scholarly journals Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Grigorios Emvalomatis ◽  
Spiro E. Stefanou ◽  
Alfons Oude Lansink
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Fixed Effects ◽  
Likelihood Function ◽  
Stochastic Frontier ◽  
General Context ◽  
Explanatory Variables ◽  
Monte Carlo Experiments ◽  
Incidental Parameters ◽  
Stochastic Frontier Models

Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are updated using information from the data and are robust to possible correlation of the group-specific constant terms with the explanatory variables. Monte Carlo experiments are performed in the specific context of stochastic frontier models to examine and compare the sampling properties of the proposed estimator with those of the random-effects and correlated random-effects estimators. The results suggest that the estimator is unbiased even in short panels. An application to a cross-country panel of EU manufacturing industries is presented as well. The proposed estimator produces a distribution of efficiency scores suggesting that these industries are highly efficient, while the other estimators suggest much poorer performance.

scholarly journals Interactions in Fixed Effects Regression Models

2020 ◽  
pp. 004912412091493
Author(s):  
Marco Giesselmann ◽  
Alexander W. Schmidt-Catran
Keyword(s):  
Monte Carlo ◽  
Time Constant ◽  
Fixed Effects ◽  
Regression Models ◽  
Time Varying ◽  
Unit Level ◽  
Monte Carlo Experiments ◽  
Product Term ◽  
Fixed Effects Regression ◽  
Effect Heterogeneity

An interaction in a fixed effects (FE) regression is usually specified by demeaning the product term. However, algebraic transformations reveal that this strategy does not yield a within-unit estimator. Instead, the standard FE interaction estimator reflects unit-level differences of the interacted variables. This property allows interactions of a time-constant variable and a time-varying variable in FE to be estimated but may yield unwanted results if both variables vary within units. In such cases, Monte Carlo experiments confirm that the standard FE estimator of x ⋅ z is biased if x is correlated with an unobserved unit-specific moderator of z (or vice versa). A within estimator of an interaction can be obtained by first demeaning each variable and then demeaning their product. This “double-demeaned” estimator is not subject to bias caused by unobserved effect heterogeneity. It is, however, less efficient than standard FE and only works with T > 2.

scholarly journals Testing a Parametric Model Against a Semiparametric Alternative

1994 ◽  
Vol 10 (5) ◽  
pp. 821-848 ◽  
Author(s):  
Joel L. Horowitz ◽  
Wolfgang Härdle
Keyword(s):  
Monte Carlo ◽  
Alternative Model ◽  
Random Variable ◽  
Parametric Model ◽  
Conditional Moment ◽  
Moment Conditions ◽  
Explanatory Variables ◽  
Conditional Moments ◽  
Monte Carlo Experiments ◽  
The Mean

This paper describes a method for testing a parametric model of the mean of a random variable Y conditional on a vector of explanatory variables X against a semiparametric alternative. The test is motivated by a conditional moment test against a parametric alternative and amounts to replacing the parametric alternative model with a semiparametric model. The resulting semiparametric test is consistent against a larger set of alternatives than are parametric conditional moments tests based on finitely many moment conditions. The results of Monte Carlo experiments and an application illustrate the usefulness of the new test.

A Comment on Diagnostic Tools for Counterfactual Inference

2009 ◽  
Vol 17 (1) ◽  
pp. 89-106 ◽  
Author(s):  
Nicholas Sambanis ◽  
Alexander Michaelides
Keyword(s):  
Monte Carlo ◽  
Political Science ◽  
Diagnostic Tools ◽  
Counterfactual Analysis ◽  
Data Sets ◽  
Explanatory Variables ◽  
Monte Carlo Experiments ◽  
Data Points

We evaluate two diagnostic tools used to determine if counterfactual analysis requires extrapolation. Counterfactuals based on extrapolation are model dependent and might not support empirically valid inferences. The diagnostics help researchers identify those counterfactual “what if” questions that are empirically plausible. We show, through simple Monte Carlo experiments, that these diagnostics will often detect extrapolation, suggesting that there is a risk of biased counterfactual inference when there is no such risk of extrapolation bias in the data. This is because the diagnostics are affected by what we call the n/k problem: as the number of data points relative to the number of explanatory variables decreases, the diagnostics are more likely to detect the risk of extrapolation bias even when such risk does not exist. We conclude that the diagnostics provide too severe a test for many data sets used in political science.

scholarly journals Entrepreneurial capability (EC) environment in ASEAN-05 emerging economies

2018 ◽  
Vol 12 (2) ◽  
pp. 206-221 ◽  
Author(s):  
Munshi Naser Ibne Afzal ◽  
Kasim Mansur ◽  
Umme Humayara Manni
Keyword(s):  
Fixed Effects ◽  
Negative Impact ◽  
Positive Impact ◽  
Stochastic Frontier ◽  
The Philippines ◽  
University Education ◽  
Positive Role ◽  
Content Type ◽  
Stochastic Frontier Models ◽  
Key Indicators

Purpose The entrepreneurial capability (EC) environment refers to the general social and economic settings of a given local/regional entrepreneurship environment. The primary purpose of this study is to uncover key indicators of the EC milieu and test these components empirically within the context of the Association of South East Asian Nations (ASEAN)-5 economies to elucidate the current state of their EC environments, at the regional and national levels. To this end, the aim of this study is twofold. First, this work endeavors to explicate the determinants of EC, with aims of elucidating its association to commercial opportunities in (ASEAN)-5 economies, namely, Indonesia, Malaysia, the Philippines, Singapore and Thailand. Next, this study applies the developed theory, including the identified determinants of EC to empirically test the efficiency and imperative coefficients of variables that have an impact on perceived entrepreneurial capabilities within a given environment. Design/methodology/approach This research applies two frontier models, namely, the consistent estimation of fixed-effects and linear transformation stochastic frontier models, to assess the coefficients of significant EC variables for the panel sample. Data corresponding to the assessed variables were retrieved from the databases of the Global Entrepreneurship Monitor (GEM) – 2016 and the World Competitiveness Yearbook (WCY) – 2016, for the period, 2010-2016. Findings The attained results suggest that factors corresponding to the variables “Entrepreneurship as a good career choice” and “perceived opportunities” have played a significantly positive role on the EC environment of ASEAN 05, although findings suggest both factors may still be improved upon. Conversely, the “fear of failure rate” factor was shown to have exerted a negative impact on the efficiency of the EC environment of ASEAN 05. Other important variables – such as intellectual property rights, university education and knowledge transfer rate – were shown to generate a positive impact on the EC environment of these economies. Originality/value This study makes an important contribution to the entrepreneurship literature and can stimulate policymakers to rethink the EC settings of ASEAN-05 in their pursuit of an innovation-driven region.

Stochastic Frontier Models of Prism Vertices

2000 ◽  
Vol 1718 (1) ◽  
pp. 18-26 ◽  
Author(s):  
Ryuichi Kitamura ◽  
Toshiyuki Yamamoto ◽  
Keiko Kishizawa ◽  
Ram M. Pendyala
Keyword(s):  
Stochastic Frontier ◽  
Space Time ◽  
Mean Difference ◽  
Terminal Vertex ◽  
Time Axis ◽  
Explanatory Variables ◽  
Stochastic Frontier Models

A methodology to estimate the location and size of space-time prisms that govern individuals’ activity and travel is presented. Because the vertices of a prism are unobservable, stochastic frontier models are formulated to locate prism vertices along the time axis using observable trip starting or ending times as the dependent variable and commute characteristics, personal and household attributes, and area characteristics as explanatory variables. Models are estimated successfully with coherent behavioral indications. A mean difference of 1.46 h is found between the observed trip ending time and the expected location of the terminal vertex for workers’ evening prisms. The estimation results aid in enhancing the understanding of prism constraints by identifying the determinants of prism vertex locations.

scholarly journals NONPARAMETRIC SIGNIFICANCE TESTING

2000 ◽  
Vol 16 (4) ◽  
pp. 576-601 ◽  
Author(s):  
Pascal Lavergne ◽  
Quang Vuong
Keyword(s):  
Monte Carlo ◽  
Null Hypothesis ◽  
Kernel Method ◽  
Significance Testing ◽  
Local Alternatives ◽  
Test Statistic ◽  
Explanatory Variables ◽  
Monte Carlo Experiments ◽  
Standard Normal ◽  
Complete Set

A procedure for testing the significance of a subset of explanatory variables in a nonparametric regression is proposed. Our test statistic uses the kernel method. Under the null hypothesis of no effect of the variables under test, we show that our test statistic has an nhp2/2 standard normal limiting distribution, where p2 is the dimension of the complete set of regressors. Our test is one-sided, consistent against all alternatives and detects local alternatives approaching the null at rate slower than n−1/2h−p2/4. Our Monte-Carlo experiments indicate that it outperforms the test proposed by Fan and Li (1996, Econometrica 64, 865–890).

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