nep-ecm New Economics Papers
on Econometrics
Issue of 2021‒03‒29
thirteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Semiparametric Identification and Estimation of Multinomial Discrete Choice Models using Error Symmetry By Arthur Lewbel; Jin Yan; Yu Zhou
  2. Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data By Min Seong Kim
  3. Dynamic factor models: does the specification matter? By Ruiz Ortega, Esther; Poncela, Pilar; Miranda Gualdrón, Karen Alejandra
  4. Regularized Conditional Estimators of Unit Inefficiency in Stochastic Frontier Analysis, with Application to Electricity Distribution Market By Zeebari, Zangin; Månsson, Kristofer; Sjölander, Pär; Söderberg, Magnus
  5. Tail Forecasting with Multivariate Bayesian Additive Regression Trees By ; Todd E. Clark; Florian Huber; Gary Koop; Massimiliano Marcellino
  6. Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects By Hugo Kruiniger
  7. Gaussian Rank Correlation and Regression By Dante Amengual; Enrique Sentana; Zhanyuan Tian
  8. The Jacobian of the Exponential Function By Jan R. Magnus; Henk G. J. Pijls; Enrique Sentana
  9. On statistical estimation and inferences in optional regression models By Mohamed Abdelghani; Alexander Melnikov; Andrey Pak
  10. Mixture composite regression models with multi-type feature selection By Tsz Chai Fung; George Tzougas; Mario Wuthrich
  11. Factor Strengths, Pricing Errors, and Estimation of Risk Premia By M. Hashem Pesaran; Ron P. Smith
  12. Is There A Replication Crisis In Finance? By Theis Ingerslev Jensen; Bryan T. Kelly; Lasse Heje Pedersen
  13. Monte Carlo results of root-N consistent estimators for the dynamic fixed effects logit model with neither explanatory variables nor time dummies By Yoshitsugu Kitazawa

  1. By: Arthur Lewbel (Boston College); Jin Yan (Chinese University of Hong Kong); Yu Zhou (School of Economics, Fudan University)
    Abstract: We provide a new method to point identify and estimate cross-sectional multinomial choice models, using conditional error symmetry. Our model nests common random coefficient specifications (without having to specify which regressors have random coefficients), and more generally allows for arbitrary heteroskedasticity on most regressors, unknown error distribution, and does not require a "large support" "(such as identification at infinity) assumption. We propose an estimator that minimizes the squared di§erences of the estimated error density at pairs of symmetric points about the origin. Our estimator is root N consistent and asymptotically normal, making statistical inference straightforward.
    Keywords: Central Symmetry, Exclusion Restriction, Multinomial Discrete Choice
    JEL: C14 C35
    Date: 2021–02–15
  2. By: Min Seong Kim (University of Connecticut)
    Abstract: In this paper, we propose the time-series average of spatial HAC estimators for the variance of the estimated common factors under the approximate factor structure. Based on this, we provide the con dence interval for the conditional mean of the dffusion-index forecasting model with cross-sectional heteroskedasticity and dependence of the idiosyncratic errors. We establish the asymptotics under very mild conditions, and no prior information about the dependence structure is needed to implement our procedure. We employ a bootstrap to select the bandwidth parameter. Simulation studies show that our procedure performs well in nite samples. We apply the proposed con dence interval to the problem of forecasting the unemployment rate using data by Ludvigson and Ng (2010).
    Keywords: Approximate factor structure, Bandwidth selection, Di¤usion index forecast, Ro-bust inference, Spatial HAC estimator
    JEL: C12 C31 C38
    Date: 2021–03
  3. By: Ruiz Ortega, Esther; Poncela, Pilar; Miranda Gualdrón, Karen Alejandra
    Abstract: Dynamic Factor Models (DFMs), which assume the existence of a small number of unobserved underlying factors capturing the comovements in large systems of variables, are very popular among empirical macroeconomists to reduce dimension and to extract factors with an economic interpretation. Factors can be extracted using either non-parametric Principal Components (PC) or parametric Kalman filter and smoothing (KFS) procedures, with the former being computationally simpler and robust against misspecification and the latter being efficient if the specification is correct and coping in a natural way with missing and mixed-frequency data, time-varying parameters, non-linearities and non-stationarity among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation and forecasting of using alternative extraction procedures and estimators of the DFM parameters under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables that has been widely analyzed in the literature without consensus about the most appropriate model speciffication. We show that this lack of consensus is ony marginally cruzial when it comes to factor extraction but it matters when the objective is forecasting.
    Keywords: State-Space Model; Principal Components; Kalman Filter; Em Algorithm
    Date: 2021–03–23
  4. By: Zeebari, Zangin (Jönköping University); Månsson, Kristofer (Jönköping University); Sjölander, Pär (Jönköping University); Söderberg, Magnus (The Ratio Institute)
    Abstract: The practical value of Stochastic Frontier Analysis (SFA) is positively related to the level of accuracy at which it estimates unit-specific inefficiencies. Conventional SFA unit inefficiency estimation is based on the mean/mode of the inefficiency, conditioned on the estimated composite error. This approach shrinks the inefficiency towards its mean/mode, which generates a distribution that is different from the distribution of the unconditional inefficiency; thus, the accuracy of the estimated inefficiency is negatively correlated with the distance the inefficiency is located from its mean/mode. We propose a regularized estimator based on Bayesian risk (expected loss) that restricts the unit inefficiency to satisfy the underlying theoretical mean and variation assumptions. We analytically investigate some properties of the maximum a posteriori probability estimator under mild assumptions and derive a regularized conditional mode estimator for three different inefficiency densities commonly used in SFA applications. Extensive simulations show that, under common empirical situations, e.g., regarding sample size and signal-to-noise ratio, the regularized estimator outperforms the conventional (unregularized) approach when the inefficiency is greater than its mean/mode. With real data from electricity distribution sector in Sweden, we demonstrate that the conventional conditional estimators and our regularized conditional estimators give substantially different results for highly inefficient companies.
    Keywords: Electricity Distribution; Productivity; Regularized Posterior Likelihood; Stochastic Frontier Analysis
    JEL: C21 D24 L94
    Date: 2021–03–24
  5. By: ; Todd E. Clark; Florian Huber; Gary Koop; Massimiliano Marcellino
    Abstract: We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our results suggest that using nonparametric models generally leads to improved forecast accuracy. In particular, when interest centers on the tails of the posterior predictive, flexible models improve upon standard VAR models with SV. Another key finding is that if we allow for nonlinearities in the conditional mean, allowing for heteroskedasticity becomes less important. A scenario analysis reveals highly nonlinear relations between the predictive distribution and financial conditions.
    Keywords: nonparametric VAR; regression trees; macroeconomic forecasting
    JEL: C11 C32 C53
    Date: 2021–03–22
  6. By: Hugo Kruiniger
    Abstract: In this paper we first propose a root-n-consistent Conditional Maximum Likelihood (CML) estimator for all the common parameters in the panel logit AR(p) model with strictly exogenous covariates and fixed effects. Our CML estimator (CMLE) converges in probability faster and is more easily computed than the kernel-weighted CMLE of Honor\'e and Kyriazidou (2000). Next, we propose a root-n-consistent CMLE for the coefficients of the exogenous covariates only. We also discuss new CMLEs for the panel logit AR(p) model without covariates. Finally, we propose CMLEs for multinomial dynamic panel logit models with and without covariates. All CMLEs are asymptotically normally distributed.
    Date: 2021–03
  7. By: Dante Amengual (CEMFI, Centro de Estudios Monetarios y Financieros); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros); Zhanyuan Tian (Boston University)
    Abstract: We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions - OLS applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogues otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model, and momentum and reversal effects in individual stock returns confi?rm that Gaussian rank procedures are insensitive to outliers.
    Keywords: Copula, growth regressions, migration, misspecification, momentum, robustness, short-term reversals.
    JEL: C13 C46 G14 O47
    Date: 2020–06
  8. By: Jan R. Magnus (Vrije Universiteit Amsterdam and Tinbergen Institu); Henk G. J. Pijls (University of Amsterdam); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We derive closed-form expressions for the Jacobian of the matrix exponential function for both diagonalizable and defective matrices. The results are applied to two cases of interest in macroeconometrics: a continuous-time macro model and the parametrization of rotation matrices governing impulse response functions in structural vector autoregressions.
    Keywords: Matrix differential calculus, orthogonal matrix, continuous-time Markov chain, Ornstein-Uhlenbeck process.
    JEL: C65 C32 C63
    Date: 2020–06
  9. By: Mohamed Abdelghani; Alexander Melnikov; Andrey Pak
    Abstract: The main object of investigation in this paper is a very general regression model in optional setting - when an observed process is an optional semimartingale depending on an unknown parameter. It is well-known that statistical data may present an information flow/filtration without usual conditions. The estimation problem is achieved by means of structural least squares (LS) estimates and their sequential versions. The main results of the paper are devoted to the strong consistency of such LS-estimates. For sequential LS-estimates the property of fixed accuracy is proved.
    Date: 2021–03
  10. By: Tsz Chai Fung; George Tzougas; Mario Wuthrich
    Abstract: The aim of this paper is to present a mixture composite regression model for claim severity modelling. Claim severity modelling poses several challenges such as multimodality, heavy-tailedness and systematic effects in data. We tackle this modelling problem by studying a mixture composite regression model for simultaneous modeling of attritional and large claims, and for considering systematic effects in both the mixture components as well as the mixing probabilities. For model fitting, we present a group-fused regularization approach that allows us for selecting the explanatory variables which significantly impact the mixing probabilities and the different mixture components, respectively. We develop an asymptotic theory for this regularized estimation approach, and fitting is performed using a novel Generalized Expectation-Maximization algorithm. We exemplify our approach on real motor insurance data set.
    Date: 2021–03
  11. By: M. Hashem Pesaran; Ron P. Smith
    Abstract: This paper examines the implications of pricing errors and factors that are not strong for the Fama-MacBeth two-pass estimator of risk premia and its asymptotic distribution when T is fixed with n → ∞, and when both n and T → ∞, jointly. While the literature just distinguishes strong and weak factors we allow for degrees of strength using a recently developed measure. Our theoretical results have important practical implications for empirical asset pricing. Pricing errors and factor strength matter for consistent estimation of risk premia and subsequent inference, thus an estimate of factor strength is required before attempting to estimate risk. Finally, using a recently developed procedure we provide rolling estimates of factor strengths for the five Fama-French factors, and show that only the market factor can be viewed as strong.
    Keywords: factor strength, pricing errors, risk premia, Fama and MacBeth two-pass estimators, Fama-French factors, panel R2
    JEL: C38 G12
    Date: 2021
  12. By: Theis Ingerslev Jensen; Bryan T. Kelly; Lasse Heje Pedersen
    Abstract: Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication, which leads to different conclusions. The majority of asset pricing factors: (1) can be replicated, (2) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio, (3) work out-of-sample in a new large data set covering 93 countries, and (4) have evidence that is strengthened (not weakened) by the large number of observed factors.
    JEL: C11 C58 G02 G10 G11 G12 G15 G17
    Date: 2021–02
  13. By: Yoshitsugu Kitazawa (Faculty of Economics, Kyushu Sangyo University)
    Abstract: This paper shows some Monte Carlo results of root-N consistent estimators for the dynamic fixed effects logit model with neither explanatory variables nor time dummies
    Keywords: Keywords: dynamic fixed effects logit model with neither explanatory variables nor time dummies ; root-N consistent GMM estimators; Monte Carlo
    JEL: C23 C25
    Date: 2020–02

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