nep-ecm New Economics Papers
on Econometrics
Issue of 2020‒02‒17
sixteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Blocked Clusterwise Regression By Max Cytrynbaum
  2. Partial Identification and Inference for Dynamic Models and Counterfactuals By Myrto Kalouptsidi; Yuichi Kitamura; Lucas Lima; Eduardo Souza-Rodrigues
  3. Diffusion Copulas: Identification and Estimation By Ruijun Bu; Kaddour Hadri; Dennis Kristensen
  4. Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors By Donegan, Connor
  5. Empirical Tail Copulas for Functional Data By Einmahl, John; Segers, Johan
  6. Generalized Forecasr Averaging in Autoregressions with a Near Unit Root By Mohitosh Kejriwal; Xuewen Yu
  7. A Multi-Factor Transformed Diffusion Model with Applications to VIX and VIX Futures By Ruijun Bu; Fredj Jawadi; Yuyi Li
  8. Semiparametric Bayesian Instrumental Variables Estimation for Nonignorable Missing Instruments By Ryo Kato; Takahiro Hoshino
  9. NAPLES;Mining the lead-lag Relationship from Non-synchronous and High-frequency Data By Katsuya Ito; Kei Nakagawa
  10. In Praise of Confidence Intervals By David Romer
  11. Rethinking error correction model in macroeconometric analysis : A relevant review By Pinshi, Christian
  12. Quasi-likelihood analysis for marked point processes and application to marked Hawkes processes By Simon Clinet
  13. Estimating the Welfare Effects of School Vouchers By Vishal Kamat; Samuel Norris
  14. Structural Change and the Problem of Phantom Break Locations By Yao Rao; Brendan McCabe
  15. Rational Addiction and Time Consistency: An Empirical Test By Piccoli, Luca; Tiezzi, Silvia
  16. Estimating persistence for irregularly spaced historical data By Franses, Ph.H.B.F.

  1. By: Max Cytrynbaum
    Abstract: A recent literature in econometrics models unobserved cross-sectional heterogeneity in panel data by assigning each cross-sectional unit a one-dimensional, discrete latent type. Such models have been shown to allow estimation and inference by regression clustering methods. This paper is motivated by the finding that the clustered heterogeneity models studied in this literature can be badly misspecified, even when the panel has significant discrete cross-sectional structure. To address this issue, we generalize previous approaches to discrete unobserved heterogeneity by allowing each unit to have multiple, imperfectly-correlated latent variables that describe its response-type to different covariates. We give inference results for a k-means style estimator of our model and develop information criteria to jointly select the number clusters for each latent variable. Monte Carlo simulations confirm our theoretical results and give intuition about the finite-sample performance of estimation and model selection. We also contribute to the theory of clustering with an over-specified number of clusters and derive new convergence rates for this setting. Our results suggest that over-fitting can be severe in k-means style estimators when the number of clusters is over-specified.
    Date: 2020–01
  2. By: Myrto Kalouptsidi; Yuichi Kitamura; Lucas Lima; Eduardo Souza-Rodrigues
    Abstract: We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We characterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference procedure by an appropriate application of subsampling. To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of firm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries. In these applications, we demonstrate how the identified sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identification.
    Keywords: Dynamic Discrete Choice, Counterfactual, Partial Identification, Subsampling, Uniform Inference, Structural Model
    JEL: C0 C18 C50 C61 L0
    Date: 2020–02–06
  3. By: Ruijun Bu; Kaddour Hadri; Dennis Kristensen
    Abstract: We propose a new semiparametric approach for modelling nonlinear univariate diffusions, where the observed processes are nonparametric transformations of underlying parametric diffusions (UPDs). This modelling strategy yields a general class of semiparametric Markov diffusion models with parametric dynamic copulas and nonparametric marginal distributions. We provide primitive conditions for the identification of the UPD parameters together with the unknown transformations from discrete samples. Semiparametric likelihood-based estimators of the UPD parameters are developed and we show that under regularity conditions both the parametric and nonparametric components converge with parametric rate towards Normal distributions. Kernel-based drift and diffusion estimators are also proposed and shown to be normally distributed in large samples. A simulation study investigates the Önite sample performance of our estimators in the context of modelling US short-term interest rates.
    Keywords: Continuous-time model; diffusion process; copula; transformation model; identification; nonparametric; semiparametric; maximum likelihood; sieve; kernel smoothing
    JEL: C14 C22 C32 C58 G12
    Date: 2018–07
  4. By: Donegan, Connor
    Abstract: This paper proposes a Bayesian method for spatial regression using eigenvector spatial filtering (ESF) and Piironen and Vehtari's (2017) regularized horseshoe (RHS) prior. ESF models are most often estimated using variable selection procedures such as stepwise selection, but in the absence of a Bayesian model averaging procedure variable selection methods cannot properly account for parameter uncertainty. Hierarchical shrinkage priors such as the RHS address the foregoing concern in a computationally efficient manner by encoding prior information about spatial filters into an adaptive prior distribution which shrinks posterior estimates towards zero in the absence of a strong signal while only minimally regularizing coefficients of important eigenvectors. This paper presents results from a large simulation study which compares the performance of the proposed Bayesian model (RHS-ESF) to alternative spatial models under a variety of spatial autocorrelation scenarios and model specifications. The RHS-ESF model performance matched that of the SAR model from which the data was generated. The study highlights that reliable uncertainty estimates require greater attention to spatial autocorrelation in covariates than is typically given. A demonstration analysis of 2016 U.S. Presidential election results further evidences robustness of results to hyper-prior specifications as well as the advantages of estimating spatial models using the Stan probabilistic programming language.
    Date: 2020–01–18
  5. By: Einmahl, John (Tilburg University, Center For Economic Research); Segers, Johan
    Abstract: For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the dependence in the upper tail. The empirical versions of these functions are rank-based estimators whose inflated estimation errors are known to converge weakly to a Gaussian process that is similar in structure to the weak limit of the empirical copula process. We extend this multivariate result to continuous functional data by establishing the asymptotic normality of the estimators of the tail copula, uniformly over all finite subsets of at most D points (D fixed). As a special case we obtain the uniform asymptotic normality of all estimated upper tail dependence coefficients. The main tool for deriving the result is the uniform asymptotic normality of all the D-variate tail empirical processes. The proof of the main result is non-standard.
    Keywords: extreme value statistics; functional data; tail empirical process; tal dependence; tial copula estimation; uniform asymptotic normality
    JEL: C13 C14
    Date: 2020
  6. By: Mohitosh Kejriwal; Xuewen Yu
    Abstract: This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging.
    JEL: C22
    Date: 2019–12
  7. By: Ruijun Bu; Fredj Jawadi; Yuyi Li
    Abstract: Transformed diffusions (TDs) have become increasingly popular in financial modelling for their model flexibility and tractability. While existing TD models are predominately one-factor models, empirical evidence often prefers models with multiple factors. We propose a novel distribution-driven nonlinear multi-factor TD model with latent components. Our model is a transformation of a underlying multivariate Ornstein Uhlenbeck (MVOU) process, where the transformation function is endogenously specified by a flexible parametric stationary distribution of the observed variable. Computationally efficient exact likelihood inference can be implemented for our model using a modified Kalman filter algorithm and the transformed affline structure also allows us to price derivatives in semi-closed form. We compare the proposed multi-factor model with existing TD models for modelling VIX and pricing VIX futures. Our results show that the proposed model outperforms all existing TD models both in the sample and out of the sample consistently across all categories and scenarios of our comparison.
    Keywords: Transformation Model; Nonlinear Diffusion; Latent Factor; Kalman Filter; Volatility Index
    JEL: C32 G13 G15
    Date: 2018–08
  8. By: Ryo Kato (Research Institute for Economics and Business Administration, Kobe University, Japan); Takahiro Hoshino (Department of Economics, Keio University and RIKEN Center for Advanced Intelligence Project, Japan)
    Abstract: This paper considers the case where instrumental variable (IV) are available to infer the e¤ect of interested variable to the outcome (or the causal e¤ect), but some components of IV are missing with the missing mechanism of not missing at random (NMAR). Although NMAR requires the analysis to prespecify the missing mechanism, it is unknown for us and what is worse, it is generally not identi ed. We use the IV distribution of original population as an auxiliary information, and show that missing mechanism can be represented as identi able nonparametric generalized additive model. We also introduce MCMC algorithm that impute the missing values and simultaneously estimate parameters of interested.
    Keywords: Instrumental variable; Missing not at random; Auxiliary information
    Date: 2020–02
  9. By: Katsuya Ito; Kei Nakagawa
    Abstract: In time-series analysis, the term "lead-lag effect" is used to describe a delayed effect on a given time series caused by another time series. lead-lag effects are ubiquitous in practice and are specifically critical in formulating investment strategies in high-frequency trading. At present, there are three major challenges in analyzing the lead-lag effects. First, in practical applications, not all time series are observed synchronously. Second, the size of the relevant dataset and rate of change of the environment is increasingly faster, and it is becoming more difficult to complete the computation within a particular time limit. Third, some lead-lag effects are time-varying and only last for a short period, and their delay lengths are often affected by external factors. In this paper, we propose NAPLES (Negative And Positive lead-lag EStimator), a new statistical measure that resolves all these problems. Through experiments on artificial and real datasets, we demonstrate that NAPLES has a strong correlation with the actual lead-lag effects, including those triggered by significant macroeconomic announcements.
    Date: 2020–02
  10. By: David Romer
    Abstract: Most empirical papers in economics focus on two aspects of their results: whether the estimates are statistically significantly different from zero and the interpretation of the point estimates. This focus obscures important information about the implications of the results for economically interesting hypotheses about values of the parameters other than zero, and in some cases, about the strength of the evidence against values of zero. This limitation can be overcome by reporting confidence intervals for papers’ main estimates and discussing their economic interpretation.
    JEL: C10 C12
    Date: 2020–01
  11. By: Pinshi, Christian
    Abstract: The cointégration methodology has bridged the growing gap between economists and econometricians in understanding dynamics, equilibrium and bias on the reliability of macroeconomic and financial analysis, which is subject to non-stationary behavior. This paper proposes a comprehensive literature review on the relevance of the error correction model. Econometricians and economists have shown that error-correction model is a powerful machine that provides the economic system and macroeconomic policy with a refinement in the econometric results
    Keywords: Cointegration, Error correction model, Macroeconomics
    JEL: C32 E0
    Date: 2020–01
  12. By: Simon Clinet
    Abstract: We develop a quasi-likelihood analysis procedure for a general class of multivariate marked point processes. As a by-product of the general method, we establish under stability and ergodicity conditions the local asymptotic normality of the quasi-log likelihood, along with the convergence of moments of quasi-likelihood and quasi-Bayesian estimators. To illustrate the general approach, we then turn our attention to a class of multivariate marked Hawkes processes with generalized exponential kernels, comprising among others the so-called Erlang kernels. We provide explicit conditions on the kernel functions and the mark dynamics under which a certain transformation of the original process is Markovian and $V$-geometrically ergodic. We finally prove that the latter result, which is of interest in its own right, constitutes the key ingredient to show that the generalized exponential Hawkes process falls under the scope of application of the quasi-likelihood analysis.
    Date: 2020–01
  13. By: Vishal Kamat; Samuel Norris
    Abstract: We analyze the welfare effects of voucher provision in the DC Opportunity Scholarship Program (OSP), a school voucher program in Washington, DC, that randomly allocated vouchers to students. To do so, we develop new discrete choice tools to show how to use data with random allocation of school vouchers to characterize what we can learn about the welfare benefits of providing a voucher of a given amount, as measured by the average willingness to pay for that voucher, and these benefits net of the costs of providing that voucher. A novel feature of our tools is that they allow specifying the relationship of the demand for the various schools with respect to prices to be entirely nonparametric or to be parameterized in a flexible manner, both of which do not necessarily imply that the welfare parameters are point identified. Applying our tools to the OSP data, we find that provision of the status-quo as well as a wide range of counterfactual voucher amounts has a positive net average benefit. We find these positive results arise due to the presence of many low-tuition schools in the program, removing these schools from the program can result in a negative net average benefit.
    Date: 2020–01
  14. By: Yao Rao; Brendan McCabe
    Abstract: It is well known, in structural break problems, that it is much easier to detect the existence of a break in a data set than to determine the location of such a break in the sample span. This paper investigates why, in the context of Gaussian linear regressions, using a decision theory framework. The nub of the problem, even for moderately sized breaks, is that the posterior probability distribution of the possible break points is usually not very informative about the true break location. The information content is measured here by a proper scoring rule. Hence, even a locally optimal break location procedure, as introduced here, is ineffective. In the regression context, it turns out to be quite common, indeed the norm, for break location procedures to misidentify the true break position up to 100% of the time. Unfortunately too, the magnitude of the di§erence between the misidentified and true break locations is usually not small.
    Keywords: CUSUM test, Phantom Break Locations, Structural change
    Date: 2018–11
  15. By: Piccoli, Luca; Tiezzi, Silvia (University of Siena)
    Abstract: This paper deals with one of the main empirical problems associated with the rational addiction theory, namely that its derived demand equation is not empirically distinguishable from models with forward looking behavior, but with time inconsistent preferences. The implication is that, even when forward looking behavior is supported by data, the standard rational addiction equation cannot distinguish between time consistency and inconsistency in preferences. We show that an encompassing general specification of the rational addiction model embeds the possibility of testing for time consistent versus time inconsistent naïve agents. We use a panel of Russian individuals to estimate a rational addiction equation for tobacco with time inconsistent preferences, where GMM estimators deal with errors in variables and unobserved heterogeneity. The results conform to the theoretical predictions and the proposed test for time consistency does not reject the hypothesis that Russian cigarettes consumers discount future utility exponentially. We further show that the proposed empirical specification of the Euler equation, whilst being indistinguishable from the general empirical specification of the rational addiction model, it allows to identify more structural parameters, such as an upper-bound for the parameter capturing present bias in time preferences.
    Keywords: rational addiction, general versus standard specification, time consistency, naïveté, GMM
    JEL: C23 D03 D12
    Date: 2020–01
  16. By: Franses, Ph.H.B.F.
    Abstract: This paper introduces to the literature on Economic History a measure of persistence which is particularly useful if the data are irregularly spaced. An illustration to 10 historical unevenly spaced data series for Holland of 1738 to 1779 showed the merits of the methodology
    Keywords: Irregularly spaced time series, Economic history, First order autoregression, Persistence
    JEL: C32 N01
    Date: 2019–09–01

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