nep-ecm New Economics Papers
on Econometrics
Issue of 2021‒11‒08
twelve papers chosen by
Sune Karlsson
Örebro universitet

  1. Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice By Chaohua Dong; Jiti Gao; Bin Peng; Yundong Tu
  2. On Time-Varying VAR models: Estimation, Testing and Impulse Response Analysis By Yayi Yan; Jiti Gao; Bin Peng
  3. Bandwidth selection for the Local Polynomial Double Conditional Smoothing under Spatial ARMA Errors* By Bastian Schäfer
  4. The persistence of wages By Paulo M.M. Rodrigues; Pedro Portugal; Anabela Carneiro; Pedro Raposo
  5. Testing for Spurious Dynamics in Structural Models with Applications to Monetary Policy By Mikhail Dmitriev; Manoj Atolia
  6. Inferring Inequality: Testing for Median-Preserving Spreads in Ordinal Data By Ramses H. Abul Naga; Christopher Stapenhurstz; Gaston Yalonetzky
  7. Heterogeneous Tail Generalized Common Factor Modeling By Simon Hediger; Jeffrey Näf; Marc S. Paolella; Pawel Polak
  8. "Production Function Estimation: Biased Coefficients and Endogenous Regressors, or a Case of Collective Amnesia?" By Jesus Felipe; John McCombie; Aashish Mehta; Donna Faye Bajaro
  9. Equilibrium multiplicity in dynamic games: testing and estimation By Taisuke Otsu; Martin Pesendorfer
  10. Difference-in-differences with an ordinal dependentvariable: Assessing the impact of the London bombings on the safety perceptions of Muslims By Arne Risa Hole
  11. Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach By Guohua Feng; Jiti Gao; Bin Peng
  12. Dividend Momentum and Stock Return Predictability: A Bayesian Approach By Juan Antolín-Díaz; Ivan Petrella; Juan F. Rubio-Ramírez

  1. By: Chaohua Dong; Jiti Gao; Bin Peng; Yundong Tu
    Abstract: This paper proposes a class of parametric multiple-index time series models that involve linear combinations of time trends, stationary variables and unit root processes as regressors. The inclusion of the three different types of time series, along with the use of a multiple-index structure for these variables to circumvent the curse of dimensionality, is due to both theoretical and practical considerations. The M-type estimators (including OLS, LAD, Huber’s estimator, quantile and expectile estimators, etc.) for the index vectors are proposed, and their asymptotic properties are established, with the aid of the generalized function approach to accommodate a wide class of loss functions that may not be necessarily differentiable at every point. The proposed multiple-index model is then applied to study the stock return predictability, which reveals strong nonlinear predictability under various loss measures. Monte Carlo simulations are also included to evaluate the finite-sample performance of the proposed estimators.
    Keywords: multivariate dynamic time series, time-varying impulse response, testing for parameter stability
    JEL: C13 C22
    Date: 2021
  2. By: Yayi Yan; Jiti Gao; Bin Peng
    Abstract: Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of theories, including the impulse responses analyses subject to both of the short-run timing and the long-run restrictions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application to the transmission mechanism of U.S. monetary policy.
    Keywords: multivariate dynamic time series, time-varying impulse response, testing for parameter stability
    JEL: C14 C32 E52
    Date: 2021
  3. By: Bastian Schäfer (Paderborn University)
    Abstract: Nonparametric estimation of the mean surface of spatial data usually depends on a bivariate regressor, which is an ineffective estimation method for large data sets. The Double Conditional Smoothing (DCS) increases computational efficiency by reducing the regression problem to one dimension. We apply the DCS scheme to two-dimensional functional or spatial time series and use local polynomial regression for estimation of the regression surface and its derivatives. Asymptotic formulas for expectation and variance are given and formulas for the asymptotic optimal bandwidth derived. We propose a iterative plug-in algorithm for estimation of these optimal bandwidths under dependent errors. Spatial ARMA processes are used to model the error sequece parametrically and some estimation procedures for spatial ARMA processes are suggested. The proposed methods are assessed via a simulation study and applied to high-freqency financial data.
    Keywords: Semiparametric regression, functional double conditional smoothing, bandwidth selection, iterative plug-in, dependent errors
    Date: 2021–11
  4. By: Paulo M.M. Rodrigues; Pedro Portugal; Anabela Carneiro; Pedro Raposo
    Abstract: This paper provides comprehensive and detailed empirical regression analyses of the sources of wage persistence. Exploring a rich matched employer-employee data set and the estimation of a dynamic panel wage equation with high-dimensional fixed effects, our empirical results show that permanent unobserved heterogeneity plays a key role in driving wage dynamics. The decomposition of the omitted variable bias indicates that the most important source of bias is the persistence of worker characteristics, followed by the heterogeneity of firms’ wage policy and last by the job-match quality. We highlight the importance of the incidental parameter problem, which induces a severe downward bias in the autoregressive parameter estimate, through both an in-depth Monte Carlo study and an empirical analysis. Using three alternative bias correction methods (the split-panel Jackknife (Dhaene and Jochmans, 2015), an analytical expression (Hahn and Kuersteiner, 2002), and a residual based bootstrap approach (Everaert and Pozzi, 2007, Gonçalves and Kaffo, 2015)), we observe that up to one-third of the reduction of the autoregressive parameter estimates induced by the control of permanent heterogeneity (high dimensional fixed effects) may not be justified.
    JEL: E24 J31 J63 J65
    Date: 2021
  5. By: Mikhail Dmitriev (Department of Economics, Florida State University); Manoj Atolia (Department of Economics, Florida State University)
    Abstract: We propose a universal and straightforward test for validating assumptions in the structural models. Structural models impose a causal structure, take data as an input, and then produce exact structural parameters. We simulate the new data while breaking the original causal structure. We then feed the model the simulated data and then see whether it produces different results. If its conclusions are the same, then the models’ implications are not sensitive to the underlying data, and the model fails the test. We then apply our test to the models analyzing monetary policy. We find out that simple SVARs successfully pass the test and can be used to identify monetary policy effects. On the other hand, DSGE models estimated via full-information methods such as Smets and Wouters (2007) fail the test and potentially force their conclusions on the data.
    Keywords: VARs, SVARs, DSGE, monetary policy
    JEL: C68 E44 E61
    Date: 2021–10
  6. By: Ramses H. Abul Naga (Departamento de Teoría e Historia Económica, Universidad de Málaga; Business School, University of Aberdeen; and Pan African Scientific Research Council.); Christopher Stapenhurstz (University of Edinburgh); Gaston Yalonetzky (University of Leeds)
    Abstract: The median-preserving spread (MPS) ordering for ordinal variables (Allison and Foster, 2004) has become ubiquitous in the inequality literature. However, the literature lacks an explicit frequentist method for inferring whether an ordered multinomial distribution G is more unequal than F according to the MPS criterion. We devise formal statistical tests of the hypothesis that G is not an MPS of F. Rejection of this hypothesis enables the conclusion that G is robustly more unequal than F. Using Monte Carlo simulations and novel graphical techniques, we fi nd that the choice between Z and Likelihood Ratio test statistics does not have a large impact on the properties of the tests, but that the method of inference does: bootstrap inference has generally better size and power properties than asymptotic inference. We illustrate the usefulness of our tests with three applications: (i) happiness inequality in the United States, (ii) self-assessed health in Europe and (iii) sanitation ladders in Pakistan.
    Keywords: inequality measurement; hypothesis testing; median preserving spread; ordinal data
    Date: 2021–10
  7. By: Simon Hediger (University of Zurich - Department of Banking and Finance); Jeffrey Näf (ETH Zurich); Marc S. Paolella (University of Zurich - Department of Banking and Finance; Swiss Finance Institute); Pawel Polak (Stony Brook University-Department of Applied Mathematics and Statistics)
    Abstract: A multivariate normal mean-variance heterogeneous tails mixture distribution is proposed for the joint distribution of financial factors and asset returns (referred to as Factor-HGH). The proposed latent variable model incorporates a Cholesky decomposition of the dispersion matrix to ensure a rich dependency structure for capturing the stylized facts of the data. It generalizes several existing model structures, with or without financial factors. It is further applicable in large dimensions due to a fast ECME estimation algorithm of all the model parameters. The advantages of modelling financial factors and asset returns jointly under non-Gaussian errors are illustrated in an empirical comparison study between the proposed Factor-HGH model and classical financial factor models. While the results for the Fama-French 49 industry portfolios are in line with Gaussian-based models, in the case of highly tail heterogeneous cryptocurrencies, the portfolio based on the Factor HGH model doubles the average return while keeping the volatility, the maximum drawdown, the turnover, and the expected-shortfall at a low level.
    Keywords: Asset Pricing Model, Cryptocurrencies, Expectation Maximization Algorithm, Heterogeneous Tails, Mixture Distribution, Portfolio Optimization
    Date: 2021–10
  8. By: Jesus Felipe; John McCombie; Aashish Mehta; Donna Faye Bajaro
    Abstract: The possible endogeneity of labor and capital in production functions, and the consequent bias of the estimated elasticities, has been discussed and addressed in the literature in different ways since the 1940s. This paper revisits an argument first outlined in the 1950s, which questioned production function estimations. This argument is that output, capital, and employment are linked through a distribution accounting identity, a key point that the recent literature has overlooked. This identity can be rewritten as a form that resembles a production function (Cobb-Douglas, CES, translog). We show that this happens because the data used in empirical exercises are value (monetary) data, not physical quantities. The argument has clear predictions about the size of the factor elasticities and about what is commonly interpreted as the bias of the estimated elasticities. To test these predictions, we estimate a typical Cobb-Douglas function using five estimators and show that: (i) the identity is responsible for the fact that the elasticities must be the factor shares; (ii) the bias of the estimated elasticities (i.e., departure from the factor shares) is, in reality, caused by the omission of a term in the identity. However, unlike in the standard omitted-variable bias problem, here the omitted term is known; and (iii) the estimation method is a second-order issue. Estimation methods that theoretically deal with endogeneity, including the most recent ones, cannot solve this problem. We conclude that the use of monetary values rather than physical data poses an insoluble problem for the estimation of production functions. This is, consequently, far more serious than any supposed endogeneity problems.
    Keywords: Accounting Identity; Endogeneity; Monetary Values; Production Functions; Total Factor Productivity
    JEL: C18 C81 C82
    Date: 2021–10
  9. By: Taisuke Otsu; Martin Pesendorfer
    Abstract: This paper surveys the recent literature on dynamic games estimation when there is a concern of equilibrium multiplicity. We focus on the questions of testing for equilibrium multiplicity and estimation in the presence of multiplicity.
    Keywords: Dynamic Markov game, Multiplicity of equilibria
    JEL: C12 C72 D44
    Date: 2021–10
  10. By: Arne Risa Hole (Department of Economics, University of Sheffield, UK)
    Abstract: In this paper, we propose a methodology for estimating treatment effects when using difference-in-differences with an ordinal dependent variable. Specifically, we derive an expression for the Average Treatment Effect on the Treated in terms of changes in response probabilities. An advantage of taking this approach is the ability to assess any distributional effects of exposure to treatment. We use the proposed estimator to evaluate the impact of the London bombings on the safety perceptions of Muslims, with our results highlighting a shift from moderately low to very high safety concerns among younger Muslims in the aftermath of the bombings.
    Keywords: difference-indifferences, ordinal dependent variables, terrorism
    JEL: C25 I10 I31 J15
    Date: 2021–09
  11. By: Guohua Feng; Jiti Gao; Bin Peng
    Abstract: Despite its paramount importance in the empirical growth literature, productivity convergence analysis has three problems that have yet to be resolved: (1) little attempt has been made to explore the hierarchical structure of industry-level datasets; (2) industry-level technology heterogeneity has largely been ignored; and (3) cross-sectional dependence has rarely been allowed for. This paper aims to address these three problems within a hierarchical panel data framework. We propose an estimation procedure and then derive the corresponding asymptotic theory. Finally, we apply the framework to a dataset of 23 manufacturing industries from a wide range of countries over the period 1963-2018. Our results show that both the manufacturing industry as a whole and individual manufacturing industries at the ISIC two-digit level exhibit strong conditional convergence in labour productivity, but not unconditional convergence. In addition, our results show that both global and industry-specific shocks are important in explaining the convergence behaviours of the manufacturing industries.
    Keywords: growth regressions, convergence in manufacturing, cross-sectional dependence, hierarchical model, asymptotic theory
    JEL: L60 O10 C23
    Date: 2021
  12. By: Juan Antolín-Díaz; Ivan Petrella; Juan F. Rubio-Ramírez
    Abstract: A long tradition in macro-finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label “dividend momentum.” Compared to estimation based on OLS, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out-of-sample and realistic asset allocation prescriptions with Sharpe ratios that out-perform common benchmarks.
    Date: 2021–11

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