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Book part
Publication date: 19 December 2012

Eric Hillebrand and Tae-Hwy Lee

We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the…

Abstract

We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the Stein-rule estimator of Hill and Judge (1987) that shrinks the unrestricted unbiased ordinary least squares (OLS) estimator toward a restricted biased principal component (PC) estimator. Since the Stein-rule estimator combines the OLS and PC estimators, it is a model-averaging estimator and produces a combined forecast. The conditions under which the improvement can be achieved depend on several unknown parameters that determine the degree of the Stein-rule shrinkage. We conduct Monte Carlo simulations to examine these parameter regions. The overall picture that emerges is that the Stein-rule shrinkage estimator can dominate both OLS and principal components estimators within an intermediate range of the signal-to-noise ratio. If the signal-to-noise ratio is low, the PC estimator is superior. If the signal-to-noise ratio is high, the OLS estimator is superior. In out-of-sample forecasting with AR(1) predictors, the Stein-rule shrinkage estimator can dominate both OLS and PC estimators when the predictors exhibit low persistence.

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30th Anniversary Edition
Type: Book
ISBN: 978-1-78190-309-4

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Book part
Publication date: 30 April 2008

Jae J. Lee

Many economic and business problems require a set of random variates from the posterior density of the unknown parameters. The set of random variates can be used to integrate…

Abstract

Many economic and business problems require a set of random variates from the posterior density of the unknown parameters. The set of random variates can be used to integrate numerically many forms of functions. Since a closed form of the posterior density of models in time series analysis is not usually well known, it is not easy to generate a set of random variates. As a sampling scheme based on the probabilities proportional to sizes of the sample space, sampling importance resampling (SIR) method can be applied to generate a set of random variates from the posterior density. Application of SIR to signal extraction model of time series analysis is illustrated and given a set of random variates, the procedures to compute the Monte Carlo estimator of the component of signal extraction model are discussed. The procedures are illustrated with simulated data.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-787-2

Book part
Publication date: 24 April 2023

Nikolay Gospodinov, Alex Maynard and Elena Pesavento

It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the…

Abstract

It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the conventional cointegration tests may suggest lack of cointegration. This chapter studies the possibility of having cointegrated errors that are characterized simultaneously by high persistence (near-unit root behavior) and very small (near zero) variance. The proposed dual parameterization induces the cointegration error process to be stochastically bounded which prevents the variables in the cointegrating system from drifting apart over a reasonably long horizon. More specifically, this chapter develops the appropriate asymptotic theory (rate of convergence and asymptotic distribution) for the estimators in unconditional and conditional vector error correction models (VECM) when the error correction term is parameterized as a dampened near-unit root process (local-to-unity process with local-to-zero variance). The important differences in the limiting behavior of the estimators and their implications for empirical analysis are discussed. Simulation results and an empirical analysis of the forward premium regressions are also provided.

Book part
Publication date: 21 December 2010

Tong Zeng and R. Carter Hill

In this paper we use Monte Carlo sampling experiments to examine the properties of pretest estimators in the random parameters logit (RPL) model. The pretests are for the presence…

Abstract

In this paper we use Monte Carlo sampling experiments to examine the properties of pretest estimators in the random parameters logit (RPL) model. The pretests are for the presence of random parameters. We study the Lagrange multiplier (LM), likelihood ratio (LR), and Wald tests, using conditional logit as the restricted model. The LM test is the fastest test to implement among these three test procedures since it only uses restricted, conditional logit, estimates. However, the LM-based pretest estimator has poor risk properties. The ratio of LM-based pretest estimator root mean squared error (RMSE) to the random parameters logit model estimator RMSE diverges from one with increases in the standard deviation of the parameter distribution. The LR and Wald tests exhibit properties of consistent tests, with the power approaching one as the specification error increases, so that the pretest estimator is consistent. We explore the power of these three tests for the random parameters by calculating the empirical percentile values, size, and rejection rates of the test statistics. We find the power of LR and Wald tests decreases with increases in the mean of the coefficient distribution. The LM test has the weakest power for presence of the random coefficient in the RPL model.

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Maximum Simulated Likelihood Methods and Applications
Type: Book
ISBN: 978-0-85724-150-4

Book part
Publication date: 19 December 2012

R. Kelley Pace, James P. LeSage and Shuang Zhu

Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that the bias…

Abstract

Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that the bias in β from applying OLS to a regressand generated from a spatial autoregressive process was exacerbated by spatial dependence in the regressor. Also, the marginal likelihood function or restricted maximum likelihood (REML) function includes a determinant term involving the regressors. Therefore, high dependence in the regressor may affect the likelihood through this term. In addition, Bowden and Turkington (1984) showed that regressor temporal autocorrelation had a non-monotonic effect on instrumental variable estimators.

We provide empirical evidence that many common economic variables used as regressors (e.g., income, race, and employment) exhibit high levels of spatial dependence. Based on this observation, we conduct a Monte Carlo study of maximum likelihood (ML), REML and two instrumental variable specifications for spatial autoregressive (SAR) and spatial Durbin models (SDM) in the presence of spatially correlated regressors.

Findings indicate that as spatial dependence in the regressor rises, REML outperforms ML and that performance of the instrumental variable methods suffer. The combination of correlated regressors and the SDM specification provides a challenging environment for instrumental variable techniques.

We also examine estimates of marginal effects and show that these behave better than estimates of the underlying model parameters used to construct marginal effects estimates. Suggestions for improving design of Monte Carlo experiments are provided.

Book part
Publication date: 1 October 2014

Nazmi Demir, Syed F. Mahmud and M. Nihat Solakoglu

This study searches for sentimental herding in Borsa Istanbul (BIST) during the last decade using a state-space model employing cross-section standard deviations of systematic…

Abstract

This study searches for sentimental herding in Borsa Istanbul (BIST) during the last decade using a state-space model employing cross-section standard deviations of systematic risk (Beta). It has been found that herding toward the market in the BIST-100 is both statistically significant and persistent independently from market fundamentals such as the volatility of returns and the levels of market returns. Herding trends over the sample period indicate that the financial crisis in 2000–2001 appeared to bring about sentimental herding in BIST which was followed by a calm period during which investors turned to fundamentals. Thereafter, we observe a volatile adverse herding pattern till the end of 2011 due to the confusing environment caused by the internal and external events.

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Risk Management Post Financial Crisis: A Period of Monetary Easing
Type: Book
ISBN: 978-1-78441-027-8

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Content available
Book part
Publication date: 13 March 2023

Abstract

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Artificial Intelligence in Marketing
Type: Book
ISBN: 978-1-80262-875-3

Book part
Publication date: 21 September 2022

Dmitrij Celov and Mariarosaria Comunale

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of

Abstract

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

Book part
Publication date: 2 February 2023

Angi Martin and Julie Cox

With a push toward inclusion of students with disabilities in the general education classroom, students who are d/Deaf or hard of hearing (DHH) are exposed to greater educational…

Abstract

With a push toward inclusion of students with disabilities in the general education classroom, students who are d/Deaf or hard of hearing (DHH) are exposed to greater educational opportunities. Given the largely verbal nature of traditional classroom instruction, there has been a need for advancements in technology to provide more access to the material covered by teachers and in class discussions. In addition, the COVID-19 pandemic and the transition to virtual learning also brought to light many additional challenges for the DHH population, which can, in part, be lessened through technological advancements.

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Using Technology to Enhance Special Education
Type: Book
ISBN: 978-1-80262-651-3

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Book part
Publication date: 29 August 2005

Paul J. Hanges and Julie S. Lyon

In this chapter, we discuss Cohen and Doveh's (this volume) proposed protocol for testing differences in intra-class correlation coefficients (ICCs). We believe that there are…

Abstract

In this chapter, we discuss Cohen and Doveh's (this volume) proposed protocol for testing differences in intra-class correlation coefficients (ICCs). We believe that there are many research questions that can be addressed by this procedure. We provide several potential examples of using this procedure at the individual, group, and organizational/society levels of analysis. We do, however, raise concerns about interpreting the ICC as an index of within-group homogeneity.

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Multi-Level Issues in Strategy and Methods
Type: Book
ISBN: 978-1-84950-330-3

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