Special Issue "Resampling Methods in Econometrics"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (15 December 2018).

Special Issue Editors

Jean-Marie Dufour
Website
Guest Editor
Department of Economics, McGill University, Montreal, Quebec H3A 2T7, Canada
Interests: finite sample inference; identification-robust inference; simulation-based econometric methods; dynamic macroeconomic modelling; structural macroeconomics and finance
Lynda A. Khalaf
Website
Guest Editor
Economics Department, Carleton University, Ottawa, Ontario, K1S 5B6 Canada
Interests: simulation-based econometric methods; irregular inference methods; financial econometrics; macro-economic and environmental econometrics

Special Issue Information

Dear Colleagues,

This Special Issue aims at gathering contributions on resampling and simulation-based estimation and inference in econometrics. These include: Methodological contributions to the underlying econometric and statistical theory; empirical work demonstrating that resampling-based methods can change our understanding of important economic issues; and simulation studies for uncovering undocumented consequential issues with standard methods that can be solved using resampling. Relevant specific topics include: Various forms of bootstrapping, Monte Carlo test methods, permutation-based methods, indirect inference and other forms of simulation-based estimation, simulation-based sequential testing, resampling-based model averaging/cross-validation, and innovative empirical applications of resampling methods.

While the scope of this Special Issue will not be restricted to these topics, we welcome contributions that underscore the usefulness of resampling in: (i) relatively small samples as occurs for example in macroeconomics; (ii) situations where identification may fail and other irregular settings; (iii) multiple testing and simultaneous inference problems; (iv) the analysis of rare events; and (v) forecasting.

Prof. Jean-Marie Dufour
Prof. Lynda A. Khalaf
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Simulation-based estimation
  • Simulation-based Inference
  • Bootstrap
  • Monte Carlo tests
  • Resampling-based specification/cross-validation

Published Papers (6 papers)

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Research

Open AccessFeature PaperArticle
Simultaneous Indirect Inference, Impulse Responses and ARMA Models
Econometrics 2020, 8(2), 12; https://doi.org/10.3390/econometrics8020012 - 02 Apr 2020
Abstract
A two-stage simulation-based framework is proposed to derive Identification Robust confidence sets by applying Indirect Inference, in the context of Autoregressive Moving Average (ARMA) processes for finite samples. Resulting objective functions are treated as test statistics, which are inverted rather than optimized, via [...] Read more.
A two-stage simulation-based framework is proposed to derive Identification Robust confidence sets by applying Indirect Inference, in the context of Autoregressive Moving Average (ARMA) processes for finite samples. Resulting objective functions are treated as test statistics, which are inverted rather than optimized, via the Monte Carlo test method. Simulation studies illustrate accurate size and good power. Projected impulse-response confidence bands are simultaneous by construction and exhibit robustness to parameter identification problems. The persistence of shocks on oil prices and returns is analyzed via impulse-response confidence bands. Our findings support the usefulness of impulse-responses as an empirically relevant transformation of the confidence set. Full article
(This article belongs to the Special Issue Resampling Methods in Econometrics)
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Open AccessArticle
Optimal Multi-Step-Ahead Prediction of ARCH/GARCH Models and NoVaS Transformation
Econometrics 2019, 7(3), 34; https://doi.org/10.3390/econometrics7030034 - 08 Aug 2019
Abstract
This paper gives a computer-intensive approach to multi-step-ahead prediction of volatility in financial returns series under an ARCH/GARCH model and also under a model-free setting, namely employing the NoVaS transformation. Our model-based approach only assumes i . i . d innovations without requiring [...] Read more.
This paper gives a computer-intensive approach to multi-step-ahead prediction of volatility in financial returns series under an ARCH/GARCH model and also under a model-free setting, namely employing the NoVaS transformation. Our model-based approach only assumes i . i . d innovations without requiring knowledge/assumption of the error distribution and is computationally straightforward. The model-free approach is formally quite similar, albeit a GARCH model is not assumed. We conducted a number of simulations to show that the proposed approach works well for both point prediction (under L 1 and/or L 2 measures) and prediction intervals that were constructed using bootstrapping. The performance of GARCH models and the model-free approach for multi-step ahead prediction was also compared under different data generating processes. Full article
(This article belongs to the Special Issue Resampling Methods in Econometrics)
Open AccessArticle
Monte Carlo Inference on Two-Sided Matching Models
Econometrics 2019, 7(1), 16; https://doi.org/10.3390/econometrics7010016 - 26 Mar 2019
Abstract
This paper considers two-sided matching models with nontransferable utilities, with one side having homogeneous preferences over the other side. When one observes only one or several large matchings, despite the large number of agents involved, asymptotic inference is difficult because the observed matching [...] Read more.
This paper considers two-sided matching models with nontransferable utilities, with one side having homogeneous preferences over the other side. When one observes only one or several large matchings, despite the large number of agents involved, asymptotic inference is difficult because the observed matching involves the preferences of all the agents on both sides in a complex way, and creates a complicated form of cross-sectional dependence across observed matches. When we assume that the observed matching is a consequence of a stable matching mechanism with homogeneous preferences on one side, and the preferences are drawn from a parametric distribution conditional on observables, the large observed matching follows a parametric distribution. This paper shows in such a situation how the method of Monte Carlo inference can be a viable option. Being a finite sample inference method, it does not require independence or local dependence among the observations which are often used to obtain asymptotic validity. Results from a Monte Carlo simulation study are presented and discussed. Full article
(This article belongs to the Special Issue Resampling Methods in Econometrics)
Open AccessArticle
Indirect Inference: Which Moments to Match?
Econometrics 2019, 7(1), 14; https://doi.org/10.3390/econometrics7010014 - 19 Mar 2019
Cited by 1
Abstract
The standard approach to indirect inference estimation considers that the auxiliary parameters, which carry the identifying information about the structural parameters of interest, are obtained from some recently identified vector of estimating equations. In contrast to this standard interpretation, we demonstrate that the [...] Read more.
The standard approach to indirect inference estimation considers that the auxiliary parameters, which carry the identifying information about the structural parameters of interest, are obtained from some recently identified vector of estimating equations. In contrast to this standard interpretation, we demonstrate that the case of overidentified auxiliary parameters is both possible, and, indeed, more commonly encountered than one may initially realize. We then revisit the “moment matching” and “parameter matching” versions of indirect inference in this context and devise efficient estimation strategies in this more general framework. Perhaps surprisingly, we demonstrate that if one were to consider the naive choice of an efficient Generalized Method of Moments (GMM)-based estimator for the auxiliary parameters, the resulting indirect inference estimators would be inefficient. In this general context, we demonstrate that efficient indirect inference estimation actually requires a two-step estimation procedure, whereby the goal of the first step is to obtain an efficient version of the auxiliary model. These two-step estimators are presented both within the context of moment matching and parameter matching. Full article
(This article belongs to the Special Issue Resampling Methods in Econometrics)
Open AccessArticle
Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence
Econometrics 2019, 7(1), 13; https://doi.org/10.3390/econometrics7010013 - 13 Mar 2019
Abstract
Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the jump intensity parameter [...] Read more.
Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the jump intensity parameter in the data generating process, and which are consistent. In this paper, long span jump tests are compared and contrasted with a variety of fixed span jump tests in a series of Monte Carlo experiments. It is found that both the long time span tests of Corradi et al. (2018) and the fixed span tests of Aït-Sahalia and Jacod (2009) exhibit reasonably good finite sample properties, for time spans both short and long. Various other tests suffer from finite sample distortions, both under sequential testing and under long time spans. The latter finding is new, and confirms the “pitfall” discussed in Huang and Tauchen (2005), of using asymptotic approximations associated with finite time span tests in order to study long time spans of data. An empirical analysis is carried out to investigate the implications of these findings, and “time-span robust” tests indicate that the prevalence of jumps is not as universal as might be expected. Full article
(This article belongs to the Special Issue Resampling Methods in Econometrics)
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Open AccessArticle
On the Validity of Tests for Asymmetry in Residual-Based Threshold Cointegration Models
Econometrics 2019, 7(1), 12; https://doi.org/10.3390/econometrics7010012 - 13 Mar 2019
Abstract
This paper investigates the properties of tests for asymmetric long-run adjustment which are often applied in empirical studies on asymmetric price transmissions. We show that substantial size distortions are caused by preconditioning the test on finding sufficient evidence for cointegration in a first [...] Read more.
This paper investigates the properties of tests for asymmetric long-run adjustment which are often applied in empirical studies on asymmetric price transmissions. We show that substantial size distortions are caused by preconditioning the test on finding sufficient evidence for cointegration in a first step. The extent of oversizing the test for long-run asymmetry depends inversely on the power of the primary cointegration test. Hence, tests for long-run asymmetry become invalid in cases of small sample sizes or slow speed of adjustment. Further, we provide simulation evidence that tests for long-run asymmetry are generally oversized if the threshold parameter is estimated by conditional least squares and show that bootstrap techniques can be used to obtain the correct size. Full article
(This article belongs to the Special Issue Resampling Methods in Econometrics)
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