Unobserved Individual Effects Research Articles

Hierarchical Bayesian analysis of racehorse running ability and jockey skills

Abstract In this paper, we proposed a new method of evaluating horse ability and jockey skills in horse racing. In the proposed method, we aimed to estimate unobservable individual effects of horses and jockeys simultaneously with regression coefficients for explanatory variables such as horse age and racetrack conditions and other parameters in the regression model. The data used in this paper are records on 1800­m races (excluding steeplechases) held by the Japan Racing Association from 2016 to 2018, including 4063 horses and 143 jockeys. We applied the hierarchical Bayesian model to stably estimate such a large amount of individual effects. We used the Markov chain Monte Carlo (MCMC) method coupled with Ancillarity- Sufficiency Interweaving Strategy for Bayesian estimation of the model and choose the best model with Widely Applicable Information Criterion as a model selection criterion. As a result, we found a large difference in the ability among horses and jockeys. Additionally, we observed a strong relationship between the individual effects and the race records for both horses and jockeys.

Specification test in panel ordered response models

This paper develops a new simple method to simultaneously test the threshold effect and the correlation between unobserved individual effects and covariates in a panel ordered response model. We first use logistic function to represent the threshold effect, and use Mundlak’s projection formula to characterize the potential correlation between individual effect and covariates. We then construct Wald statistics to test the existence of such correlation and threshold effect separately, and then to test whether the two phenomena exist simultaneously by controlling the false discovery rate (FDR). The advantage of using FDR approach is that we can accurately know the specific form that the model should adopt when rejecting the multiple hypotheses. Simulation results show that our method can effectively capture the actual model even in the finite sample. We apply our method to test the relationship between income and happiness using data from the survey of China Family Panel Studies. While the findings suggest that income is significantly correlated with unobserved individual effect, no evidence is found in support of the threshold effect between income and happiness.

The “Africa Rising”: An Empirical Analysis of the Determinants of Per-Capita Growth

In developing economies, the race toward inclusive development has prompted researchers to reconsider the drivers of growth in view of achieving the Sustainable Development Goals (SDGs). This research note has the purpose to explore the determinants of African growth after analysing reference literature to select the explanatory variables. We examine growth in a panel of 54 African countries using the generalised method of moment system estimators (GMM-sys), by controlling idiosyncratic unobserved individual effects of countries with fixed-effects models. Using GMM-sys to estimate growth models is not novel, and many previous studies have used this appropriate approach for growth analyses. As far as we know, we have not found any studies analysing the determinants of growth in a panel of 54 African countries and/or using GMM estimators with data referring to the last decade. We have used the real per-capita GDP as a dependent variable. As a data source, the main international organisations (UN, WB, IMF) have been considered, and the analysed timeframe is 2010-2019, where we have more data completeness. Based on the time-series data collected and estimation methodology used, the findings show determinants of African growth, and our findings can have an implication for managers and decision-makers with an interest in African emerging markets. The real per-capita GDP with one order of lags is the variable with the highest magnitude in all models and it is expected of us. Furthermore, we have found confirmations for the variables of the macroeconomic framework highlighted in the recent reference literature on growth, and we have found new evidence for the variables of the African merchandise trade across developed and developing countries. In the conclusion, we highlight the importance of a sound governance and business environment in African countries for achieving the SDGs.

The Impact of Capital Investments on Firm Financial Performance – Empirical Evidence from the Listed Food and Agriculture Companies in Vietnam

Capital investment is one of the most important financial decisions of a firm, which effects the business survival. This paper investigates the impact of capital investments on firm performance to provide an insight for future capital investment decisions of companies. The study uses unbalance panel data gathered from 62 companies which covered 688 observations of listed food and agriculture sector in Vietnam stock exchange during the period 2009 – 2020. To control for unobserved individual effects, the random effect estimator was employed. Profit margin (PM) is used as a proxy for firm performance, while independent variables are capital investment rate in both short-term and long-term effect, equity to total assets ratio, firm size and tangible to fixed assets ratio. The results indicate that capital investments have statistically significant positive impact on long-term performance of the analyzed enterprises. This outcome is in accordant with many previous studies. Especially in the context of Vietnam listed food and agriculture companies have high level of capital investment, a portion increase in capital investment rate will increase the company profit margin. Other variables also have statistically significant relationship with profit margin, in details, equity ratio and size have positive effect while tangible assets ratio has negative effect on firm performance. Therefore, the research suggest firm managers should choose the appropriate capital investment strategies in long-term perspective, within the consideration of equity ratio, economics of scale and tangible assets investment ratio to get the most efficient performance.

Obtaining the effects of unobservable firm-specific characteristics on cost stickiness by the one-way error component regression model

PurposeThis research attempts to obtain the effect of unobservable firm-specific characteristics on selling, general and administrative (SG&A) cost stickiness by using panel data.Design/methodology/approachFor this purpose, first, the authors describe the one-way error component regression model in panel data and presented that unobservable individual effect how could be estimated. Then the authors tested this panel data’s ability by estimating the effect of unobservable firm-specific characteristics on SG&A stickiness.FindingsThe authors find, for 195 firm-year of the industrial sector over 5 years, the SG&A costs increase on average at a rate of 0.76% per 1% increase in sales but decrease only 0.51% per 1% decrease in sales. In addition, the authors find that the unobservable characteristics of each company have different effects on SG&A cost stickiness.Originality/valueAs the present study is the pioneer study on describe the one-way error component regression model in panel data and presented the unobservable individual effects. The findings of this study can contribute to the realm of this study and the related literature.

Classical Approach to Zero-Inflated Dynamic Panel Ordered Probit Model with an Application in Drug Abuse

The Zero inflated ordered categorical data with time series structure are often a characteristic of behavioral research attributed to non-participation decision and zero consumption of substance such as drugs among the participants. The existing Semi-parametric zero inflated dynamic panel probit model with selectivity have exhibited biasness and inconsistency in estimators as a result of poor treatment of initial condition and exclusion of selectivity in the unobserved individual effects respectively. The model assumed that the cut points are known to address heaping in the data and therefore cannot be used when the cut points are unknown. In this paper, a Zero inflated dynamic panel ordered probit models have been developed to address the above challenges. Average partial effects that presents the impacts on the specific probabilities per unit change in the covariates are also given. Since the solutions are not of closed form, Maximum likelihood estimation based on Newton Raphson algorithm was used to estimate the parameters of the model. A Monte Carlo study was carried out to investigate some theoretical properties of the estimators in the models. The study found that the Zero inflated dynamic panel ordered probit models with independent and correlated error terms produced consistent estimators. The Zero inflated dynamic panel ordered probit models with independent and correlated error terms had more accurate estimators than the Dynamic panel ordered probit model. The Zero inflated dynamic panel ordered probit model with correlated error terms fitted the National Longitudinal Survey of Youth 1997 better than Zero inflated dynamic panel ordered probit model with independent error terms and Dynamic panel ordered probit model. The Zero inflated dynamic panel ordered probit model with independent error terms fitted the National Longitudinal Survey of Youth 1997 better the Dynamic panel ordered probit model.

Tasa de crecimiento salarial real a la educación privada para profesores universitarios: un análisis por género con datos panel en el suroccidente colombiano

The objective of this document consists of estimating the real wage growth rate over time for the professors of the Faculty of Economics at the University of Cauca, which is located in Popayan, Colombia, by making special emphasis on gender differentials. In order to achieve it, an extended function of Mincer (1974) with panel data is used, which includes apart from the traditional variables related to education and experience, a set of covariates distributed as exogenous and invariant covariates over time, and other endogenous semi-variants covariates over time. For such a reason, we use an Efficient Generalized Instrumental Variable model estimated under the Hausman-Taylor framework, where some covariates are allowed to be correlated with unobservable individual random effects.

Integrated likelihood based inference for nonlinear panel data models with unobserved effects

We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation study suggest that our methodology can work very well even in moderately sized panels of short duration in both static and dynamic models.

Change point estimation in panel data with time‐varying individual effects

SummaryExisting panel data methods remove unobserved individual effects before change point estimation through data transformations such as first‐differencing. In this paper, we show that multiple change points can be consistently estimated in short panels via ordinary least squares. Since no data variation is removed before change point estimation, our method has better small‐sample properties compared to first‐differencing methods. We also propose two tests that identify whether the change points found by our method originate in the slope parameters or in the covariance of the regressors with individual effects. We illustrate our method via modeling the environmental Kuznets curve and the US house price expectations after the financial crisis.

The role of demand in fostering product vs process innovation: a model and an empirical test

While the extant innovation literature has provided extensive evidence of the so-called “demand-pull” effect, the possible diverse impact of demand evolution on product vs process innovation activities has not been yet investigated. This paper develops a formal model predicting a larger inducing impact of past sales in fostering product rather than process innovation. This prediction is then tested through a dynamic microeconometric model, controlling for R&D persistence, sample selection, observed and unobservable individual firm effects and time and sectoral peculiarities. Results are consistent with the model and suggest that an expansionary economic policy may benefit the diffusion of new products or even the emergence of entire new sectors.

Trading firms and the gender wage gap: evidence from South Africa

abstractFirms engaged in international trade account for a large share of South Africa’s output and employment. Therefore, their behaviour has potentially large effects on national outcomes including gender inequality. In this paper, we test whether the gender wage gap (GWG) of trading firms differs from that of domestic firms based on a growing literature that has outlined how the special characteristics of trading firms could lead to such a different GWG. Using a unique employer–employee matched data panel from 2011 to 2016 for South Africa based on novel tax record data and employing various fixed effects regressions, we find that the GWG of trading firms is significantly larger than that of domestic firms. This holds even when controlling for unobserved individual and firm fixed effects that account for factors such as worker education or firm profitability. We also find that firms which both import and export (a crude proxy for foreign-owned firms) behave more equally than other trading firms. This could be driven by foreign owned firms that impose their more equal domestic pay structures on their South African affiliates and would emphasize the role of foreign investment for gender equality.

Employee involvement and job satisfaction: a tale of the millennial generation

PurposeThe purpose of this paper is to empirically study the effect of employee involvement in the workplace on job satisfaction for millennial workers in Colombia.Design/methodology/approachData were obtained from a sample of 2103 millennial employees working in 11 companies of different sectors located in the five main cities of Colombia. Ordered probit models were estimated to study the effect of employee involvement on job satisfaction, in general, and how different forms of participative decision making in the workplace produce different impacts on individual satisfaction with objective and intrinsic aspects of the job, in particular.FindingsThe empirical results show that, for millennial workers, there is a positive link between employee involvement and job satisfaction. Moreover, there is a higher positive impact on job satisfaction when millennial workers participate in decisions on general aspects of the company than when they participate in specific decisions such as those concerning teamwork or main tasks at work. Another interesting result is that millennial workers attach high importance to intrinsic aspects of their jobs (such as the possibility to use their knowledge in the work), which may improve their satisfaction in a higher participative environment.Research limitations/implicationsThe results can present bias due to the use of self-report data from millennial workers. Another potential limitation is the cross-sectional nature of the data, which does not control for unobserved individual effects. The study may be extended to other developing countries to help identify results more precisely for different contexts.Originality/valueThe value lies in exploring the relationship between employee involvement and job satisfaction for millennial workers in the context of a developing country. The paper simultaneously considers different types of employee involvement and estimates their effects on different facets of job satisfaction.

THE FACTOR-LASSO AND K-STEP BOOTSTRAP APPROACH FOR INFERENCE IN HIGH-DIMENSIONAL ECONOMIC APPLICATIONS

We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a large number of additional time-varying confounding variables. We suppose that, in addition to unrestricted time and individual specific effects, these confounding variables are generated by a small number of common factors and high-dimensional weakly dependent disturbances. We allow that both the factors and the disturbances are related to the outcome variable and other variables of interest. To make informative inference feasible, we impose that the contribution of the part of the confounding variables not captured by time specific effects, individual specific effects, or the common factors can be captured by a relatively small number of terms whose identities are unknown. Within this framework, we provide a convenient inferential procedure based on factor extraction followed by lasso regression and show that the procedure has good asymptotic properties. We also provide a simple k-step bootstrap procedure that may be used to construct inferential statements about the low-dimensional parameters of interest and prove its asymptotic validity. We provide simulation evidence about the performance of our procedure and illustrate its use in an empirical application.

Fixed Effects Estimation of Large-TPanel Data Models

This article reviews recent advances in fixed effects estimation of panel data models for long panels, where the number of time periods is relatively large. We focus on semiparametric models with unobserved individual and time effects, where the distribution of the outcome variable, conditional on covariates and unobserved effects, is specified parametrically while the distribution of the unobserved effects is left unrestricted. In contrast to existing reviews on long panels, we discuss models with both individual and time effects, split-panel jackknife bias corrections, unbalanced panels, distribution and quantile effects, and other extensions. Understanding and correcting the incidental parameter bias caused by the estimation of many fixed effects are our main focuses, and the unifying theme is that the order of this bias is given by the simple formula p/ n for all models discussed, with p being the number of estimated parameters and n the total sample size.

Estimation and inference in functional-coefficient spatial autoregressive panel data models with fixed effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors with a large number of cross-sectional units and a finite time periods. Our proposed methodology removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, however avoids using backfitting technique. We derive the limiting results for the proposed estimators and construct a consistent nonparametric test to test for spatial endogeneity. A small Monte Carlo study shows that our proposed estimators and test statistic exhibit good finite-sample performance.

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance.

Non-parametric Panel Data Models with Interactive Fixed Effects

This article studies non-parametric panel data models with multidimensional, unobserved individual effects when the number of time periods is fixed. I focus on models where the unobservables have a factor structure and enter an unknown structural function non-additively. The setup allows the individual effects to impact outcomes differently in different time periods and it allows for heterogeneous marginal effects. I provide sufficient conditions for point identification of all parameters of the model. Furthermore, I present a non-parametric sieve maximum likelihood estimator as well as flexible semiparametric and parametric estimators. Monte Carlo experiments demonstrate that the estimators perform well in finite samples. Finally, in an empirical application, I use these estimators to investigate the relationship between teaching practice and student achievement. The results differ considerably from those obtained with commonly used panel data methods.

The Role of Demand in Fostering Product vs Process Innovation: A Model and an Empirical Test

While the extant innovation literature has provided extensive evidence of the so-called demand-pull effect, the possible diverse impact of demand evolution on product vs process innovation activities has not been yet investigated. This paper develops a formal model predicting a larger inducing impact of past sales in fostering product rather than process innovation. This prediction is then tested through a dynamic microeconometric model, controlling for R&D persistence, sample selection, observed and unobservable individual firm effects and time and sectoral peculiarities. Results are consistent with the model and suggest that an expansionary economic policy may benefit the diffusion of new products or even the emergence of entire new sectors.

The Role of Demand in Fostering Product vs Process Innovation: A Model and an Empirical Test

While the extant innovation literature has provided extensive evidence of the so-called demand-pull effect, the possible diverse impact of demand evolution on product vs process innovation activities has not been yet investigated. This paper develops a formal model predicting a larger inducing impact of past sales in fostering product rather than process innovation. This prediction is then tested through a dynamic microeconometric model, controlling for R&D persistence, sample selection, observed and unobservable individual firm effects and time and sectoral peculiarities. Results are consistent with the model and suggest that an expansionary economic policy may benefit the diffusion of new products or even the emergence of entire new sectors.

Semi-parametric inference for semi-varying coefficient panel data model with individual effects

We study a semi-varying coefficient panel data model with unobserved individual effects, where all the covariates are high-dimensional variables. Based on multivariate local linear fitting, the transformation technique and the profile likelihood method, we establish semi-parametric fixed effects estimators, semi-parametric random effects estimators, and their asymptotic properties. We also introduce a test for discriminating between a semi-varying coefficient random effects panel data model and a semi-varying coefficient fixed effects panel data model. The critical values are estimated by a bootstrap procedure. Monte Carlo studies exhibit the finite-sample performance of the proposed estimators and test statistics. Simulation results show that the methods perform well for moderate sample sizes. Finally, we analyze the cigarette consumption panel data from 46 American states covering the period 1963–1992.