Panel Data Estimation Techniques for Farm-level Data Model

Econometric models wishing to estimate relevant parameters for agricultural policy analysis are increasingly relying on unbalanced panels of farm-level data. Since in the agricultural economics literature such models have often been estimated through simplified approaches, in this paper we try to verify whether the adoption of more sophisticated panel data techniques may impact the estimation results. For this reason, the policy model by Moro and Sckokai (1999) has been reestimated using techniques recently developed in the econometric literature. The preliminary results show a strong impact on the estimations. This seems to suggest that the adoption of proper panel-data techniques is likely to be very important in order to obtain reliable estimates of some key policy parameters.Agricultural policy, Panel data, Systems of equations, Agricultural and Food Policy,

Testing the NEG model: further evidence from panel data

Local wage variations in the UK are explained by two non-nested rival hypotheses. The first derives from new economic geography theory, in which wages depend on market potential. The second come from urban economics theory, giving a reduced form with wage rates dependent on employment density. The paper examines whether one of these rivals is encompassed by the other by fitting an artificial nesting model using three alternative panel data estimators. The estimates indicate that neither hypothesis is encompassed by its rival, suggesting a need for new, more comprehensive, theory

New Developments in Panel Data Estimation: Full-Factorial Panel Data Model

Panel data has been widely used in many social science studies. Pooling data across cross-sections and time-series improves quality of data analysis; however, the model is limited in its ability to actually accurately predict variables of interest due to severe practical data limitations and the ability of properly capturing varying market structures. In this article, a simple and innovative model of product share is introduced. The Full-Factorial Panel Data Model is based on the simple premises of re-conceptualization of any zero-sum group as a series of two-entity markets. This model solves the challenges associated with pooling data across disparate cross-sections and time-periods as well as the changing competitive market structure issues and therefore results in reliable variable of interest estimates.Research Methods/ Statistical Methods,

Conventional versus network dependence panel data gravity model specifications

Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing conventional multidimensional fixed effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency and spatial proximity of countries.Series: Working Papers in Regional Scienc

Model-based Clustering of non-Gaussian Panel Data

In this paper we propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behaviour and equilibrium level. Inference is addressed from a Bayesian perspective and model comparison is conducted using the formal tool of Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input from the user and possess hierarchical structures that enhance the robustness of the inference. Two examples illustrate the methodology: one analyses economic growth of OECD countries and the second one investigates employment growth of Spanish manufacturing firmsautoregressive modelling; employment growth; GDP growth convergence; hierarchical prior; model comparison; posterior propriety; skewness

Bayesian Inference in a Cointegrating Panel Data Model

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.Bayesian, panel data cointegration, error correction model, reduced rank regression, Markov Chain Monte Carlo.

The Minimum Distance Estimation with Multiple Integral in Panel Data

This paper studies the minimum distance estimation problem for panel data model. We propose the minimum distance estimators of regression parameters of the panel data model and investigate their asymptotic distributions. This paper contains two main contributions. First, the domain of application of the minimum distance estimation method is extended to the panel data model. Second, the proposed estimators are more efficient than other existing ones. Simulation studies compare performance of the proposed estimators with performance of others and demonstrate some superiority of our estimators.Comment: Minimum distance estimation; panel dat

TESTING THE NEG MODEL : FURTHER EVIDENCE FROM PANEL DATA

Local wage variations in the UK are explained by two non-nested rival hypotheses. The first derives from new economic geography theory, in which wages depend on market access. The second come from urban economics theory, giving a reduced form with wage rates dependent on employment density. The paper examines whether one of these rivals is encompassed by the other by fitting an artificial nesting model using three alternative panel data estimators. The estimates indicate that neither hypothesis is encompassed by its rival, suggesting a need for new, more comprehensive, theory.PANEL DATA, SPATIALLY CORRELATED ERROR COMPONENTS, MARKET ACCESS, NEW ECONOMIC GEOGRAPHY, SPATIAL ECONOMETRICS, NON-NESTED HYPOTHESIS

A Nonlinear Panel Data Model of Cross-Sectional Dependence

This paper proposes a nonlinear panel data model which can generate endogenously both `weak' and `strong' cross-sectional dependence. The model's distinguishing characteristic is that a given agent's behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as both a structural and reduced form vehicle to model different types of cross-sectional dependence, including evolving clusters.Nonlinear Panel Data Model; Clustering; Cross-section Dependence; Factor Models; Monte Carlo Simulations; Application to Stock Returns and Inflation Expectations