lack of commensurability of the distances between the ordered categories. This chapter provides a critical review of the most recent theoretical developments addressing this challenge and providing methods for ethical poverty, welfare, and inequality comparisons with univariate ordered multinomial distributions.
    Keywords: inequality,ordinal variables,partial ordering,poverty,welfare
    JEL: D31 D63 I31 I32
    Date: 2021
  15. By: Baron Law
    Abstract: A simple method is proposed to estimate the instantaneous correlations between state variables in a hybrid system from the empirical correlations between observable market quantities such as spot rate, stock price and implied volatility. The new algorithm is extremely fast since only 4x4 linear systems are involved. In case the resulting matrix from the linear systems is not positive semidefinite, it can be converted easily using a method called shrinking, which requires only bisection-style iterations. The square of short-term at-the-money implied volatility is suggested as the proxy for the unobservable stochastic variance. If the implied volatility is not available, a simple algorithm is provided to fill in the missing correlations. Numerical study shows that the estimates are reasonably accurate, when using more than 1,000 data points. In addition, the algorithm is robust to misspecified interest rate model parameters and the short sampling period assumption. G2++ and Heston are used for illustration but the method can be extended to other affine term structure, local volatility and jump diffusion models, with or without stochastic interest rate.
    Date: 2021–11

This nep-ecm issue is ©2021 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.