nep-ecm New Economics Papers
on Econometrics
Issue of 2019‒05‒27
nine papers chosen by
Sune Karlsson
Örebro universitet

  1. Data-Driven Local Polynomial Trend Estimation for Economic Data - Steady State Adjusting Trends By Marlon Fritz
  2. The Empirical Saddlepoint Estimator By Benjamin Holcblat; Fallaw Sowell
  3. Nonparametric Instrumental Regressions with (Potentially Discrete) Instruments Independent of the Error Term By Samuele Centorrino; Fr\'ed\'erique F\`eve; Jean-Pierre Florens
  4. Simultaneous multiple change-point and factor analysis for high-dimensional time series By Barigozzi, Matteo; Cho, Haeran; Fryzlewicz, Piotr
  5. Essays in econometric theory By Sadikoglu, Serhan
  6. Cointegration in high frequency data By Simon Clinet; Yoann Potiron
  7. Identifying Modern Macro Equations with Old Shocks By Régis Barnichon; Geert Mesters
  8. Adaptive estimation in the linear random coefficients model when regressors have limited variation By Christophe Gaillac; Eric Gautier
  9. Exogenous uncertainty and the identification of Structural Vector Autoregressions with external instruments By Angelini, Giovanni; Fanelli, Luca

  1. By: Marlon Fritz (University of Paderborn)
    Abstract: Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend estimator that is local polynomial, to improve arbitrary trend estimations of commonly used methods with respect to the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. To demonstrate the power of this local polynomial trend estimation approach, an extensive simulation study is performed. Furthermore, an example using UK GDP data along with a detailed manual for empirical application is provided. Smaller error criterion values, adequate detection of the true data generating process (DGP), simplicity and availability favor this purely data-driven local polynomial trend estimation method.
    Keywords: Nonparametric Model, Nonstationary Process, Time Series Models, Empirical Growth Trends
    JEL: C14 C22 O47
    Date: 2019–02
  2. By: Benjamin Holcblat; Fallaw Sowell
    Abstract: We define a moment-based estimator that maximizes the empirical saddlepoint (ESP) approximation of the distribution of solutions to empirical moment conditions. We call it the ESP estimator. We prove its existence, consistency and asymptotic normality, and we propose novel test statistics. We also show that the ESP estimator corresponds to the MM (method of moments) estimator shrunk toward parameter values with lower estimated variance, so it reduces the documented instability of existing moment-based estimators. In the case of just-identified moment conditions, which is the case we focus on, the ESP estimator is different from the MM estimator, unlike the recently proposed alternatives, such as the empirical-likelihood-type estimators.
    Date: 2019–05
  3. By: Samuele Centorrino; Fr\'ed\'erique F\`eve; Jean-Pierre Florens
    Abstract: We consider a nonparametric instrumental regression model with continuous endogenous regressor where instruments are fully independent of the error term. This assumption allows us to extend the reach of this model to cases where the instrumental variable is discrete, and therefore to substantially enlarge its potential empirical applications. Under our assumptions, the regression function becomes solution to a nonlinear integral equation. We contribute to existing literature by providing an exhaustive analysis of identification and a simple iterative estimation procedure. Details on the implementation and on the asymptotic properties of this estimation algorithm are given. We conclude the paper with a simulation experiment for a binary instrument and an empirical application to the estimation of the Engel curve for food, where we show that our estimator delivers results that are consistent with existing evidence under several discretizations of the instrumental variable.
    Date: 2019–05
  4. By: Barigozzi, Matteo; Cho, Haeran; Fryzlewicz, Piotr
    Abstract: We propose the first comprehensive treatment of high-dimensional time series factor models with multiple change-points in their second-order structure. We operate under the most flexible definition of piecewise stationarity, and estimate the number and locations of change-points consistently as well as identifying whether they originate in the common or idiosyncratic components. Through the use of wavelets, we transform the problem of change-point detection in the second-order structure of a high-dimensional time series, into the (relatively easier) problem of change-point detection in the means of high-dimensional panel data. Also, our methodology circumvents the difficult issue of the accurate estimation of the true number of factors in the presence of multiple change-points by adopting a screening procedure. We further show that consistent factor analysis is achieved over each segment defined by the change-points estimated by the proposed methodology. In extensive simulation studies, we observe that factor analysis prior to change-point detection improves the detectability of change-points, and identify and describe an interesting ‘spillover’ effect in which substantial breaks in the idiosyncratic components get, naturally enough, identified as change-points in the common components, which prompts us to regard the corresponding change-points as also acting as a form of ‘factors’. Our methodology is implemented in the R package factorcpt, available from CRAN.
    Keywords: piecewise stationary factor model; change-point detection; principal component analysis; wavelet transformation; Double CUSUM Binary Segmentation
    JEL: C1
    Date: 2018–09
  5. By: Sadikoglu, Serhan (Tilburg University, School of Economics and Management)
    Abstract: This dissertation contains three essays in the field of econometric theory. The first essay focuses on single-index binary choice regression model. A new class of semiparametric estimators based on indirect inference is proposed to estimate the regression coefficients. It is demonstrated that the proposed estimation methodology is feasible under weak distributional assumptions and robust to misclassification of responses. The second essay examines the estimation of threshold regression models with dependent data. In particular, the integrated difference kernel estimator is used as a plug-in estimator which facilitates estimation of a wide array of parametric, semiparametric and nonparametric threshold regression models. The third essay studies the identification and estimation of non separable panel data models with index structure and correlated random effects. The parameter vectors of interest are shown to be identified up to scale and could be estimated by a generalized method of moments estimator with moment conditions based on average derivative and outer product of the difference of derivatives of the regression function.
    Date: 2019
  6. By: Simon Clinet; Yoann Potiron
    Abstract: In this paper, we consider a framework adapting the notion of cointegration when two asset prices are generated by a driftless It\^{o}-semimartingale featuring jumps with infinite activity, observed synchronously and regularly at high frequency. We develop a regression based estimation of the cointegrated relations method and show the related consistency and central limit theory when there is cointegration within that framework. We also provide a Dickey-Fuller type residual based test for the null of no cointegration against the alternative of cointegration, along with its limit theory. Under no cointegration, the asymptotic limit is the same as that of the original Dickey-Fuller residual based test, so that critical values can be easily tabulated in the same way. Finite sample indicates adequate size and good power properties in a variety of realistic configurations, outperforming original Dickey-Fuller and Phillips-Perron type residual based tests, whose sizes are distorted by non ergodic time-varying variance and power is altered by price jumps. Two empirical examples consolidate the Monte-Carlo evidence that the adapted tests can be rejected while the original tests are not, and vice versa.
    Date: 2019–05
  7. By: Régis Barnichon; Geert Mesters
    Abstract: Despite decades of research, the consistent estimation of structural forward looking macroeconomic equations remains a formidable empirical challenge because of pervasive endogeneity issues. Prominent cases |the estimation of Phillips curves, of Euler equations for consumption or output, or of monetary policy rules| have typically relied on using pre-determined variables as instruments, with mixed success. In this work, we propose a new approach that consists in using sequences of independently identified structural shocks as instrumental variables. Our approach is robust to weak instruments and is valid regardless of the shocks' variance contribution. We estimate a Phillips curve using monetary shocks as instruments and find that conventional methods (i) substantially under-estimate the slope of the Phillips curve and (ii) over-estimate the role of forward-looking inflation expectations.
    Keywords: structural equations, Instrumental Variables, impulse responses, robust inference
    JEL: C14 C32 E32 E52
    Date: 2019–05
  8. By: Christophe Gaillac (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - CNRS - Centre National de la Recherche Scientifique - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique); Eric Gautier (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - CNRS - Centre National de la Recherche Scientifique - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales, UT1 - Université Toulouse 1 Capitole)
    Abstract: We consider a linear model where the coefficients-intercept and slopes-are random and independent from regressors which support is a proper subset. When the density has finite weighted L 2 norm, for well chosen weights, the joint density of the random coefficients is identified. Lower bounds on the supremum risk for the estimation of the density are derived for this model and a related white noise model. We present an estimator, its rates of convergence, and a data-driven rule which delivers adaptive estimators. An R package RandomCoefficients that implements our estimator is available on CRAN.R.
    Keywords: Minimax,Random Coefficients,Ill-posed Inverse Problem,Adaptation
    Date: 2019–05–15
  9. By: Angelini, Giovanni; Fanelli, Luca
    Abstract: We provide necessary and sufficient conditions for the identification of Structural Vector Autoregressions (SVARs) with external instruments, considering the case in which r instruments are used to identify g structural shocks of interest, r>=g>=1. Novel frequentist estimation methods are discussed by considering both a partial shocks identification strategy, where only g structural shocks are of interest and are instrumented, and in a full shocks identification strategy, where despite g structural shocks are instrumented, all n structural shocks of the system can be identified under certain conditions. The suggested approach is applied to empirically investigate whether financial and macroeconomic uncertainty can be approximated as exogenous drivers of U.S. real economic activity, or rather as endogenous responses to first moment shocks, or both. We analyze whether the dynamic causal effects of non-uncertainty shocks on macroeconomic and financial uncertainty are signicant in the period after the Global Financial Crisis.
    Keywords: Exogenous Uncertainty, External Instruments, Identification, proxy-SVAR, SVAR.
    JEL: C32 C51 E44 G10
    Date: 2018–05

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