Search results

1 – 10 of over 5000
To view the access options for this content please click here
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…

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.

Details

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

To view the access options for this content please click here
Article
Publication date: 11 November 2014

Rick L. Andrews and Peter Ebbes

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models

Abstract

Purpose

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models occur when certain factors, unobserved by the researcher, affect both demand and the values of a marketing mix variable set by managers. For example, unobserved factors such as style, prestige or reputation might result in higher prices for a product and higher demand for that product. If not addressed properly, endogeneity can bias the elasticities of the endogenous variable and subsequent optimization of the marketing mix. In practice, instrumental variables (IV) estimation techniques are often used to remedy an endogeneity problem. It is well-known that, for linear regression models, the use of IV techniques with poor-quality instruments can produce very poor parameter estimates, in some circumstances even worse than those that result from ignoring the endogeneity problem altogether. The literature has not addressed the consequences of using poor-quality instruments to remedy endogeneity problems in non-linear models, such as logit-based demand models.

Design/methodology/approach

Using simulation methods, the authors investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models applied to finite-sample data sets. The results show that, even when the conditions for lack of parameter identification due to poor-quality instruments do not hold exactly, estimates of price elasticities can still be quite poor. That being the case, the authors investigate the relative performance of several non-linear IV estimation procedures utilizing readily available instruments in finite samples.

Findings

The study highlights the attractiveness of the control function approach (Petrin and Train, 2010) and readily available instruments, which together reduce the mean squared elasticity errors substantially for experimental conditions in which the theory-backed instruments are poor in quality. The authors find important effects for sample size, in particular for the number of brands, for which it is shown that endogeneity problems are exacerbated with increases in the number of brands, especially when poor-quality instruments are used. In addition, the number of stores is found to be important for likelihood ratio testing. The results of the simulation are shown to generalize to situations under Nash pricing in oligopolistic markets, to conditions in which cross-sectional preference heterogeneity exists and to nested logit and probit-based demand specifications as well. Based on the results of the simulation, the authors suggest a procedure for managing a potential endogeneity problem in logit-based demand models.

Originality/value

The literature on demand modeling has focused on deriving analytical results on the consequences of using poor-quality instruments to remedy endogeneity problems in linear models. Despite the widespread use of non-linear demand models such as logit, this study is the first to address the consequences of using poor-quality instruments in these models and to make practical recommendations on how to avoid poor outcomes.

Details

Journal of Modelling in Management, vol. 9 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

To view the access options for this content please click here
Article
Publication date: 1 August 2006

O. Nicolis and G. Tondini

The objective of this study is to formulate a model for forecasting the performance of firms in terms of trends in turnover, investments, exports, employment and…

Abstract

Purpose

The objective of this study is to formulate a model for forecasting the performance of firms in terms of trends in turnover, investments, exports, employment and flexibility, and to identify the principal correlations with selected dependent variables, such as the level of computerization, the extent of collaboration with competitors and the characteristics of the product.

Design/methodology/approach

This paper analyses data, which refers to a survey conducted on a sample of 89 firms from the Treviso province in the north east of Italy by using the Logit model.

Findings

From the application of the logit models it emerges that the most important variables contributing to the economic success of the firms are technological flexibility, collaboration, with competitors, and investments in certain areas such as research and development, marketing and fixed technology. Moreover, the findings show that the factors which contribute most significantly to technological flexibility (a key factor for the growth of the firm) are flexibility to demand, the level of computerization and staff training.

Practical implications

From the application of logit models it emerges that the most important variables influencing the good performance of firms are flexibility in keeping pace with technology, collaboration with competitors, and the choice of certain types of investment. Moreover, the variables which contribute most to greater flexibility are investments in human capital and in information technology, as well website use, the technological characteristics of the product and the firm's flexibility in following the demand trend.

Originality/value

In this study logit models are analysed from both a theoretical and applied point of view.

Details

Managerial Finance, vol. 32 no. 8
Type: Research Article
ISSN: 0307-4358

Keywords

To view the access options for this content please click here
Book part
Publication date: 6 August 2014

Kenneth Y. Chay and Dean R. Hyslop

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different…

Abstract

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

To view the access options for this content please click here
Book part
Publication date: 1 August 2004

Harry P. Bowen and Margarethe F. Wiersema

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable…

Abstract

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

Details

Research Methodology in Strategy and Management
Type: Book
ISBN: 978-1-84950-235-1

To view the access options for this content please click here
Article
Publication date: 1 June 2010

Eleftherios Giovanis

The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.

Abstract

Purpose

The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.

Design/methodology/approach

A logit regression was applied and the prediction performance in two out‐of‐sample periods, 2007‐2009 and 2010 was examined. On the other hand, feed‐forwards neural networks with Levenberg‐Marquardt error backpropagation algorithm were applied and then neural networks self‐organizing map (SOM) on the training outputs was estimated.

Findings

The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011‐2012, if government does not apply direct drastic measures.

Originality/value

Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.

Details

Journal of Financial Economic Policy, vol. 2 no. 2
Type: Research Article
ISSN: 1757-6385

Keywords

To view the access options for this content please click here

Abstract

Details

Handbook of Transport Modelling
Type: Book
ISBN: 978-0-08-045376-7

To view the access options for this content please click here
Book part
Publication date: 29 August 2007

Xavier Martin, Anand Swaminathan and Laszlo Tihanyi

Strategy deals with decisions about the scope of the firm and related choices about how to compete in various businesses. As such, research in strategy entails the…

Abstract

Strategy deals with decisions about the scope of the firm and related choices about how to compete in various businesses. As such, research in strategy entails the analysis of discrete choices that may not be independent of each other. In this paper, we review the methodological implications of modeling such choices and propose conditional, nested, mixed logit, and hazard rate models as solutions to the issues that arise from non-independence among strategic choices. We describe applications with an emphasis on international strategy, an area where firms face a multiplicity of choices with respect to both location and mode of entry.

Details

Research Methodology in Strategy and Management
Type: Book
ISBN: 978-0-7623-1404-1

To view the access options for this content please click here
Book part
Publication date: 14 September 2007

Frank S. Koppelman

Abstract

Details

Handbook of Transport Modelling
Type: Book
ISBN: 978-0-08-045376-7

To view the access options for this content please click here
Article
Publication date: 20 November 2017

Andreas Behr and Jurij Weinblat

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Abstract

Purpose

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Design/methodology/approach

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Findings

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Originality/value

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

Details

The Journal of Risk Finance, vol. 18 no. 5
Type: Research Article
ISSN: 1526-5943

Keywords

1 – 10 of over 5000