nep-ecm New Economics Papers
on Econometrics
Issue of 2017‒04‒30
eight papers chosen by
Sune Karlsson
Örebro universitet

  1. Bayesian Inference for a 1-Factor Copula Model By Ban Kheng Tan; Anastasios Panagiotelis; George Athanasopoulos
  2. Identification of and Correction for Publication Bias By Isaiah Andrews; Maximilian Kasy
  3. Parametrically and Semiparametrically Efficient Detection of Random Regression Coefficients By Mohamed Fihri; Abdelhadi Akharif; Amal Mellouk; Marc Hallin
  4. Inference after Model Averaging in Linear Regression Models By Xinyu Zhang; Chu-An Liu
  5. Coherent Probabilistic Forecasts for Hierarchical Time Series By Souhaib Ben Taieb; James W. Taylor; Rob J. Hyndman
  6. Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS By Marco Valerio Geraci; Jean-Yves Gnabo
  7. The transmission of monetary policy shocks By Miranda-Agrippino, Silvia; Ricco, Giovanni
  8. Attrition in Randomized Control Trials: Using tracking information to correct bias By Macours, Karen; Molina Millan, Teresa

  1. By: Ban Kheng Tan; Anastasios Panagiotelis; George Athanasopoulos
    Abstract: We develop efficient Bayesian inference for the 1-factor copula model with two significant contributions over classical inference. First, our approach leads to straightforward inference on the latent factor since iterates of the latent factor are generated as a by-product in the proposed Markov chain Monte Carlo algorithm. In contrast, there is no known classical approach for inference on the latents. Second, by developing a reversible jump Markov chain Monte Carlo scheme, we are able to select or average over factor copula specifications that are constructed from a large set of candidate parametric bivariate copula building blocks. Our approach can accommodate margins that are discrete, continuous or a combination of both. Through extensive simulations multiple schemes are compared on the basis of computational and Monte Carlo efficiency. The preferred schemes provide reliable inference on all parameters including the latent factor and model space. The potential of the proposed methodology is highlighted in an empirical study of ten binary variables measuring the multidimensional nature of poverty collected for 11463 East Timorese households. We construct a poverty index using estimates of the latent factor. Compared to a classical analysis, our method yields better out-of-sample fit and uncovers a variety of flexible relationships between the latent measure and observed variables by averaging over a diverse set of copulas.
    Keywords: Model averaging, reversible jump MCMC, vine copulas, dimension reduction, multidimensional poverty index.
    JEL: C11 C15 C32 C58 C63
    Date: 2017
  2. By: Isaiah Andrews; Maximilian Kasy
    Abstract: Some empirical results are more likely to be published than others. Such selective publication leads to biased estimators and distorted inference. This paper proposes two approaches for identifying the conditional probability of publication as a function of a study's results, the first based on systematic replication studies and the second based on meta-studies. For known conditional publication probabilities, we propose median-unbiased estimators and associated confidence sets that correct for selective publication. We apply our methods to recent large-scale replication studies in experimental economics and psychology, and to meta-studies of the effects of minimum wages and de-worming programs.
    JEL: C12 C13 C18
    Date: 2017–03
  3. By: Mohamed Fihri; Abdelhadi Akharif; Amal Mellouk; Marc Hallin
    Abstract: Locally asymptotically optimal (in the Hajek-Le Cam sense) pseudo-Gaussian and rank-based procedures for detecting randomness of coefficients in linear regression models are proposed. The parametric and semiparametric efficiency properties of those procedures are investigated. Their asymptotic relative efficiencies (with respect to the pseudo-Gaussian procedure) turns out to be be huge under heavy-tailed and skewed densities, stressing the importance of an adequate choice of scores. Simulations demonstrate the excellent finite-sample performances of a class of rank-based procedures based on data-driven scores.
    Keywords: local Asymptotic normality; optimal tests; pseudo-gaussian test; semiparametric efficiency; rank tests; random coefficient regression model
    Date: 2017–04
  4. By: Xinyu Zhang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences); Chu-An Liu (Institute of Economics, Academia Sinica, Taipei, Taiwan)
    Abstract: This paper considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distributions of MMA and JMA estimators and propose a simulation-based confidence interval for the least squares averaging estimator. Monte Carlo simulations show that the coverage probabilities of proposed confidence intervals achieve the nominal level. JEL Calssification: C51, C52
    Keywords: Confidence intervals, Inference post-model-averaging, Jackknife model averaging, Mallows model averaging
    Date: 2017–05
  5. By: Souhaib Ben Taieb; James W. Taylor; Rob J. Hyndman
    Abstract: Many applications require forecasts for a hierarchy comprising a set of time series along with aggregates of subsets of these series. Although forecasts can be produced independently for each series in the hierarchy, typically this does not lead to coherent forecasts -- the property that forecasts add up appropriately across the hierarchy. State-of-the-art hierarchical forecasting methods usually reconcile the independently generated forecasts to satisfy the aggregation constraints. A fundamental limitation of prior research is that it has considered only the problem of forecasting the mean of each time series. We consider the situation where probabilistic forecasts are needed for each series in the hierarchy. We define forecast coherency in this setting, and propose an algorithm to compute predictive distributions for each series in the hierarchy. Our algorithm has the advantage of synthesizing information from different levels in the hierarchy through a sparse forecast combination and a probabilistic hierarchical aggregation. We evaluate the accuracy of our forecasting algorithm on both simulated data and large-scale electricity smart meter data. The results show consistent performance gains compared to state-of-the art methods.
    Keywords: forecast combination, probabilistic forecast, copula, machine learning.; Forecast combination, probabilistic forecast, copula, machine learning
    JEL: C53 Q47 C32
    Date: 2017
  6. By: Marco Valerio Geraci; Jean-Yves Gnabo
    Abstract: In this paper we propose a time-varying parameter framework to estimate the dynamic network of financial spillovers. In a series of simulation exercises, we show that our framework performs better than the classical approach based on Granger causality testing over rolling windows. We apply it to all financial stocks listed in the S&P 500 and uncover a gradual decrease in interconnectedness after the crisis, which is not observable using the rolling window approach. We show that this is because the rolling window results are highly sensitive to crisis observations.
    Keywords: financial interconnectedness; time-varying parameter; granger causality
    JEL: G10 G18 C32 C51 C63
    Date: 2015–12
  7. By: Miranda-Agrippino, Silvia (Bank of England); Ricco, Giovanni (University of Warwick)
    Abstract: Despite years of research, there is still uncertainty around the effects of monetary policy shocks. We reassess the empirical evidence by combining a new identification that accounts for informational rigidities, with a flexible econometric method robust to misspecifications that bridges between VARs and Local Projections. We show that most of the lack of robustness of the results in the extant literature is due to compounding unrealistic assumptions of full information with the use of severely misspecified models. Using our novel methodology, we find that a monetary tightening is unequivocally contractionary, with no evidence of either price or output puzzles.
    Keywords: Monetary policy; local projections; VARs; expectations; information rigidity; survey forecasts; external instruments
    JEL: C11 C14 E52 G14
    Date: 2017–04–21
  8. By: Macours, Karen; Molina Millan, Teresa
    Abstract: This paper starts from a review of RCT studies in development economics, and documents many studies largely ignore attrition once attrition rates are found balanced between treatment arms. The paper analyzes the implications of attrition for the internal and external validity of the results of a randomized experiment with balanced attrition rates, and proposes a new method to correct for attrition bias. We rely on a 10-years longitudinal data set with a final attrition rate of 10 percent, obtained after intensive tracking of migrants, and document the sensitivity of ITT estimates for schooling gains and labour market outcomes for a social program in Nicaragua. We find that not including those found during the intensive tracking leads to an overestimate of the ITT effects for the target population by more than 35 percent, and that selection into attrition is driven by observable baseline characteristics. We propose to correct for attrition using inverse probability weighting with estimates of weights that exploit the similarities between missing individuals and those found during an intensive tracking phase. We compare these estimates with alternative strategies using regression adjustment, standard weights, bounds or proxy information.
    Keywords: attrition; long-term evaluation; randomized control trials
    JEL: C31 C81 C93 O12
    Date: 2017–04

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