Consistent Variance of the Laplace‐type Estimators: Application to Dsge Models

Kormilitsina, Anna; Nekipelov, Denis

doi:10.1111/iere.12169

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…Then we have

Σ = A_{0} A_{0}^{'}

and

B_{j} A_{0} = A_{j}

, which in turn can be written as

g false(γ, θ false) = 0

, where γ is a vector that consists of the elements of

B_{j}

's and the distinct elements of Σ and θ is a vector of DSGE parameters. In the second approach, one can match moments (e.g., Kormilitsina and Nekipelov (), Andreasen, Fernández‐Villaverde, and Rubio‐Ramírez ( )).…”

Section: Discussionmentioning

confidence: 99%

“…While our model selection depends on the choice of weighting matrices, if one is to calculate standard errors from MCMC draws,

{\overset{W}{true}}_{T}

needs to be set to the inverse of a consistent estimator of the asymptotic covariance matrix of

{\overset{γ}{true}}_{T}

, which eliminates the arbitrariness of the choice of the weighting matrix. When the optimal weighting matrix is not used, the formula in Chernozhukov and Hong ( ) and Kormilitsina and Nekipelov () should be used to calculate standard errors.…”

Section: Asymptotic Theorymentioning

confidence: 99%

“…IRF matching can also be used even when shocks are fewer than observed variables (see Fernández‐Villaverde, Rubio‐Ramírez and Schorfheide ( , p. 686)). Other applications of LTEs to estimate macroeconomic models include Christiano, Eichenbaum, and Trabandt (), Kormilitsina and Nekipelov (), Gemma, Kurozumi, and Shintani (), and Miyamoto and Nguyen ( ).…”

Section: Introductionmentioning

confidence: 99%

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Quasi-Bayesian model selection

Inoue¹,

Shintani²

2018

Quantitative Economics

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In this paper, we establish the consistency of the model selection criterion based on the quasi‐marginal likelihood (QML) obtained from Laplace‐type estimators. We consider cases in which parameters are strongly identified, weakly identified and partially identified. Our Monte Carlo results confirm our consistency results. Our proposed procedure is applied to select among New Keynesian macroeconomic models using US data.

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“…Then we have

Σ = A_{0} A_{0}^{'}

and

B_{j} A_{0} = A_{j}

, which in turn can be written as

g false(γ, θ false) = 0

, where γ is a vector that consists of the elements of

B_{j}

Section: Discussionmentioning

confidence: 99%

“…While our model selection depends on the choice of weighting matrices, if one is to calculate standard errors from MCMC draws,

{\overset{W}{true}}_{T}

needs to be set to the inverse of a consistent estimator of the asymptotic covariance matrix of

{\overset{γ}{true}}_{T}

Section: Asymptotic Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quasi-Bayesian model selection

Inoue¹,

Shintani²

2018

Quantitative Economics

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show abstract

“…In the follow-up work in [29] for the cases where Q(θ ; P N ) may be steep in the vicinity of the maximum, which may lead to slow convergence of the simulations required to sample from the quasiposterior, it is proposed to scale the exponent in the pseudo-likelihood function as exp (λ Q(θ ; P N )) using a constant λ which is selected based on the speed of mixing of the simulated Markov Chain (produced using the new pseudo-posterior). The corresponding posterior mean remains a consistent estimator for the maximizer of the population objective function while its asymptotic variance can be estimated by scaling the variance of the quasi-posterior using λ.…”

Section: Smoothness and Separability Of Differentially Private Estima...mentioning

confidence: 99%

“…[11] and later [29] focus on the cases where Q(θ ; P N ) is stochastically equicontinuous and the quasiposterior is asymptotically equivalent to…”

Section: Smoothness and Separability Of Differentially Private Estima...mentioning

confidence: 99%

Identification and Formal Privacy Guarantees

Komarova¹,

Nekipelov²

2020

Preprint

Self Cite

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Empirical economic research crucially relies on highly sensitive individual datasets. At the same time, increasing availability of public individual-level data that comes from social networks, public government records and directories makes it possible for adversaries to potentially de-identify anonymized records in sensitive research datasets. This increasing disclosure risk has incentivised large data curators, most notably the US Census bureau and several large companies including Apple, Facebook and Microsoft to look for algorithmic solutions to provide formal non-disclosure guarantees for their secure data. The most commonly accepted formal data security concept in the Computer Science community is referred to as differential privacy. Differential privacy restricts the interaction of the researcher with the data by allowing her to issue queries that evaluate the functions of the data. The differential privacy mechanism then replaces the actual outcome of the query with a randomised outcome with the amount of randomness determined by the sensitivity of the outcome to individual observations in the data.While differential privacy does provide formal data security guarantees, its impact on the identification of empirical economic models as well as on the performance of estimators in nonlinear empirical Econometric models has not been sufficiently studied. Since privacy protection mechanisms are inherently finite-sample procedures, we define the notion of identifiability of the parameter of interest as a property of the limit of experiments. It is naturally characterized by concepts from the random sets theory and is linked to the asymptotic behavior in measure of differentially private estimators.We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy. Under differential privacy their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently. This result is clearly supported by our simulation evidence. Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy.

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Solution and Estimation Methods for DSGE Models

Fernández-Villaverde

Ramírez

Schorfheide

2016

Handbook of Macroeconomics

155

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provided excellent research assistance. We thank our discussant Serguei Maliar, the editors John Taylor and Harald Uhlig, John Cochrane, and the participants at the Handbook Conference hosted by the Hoover Institution for helpful comments and suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Consistent Variance of the Laplace‐type Estimators: Application to Dsge Models

Cited by 8 publications

References 37 publications

Quasi-Bayesian model selection

Quasi-Bayesian model selection

Identification and Formal Privacy Guarantees

Solution and Estimation Methods for DSGE Models

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