scholarly journals CONTINUOUSLY UPDATED INDIRECT INFERENCE IN HETEROSKEDASTIC SPATIAL MODELS

2021 ◽  
pp. 1-39
Author(s):  
Maria Kyriacou ◽  
Peter C.B. Phillips ◽  
Francesca Rossi
Keyword(s):  
Likelihood Function ◽  
Unobserved Heterogeneity ◽  
Generalized Method Of Moments ◽  
House Price ◽  
Ordinary Least Squares ◽  
Likelihood Method ◽  
Indirect Inference ◽  
Spatial Effects ◽  
Finite Sample ◽  
Starting Point

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, methods based on the quasi-likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference (II) approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the II procedure to take account of the parameterization of the variance–covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

scholarly journals Indirect Inference Estimation of Spatial Autoregressions

Econometrics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 34
Author(s):  
Yong Bao ◽  
Xiaotian Liu ◽  
Lihong Yang
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Asymptotic Distribution ◽  
Monte Carlo Study ◽  
Ordinary Least Squares ◽  
Indirect Inference ◽  
Finite Sample ◽  
Spatial Unit ◽  
Asymptotically Normal ◽  
Distribution Result

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

scholarly journals Comparing estimation methods of non-stationary errors-in-variables models

2019 ◽  
Vol 3 (1) ◽  
pp. 73-101 ◽  
Author(s):  
Naoto Kunitomo ◽  
Naoki Awaya ◽  
Daisuke Kurisu
Keyword(s):  
Maximum Likelihood ◽  
Measurement Errors ◽  
Likelihood Function ◽  
Asymptotic Properties ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Errors In Variables ◽  
Economic Time Series ◽  
Finite Sample ◽  
Ml Estimation

AbstractWe investigate the estimation methods of the multivariate non-stationary errors-in-variables models when there are non-stationary trend components and the measurement errors or noise components. We compare the maximum likelihood (ML) estimation and the separating information maximum likelihood (SIML) estimation. The latter was proposed by Kunitomo and Sato (Trend, seasonality and economic time series: the nonstationary errors-in-variables models. MIMS-RBP-SDS-3, MIMS, Meiji University. http://www.mims.meiji.ac.jp/, 2017) and Kunitomo et al. (Separating information maximum likelihood method for high-frequency financial data. Springer, Berlin, 2018). We have found that the Gaussian likelihood function can have non-concave shape in some cases and the ML method does work only when the Gaussianity of non-stationary and stationary components holds with some restrictions such as the signal–noise variance ratio in the parameter space. The SIML estimation has the asymptotic robust properties in more general situations. We explore the finite sample and asymptotic properties of the ML and SIML methods for the non-stationary errors-in variables models.

scholarly journals Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation

2020 ◽  
Vol 9 (4) ◽  
pp. 259 ◽  
Author(s):  
Rafael Suárez-Vega ◽  
Juan M. Hernández
Keyword(s):  
Geographically Weighted Regression ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Peer To Peer ◽  
Weighted Regression ◽  
Spatial Effects ◽  
Digital Platforms ◽  
Starting Point ◽  
Wide Range

Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.

scholarly journals Robust estimation for multivariate wrapped models

METRON ◽  
2021 ◽  
Author(s):  
Giovanni Saraceno ◽  
Claudio Agostinelli ◽  
Luca Greco
Keyword(s):  
Robust Estimation ◽  
Numerical Study ◽  
Real Data ◽  
Likelihood Method ◽  
Weighted Likelihood ◽  
Finite Sample ◽  
Pearson Residuals ◽  
Data Points ◽  
Wrapped Distributions ◽  
Standard Techniques

AbstractA weighted likelihood technique for robust estimation of multivariate Wrapped distributions of data points scattered on a $$p-$$ p - dimensional torus is proposed. The occurrence of outliers in the sample at hand can badly compromise inference for standard techniques such as maximum likelihood method. Therefore, there is the need to handle such model inadequacies in the fitting process by a robust technique and an effective downweighting of observations not following the assumed model. Furthermore, the employ of a robust method could help in situations of hidden and unexpected substructures in the data. Here, it is suggested to build a set of data-dependent weights based on the Pearson residuals and solve the corresponding weighted likelihood estimating equations. In particular, robust estimation is carried out by using a Classification EM algorithm whose M-step is enhanced by the computation of weights based on current parameters’ values. The finite sample behavior of the proposed method has been investigated by a Monte Carlo numerical study and real data examples.

scholarly journals A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

2019 ◽  
Vol 8 (11) ◽  
pp. 508
Author(s):  
Lan Hu ◽  
Yongwan Chun ◽  
Daniel A. Griffith
Keyword(s):  
Multilevel Model ◽  
House Prices ◽  
House Price ◽  
Spatial Filtering ◽  
Neighborhood Environment ◽  
Model Specification ◽  
Spatial Effects ◽  
Residential Areas ◽  
Fairfax County ◽  
Eigenvector Spatial Filtering

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

REDUNDANCY OF MOMENT CONDITIONS AND THE EFFICIENCY OF OLS IN SUR MODELS

2008 ◽  
Vol 24 (5) ◽  
pp. 1456-1460 ◽  
Author(s):  
Hailong Qian
Keyword(s):  
Least Squares ◽  
Asymptotic Efficiency ◽  
Generalized Method Of Moments ◽  
Sufficient Conditions ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
American Statistical Association ◽  
Orthogonality Condition ◽  
Moment Conditions ◽  
Necessary And Sufficient

In this note, based on the generalized method of moments (GMM) interpretation of the usual ordinary least squares (OLS) and feasible generalized least squares (FGLS) estimators of seemingly unrelated regressions (SUR) models, we show that the OLS estimator is asymptotically as efficient as the FGLS estimator if and only if the cross-equation orthogonality condition is redundant given the within-equation orthogonality condition. Using the condition for redundancy of moment conditions of Breusch, Qian, Schmidt, and Wyhowski (1999, Journal of Econometrics 99, 89–111), we then derive the necessary and sufficient condition for the equal asymptotic efficiency of the OLS and FGLS estimators of SUR models. We also provide several useful sufficient conditions for the equal asymptotic efficiency of OLS and FGLS estimators that can be interpreted as various mixings of the two famous sufficient conditions of Zellner (1962, Journal of the American Statistical Association 57, 348–368).

Exploring the nonlinear effect of size on profitability: evidence from an insurance brokerage industry in an emerging market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Richard Angelous Kotey ◽  
Richard Akomatey ◽  
Baah Aye Kusi
Keyword(s):  
Nonlinear Effect ◽  
Emerging Market ◽  
Empirical Studies ◽  
Random Effect ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Growth Strategies ◽  
Content Type ◽  
And Performance ◽  
Brokerage Firms

PurposeThis study examines the possible nonlinear effect of size on stakeholder and shareholder profitability in the Ghanaian insurance brokerage industry.Design/methodology/approachThis study employs a panel dataset of 64 Ghanaian insurance brokerage firms spanning 2011–2015. Static [ordinary least squares (OLS), fixed effect and random effect and dynamic (two-step generalized method of moments (GMM))] estimation techniques are employed to analyze the data.FindingsThe study finds the existence of both economies and diseconomies of scale and scope theories in the Ghanaian insurance brokerage industry confirming the existence of nonlinear nexus between size and performance. This finding is consistent for both stakeholder and shareholder profit performance. Thus, the results show that size improves profitability of insurance brokerage firms, but beyond a certain threshold, the relationship turns negative as size negatively affects profitability.Practical implicationsThe research findings have implications for both policy and research; the study recommends that Ghanaian brokerage managers should understand that not all growth is good and exercise a duty of care when applying growth strategies by monitoring size effect on performance so as not to go beyond the inflection point. Further research can be done to examine this effect in other contexts, timeframes and jurisdictions.Originality/valueThis research is unique in that it employs a panel dataset consisting of 96% of insurance brokerage firms in Ghana whilst employing both static and nonstatic regression models to examine the effect of size. The research analysis adopted is robust, and the findings are significant. Also, the lack of empirical studies on the operations and dealings of auxiliary institutions such as the insurance brokerage firms adds value to this research.

The ascension of executives' tenure, corporate risk-taking and corporate performance: evidence from Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Osama F. Atayah ◽  
Khakan Najaf ◽  
Ravichandran K. Subramaniam ◽  
Phaik Nie Chin
Keyword(s):  
Risk Taking ◽  
Corporate Performance ◽  
Stock Exchange ◽  
Generalized Method Of Moments ◽  
Negative Relationship ◽  
Ordinary Least Squares ◽  
Executive Management ◽  
Content Type ◽  
Top Executives ◽  
Corporate Risk

PurposeThis study aims to investigate the implication of top executives’ number of years of experience (tenure) on corporate risk-taking behaviour and corporate performance in Malaysian corporations.Design/methodology/approachTo test the hypothesis efficiently, the authors have extracted the data from Bloomberg for 788 listed companies of the Malaysian Stock Exchange. The methodology entails ordinary least squares regressions, quantile regression and dynamic system generalized method of moments model.FindingsFirst, the authors show that executive management tenure has a significant negative relationship with corporate risk-taking. It means that the long-tenured executives tend to undertake less risky strategies and decisions. Second, this study reveals that the longer executive management tenure has a positive relationship with corporate performance. Third, the moderating effect of corporate risk-taking with executive tenure (Tenure dummy*Risk) has a negative relationship with the corporate performance by 1%.Practical implicationsIt implies that the appointment of experienced executive management contributes towards corporate performance directly. However, experienced management trends take less risk, which eventually results in mitigating the corporate performance. On that basis, the findings are significant in highlighting the usefulness of executive leadership term and offers insights to academics, practitioners and policymakers.Originality/valueThis paper is novel since it is unique in evaluating the executive tenure and the preferences to handle risk strategies and how that impact the firm performance.

scholarly journals Manifolds of differentiable densities

2018 ◽  
Vol 22 ◽  
pp. 19-34 ◽  
Author(s):  
Nigel J. Newton
Keyword(s):  
Continuous Function ◽  
Likelihood Function ◽  
Probability Measures ◽  
Fréchet Spaces ◽  
Finite Sample ◽  
Infinite Dimensional ◽  
Statistical Manifolds ◽  
Covariant Derivatives ◽  
Finite Measures ◽  
Non Parametric

We develop a family of infinite-dimensional (non-parametric) manifolds of probability measures. The latter are defined on underlying Banach spaces, and have densities of class Cbk with respect to appropriate reference measures. The case k = ∞, in which the manifolds are modelled on Fréchet spaces, is included. The manifolds admit the Fisher-Rao metric and, unusually for the non-parametric setting, Amari’s α-covariant derivatives for all α ∈ ℝ. By construction, they are C∞-embedded submanifolds of particular manifolds of finite measures. The statistical manifolds are dually (α = ±1) flat, and admit mixture and exponential representations as charts. Their curvatures with respect to the α-covariant derivatives are derived. The likelihood function associated with a finite sample is a continuous function on each of the manifolds, and the α-divergences are of class C∞.

Bank competition, government intervention and SME debt financing

2017 ◽  
Vol 7 (4) ◽  
pp. 478-492 ◽  
Author(s):  
Jianhua Du ◽  
Chao Bian ◽  
Christopher Gan
Keyword(s):  
Government Intervention ◽  
Generalized Method Of Moments ◽  
External Debt ◽  
Ordinary Least Squares ◽  
Debt Financing ◽  
Bank Loans ◽  
Bank Competition ◽  
Firm Level ◽  
Content Type ◽  
The Government

Purpose The purpose of this paper is to examine the effects of the government intervention and bank competition on small and medium enterprise (SME) external debt financing in Chinese capital market. Design/methodology/approach This study uses ordinary least squares with standard errors clustered at the firm level. In addition, the authors use the dynamic system generalized method of moments to address the possible endogeneity issue in the regressions. Findings Using a sample of 908 firms from 2000 to 2010, the authors found that SMEs are more likely to access bank loans only in regions with higher level of government intervention than median government intervention. Further, the result shows that the government is motivated to help SMEs to obtain more external debt in regions where the level of bank competition is lower than the median bank competition index. Last, the authors found evidence that firms with politically connected CEOs are likely to access bank loans. Research limitations/implications This paper highlights that government intervention enables the SMEs to secure more bank loans. Second, the authors’ results imply that the government is motivated to help SMEs to obtain more external debt in regions with low level of bank competition. Originality/value This study contributes to the current literature by revealing that government intervention is the driving force alleviating SMEs’ constraints in accessing external financing. Second, this study finds the evidence to supports the argument that government has a strong motive to help SMEs to secure long-term credits for political purpose (Fan et al., 2012), when the level of bank competition is low (Berger and Udell, 2006).

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