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Book part
Publication date: 19 December 2012

George G. Judge and Ron C. Mittelhammer

In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss…

Abstract

In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide between members of the relevant family of econometric models and demonstrates, under quadratic loss, the nonoptimality of the conventional pretest estimator. First-order asymptotic properties of the combined estimator are demonstrated. A sampling study is used to illustrate finite sample performance over a range of econometric model sampling designs that includes performance relative to a Hausman-type model selection pretest estimator. An important empirical problem from the causal effects literature is analyzed to indicate the applicability and econometric implications of the methodology. This combining estimation and inference framework can be extended to a range of models and corresponding estimators. The combining estimator is novel in that it provides directly minimum quadratic loss solutions.

Article
Publication date: 16 October 2020

Julia S. Mehlitz and Benjamin R. Auer

Motivated by the growing importance of the expected shortfall in banking and finance, this study aims to compare the performance of popular non-parametric estimators of the…

Abstract

Purpose

Motivated by the growing importance of the expected shortfall in banking and finance, this study aims to compare the performance of popular non-parametric estimators of the expected shortfall (i.e. different variants of historical, outlier-adjusted and kernel methods) to each other, selected parametric benchmarks and estimates based on the idea of forecast combination.

Design/methodology/approach

Within a multidimensional simulation setup (spanned by different distributional settings, sample sizes and confidence levels), the authors rank the estimators based on classic error measures, as well as an innovative performance profile technique, which the authors adapt from the mathematical programming literature.

Findings

The rich set of results supports academics and practitioners in the search for an answer to the question of which estimators are preferable under which circumstances. This is because no estimator or combination of estimators ranks first in all considered settings.

Originality/value

To the best of their knowledge, the authors are the first to provide a structured simulation-based comparison of non-parametric expected shortfall estimators, study the effects of estimator averaging and apply the mentioned profiling technique in risk management.

Details

The Journal of Risk Finance, vol. 21 no. 4
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 29 June 2020

Jian Zhou

This study aims to show that the best-performing realized measures vary across markets when it comes to forecast real estate investment trust (REIT) volatility. This finding…

Abstract

Purpose

This study aims to show that the best-performing realized measures vary across markets when it comes to forecast real estate investment trust (REIT) volatility. This finding provides little guidance for practitioners on which one to use when facing a new market. The authors attempt to fill the hole by seeking a common estimator, which can study for different markets.

Design/methodology/approach

The authors do so by drawing upon the general forecasting literature, which finds that combinations of individual forecasts often outperform even the best individual forecast. The authors carry out the study by first introducing a number of commonly used realized measures and then considering several different combination strategies. The authors apply all of the individual measures and their different combinations to three major global REIT markets (Australia, UK and US).

Findings

The findings show that both unconstrained and constrained versions of the regression-based combinations consistently rank among the group of best forecasters across the three markets under study. None of their peers can do it including the three simple combinations and all of the individual measures. The conclusions are robust to the choice of evaluation metrics and of the out-of-sample evaluation periods.

Originality/value

The study provides practitioners with easy-to-follow insights on how to forecast REIT volatility, that is, use a regression-based combination of individual realized measures. The study has also extended the thin real estate literature on using high-frequency data to examine REIT volatility.

Details

Journal of European Real Estate Research , vol. 14 no. 1
Type: Research Article
ISSN: 1753-9269

Keywords

Book part
Publication date: 16 December 2009

Peter Bearse and Paul Rilstone

A new, direct method is developed for reducing, to an arbitrary order, the boundary bias of kernel density and density derivative estimators. The basic asymptotic properties of…

Abstract

A new, direct method is developed for reducing, to an arbitrary order, the boundary bias of kernel density and density derivative estimators. The basic asymptotic properties of the estimators are derived. Simple examples are provided. A number of simulations are reported, which demonstrate the viability and efficacy of the approach compared to several popular alternatives.

Details

Nonparametric Econometric Methods
Type: Book
ISBN: 978-1-84950-624-3

Book part
Publication date: 4 August 2017

Camilla Jensen

Past research suggests that a financial crisis event has a dual and ambiguous effect on the exporting strategy of subsidiaries of multinational firms in a value chain and…

Abstract

Past research suggests that a financial crisis event has a dual and ambiguous effect on the exporting strategy of subsidiaries of multinational firms in a value chain and offshoring perspective. From a total volume perspective exports are expected to contract due to a decline in demand (demand shock) from other subsidiaries downstream in the value chain. While in a comparative perspective multinational subsidiaries are found to perform relatively better than local firms that are integrated differently (arms’ length) in global production networks (e.g., offshoring outsourcing). This chapter tries to reconcile these findings by testing a number of hypothesis about global integration strategies in the context of the Global Financial Crisis (GFC) and how it affected exporting among multinational subsidiaries operating out of Turkey. Controlling for the impact that exchange rate depreciations and volatility has on firm-level exports the study shows that the particular global event studied had no additional impact on individual firms’ exports. Since multinational subsidiaries are more insulated from these effects they are able to expand rather than contract their global integration strategies throughout the course of the GFC.

Details

Breaking up the Global Value Chain
Type: Book
ISBN: 978-1-78743-071-6

Keywords

Book part
Publication date: 23 June 2016

Yulia Kotlyarova, Marcia M. A. Schafgans and Victoria Zinde-Walsh

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the…

Abstract

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the leading term in the expansion of the bias may provide a poor approximation. We explore the relation between smoothness and bias and provide estimators for the degree of the smoothness and the bias. We demonstrate the existence of a linear combination of estimators whose trace of the asymptotic mean-squared error is reduced relative to the individual estimator at the optimal bandwidth. We examine the finite-sample performance of a combined estimator that minimizes the trace of the MSE of a linear combination of individual kernel estimators for a multimodal density. The combined estimator provides a robust alternative to individual estimators that protects against uncertainty about the degree of smoothness.

Details

Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

Book part
Publication date: 18 January 2022

Tae-Hwy Lee, Shahnaz Parsaeian and Aman Ullah

Hashem Pesaran has made many seminal contributions, among others, in the time series econometrics estimation and forecasting under structural break, see Pesaran and Timmermann

Abstract

Hashem Pesaran has made many seminal contributions, among others, in the time series econometrics estimation and forecasting under structural break, see Pesaran and Timmermann (2005, 2007), Pesaran, Pettenuzzo, and Timmermann (2006), and Pesaran, Pick, and Pranovich (2013). In this chapter, the authors focus on the estimation of regression parameters under multiple structural breaks with heteroskedasticity across regimes. The authors propose a combined estimator of regression parameters based on combining restricted estimator under the situation that there is no break in the parameters, with unrestricted estimator under the break. The operational optimal combination weight is between zero and one. The analytical finite sample risk is derived, and it is shown that the risk of the proposed combined estimator is lower than that of the unrestricted estimator under any break size and break points. Further, the authors show that the combined estimator outperforms over the unrestricted estimator in terms of the mean squared forecast errors. Properties of the estimator are also demonstrated in simulations. Finally, empirical illustrations for parameter estimators and forecasts are presented through macroeconomic and financial data sets.

Details

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

Keywords

Book part
Publication date: 19 December 2012

John C. Chao, Jerry A. Hausman, Whitney K. Newey, Norman R. Swanson and Tiemen Woutersen

This chapter shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments…

Abstract

This chapter shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments. These estimators, called HFUL (Heteroscedasticity robust Fuller) and HLIM (Heteroskedasticity robust limited information maximum likelihood (LIML)) were introduced by Hausman, Newey, Woutersen, Chao, and Swanson (2012), but without derivation. Combining consistent estimators is a theme that is associated with Jerry Hausman and, therefore, we present this derivation in this volume. Additionally, and in order to further understand and interpret HFUL and HLIM in the context of jackknife type variance ratio estimators, we show that a new variant of HLIM, under specific grouped data settings with dummy instruments, simplifies to the Bekker and van der Ploeg (2005) MM (method of moments) estimator.

Details

Essays in Honor of Jerry Hausman
Type: Book
ISBN: 978-1-78190-308-7

Keywords

Book part
Publication date: 23 June 2016

Daniel J. Henderson and Christopher F. Parmeter

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also…

Abstract

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

Details

Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

Book part
Publication date: 5 April 2024

Hung-pin Lai

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic…

Abstract

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

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