Portuguese tourism demand:a dynamic panel data analysis

This article considers the determinants of Portuguese tourism demand for the period 2004-2013. The econometric methodology uses a panel unit root test and the dynamic panel data (GMM-system estimator). The different techniques of panel unit root (Levin, Lin and Chu; Im, Pesaran and Shin W-stat and augmented Dickey-Fuller - Fisher Chi-square) show that the variables used in this panel are stationary. The dynamic model proves that tourism demand is a dynamic process. The variables relative prices, income per capita, human capital and government spending encourage international tourism demand for Portugal.info:eu-repo/semantics/publishedVersio

The estimation of three-dimensional fixed effects panel data models

The paper introduces for the most frequently used three-dimensional fixed effects panel data models the appropriate Within estimators. It analyzes the behaviour of these estimators in the case of no-self-flow data, unbalanced data and dynamic autoregressive models.panel data, unbalanced panel, dynamic panel data model, multidimensional panel data, fixed effects, trade models, gravity models, FDI

GMM Estimation of Dynamic Panel Data Models with Persistent Data

This paper considers GMM based estimation and testing procedures for two versions of the AR(1) model with Fixed Effects, henceforth abbreviated as ARFE(1): the conditional ARFE(1) model, and the inclusive ARFE(1) model, which contains the stationary ARFE(1) models and the ARFE(1) model with a unit root. First, the paper presents a two-step Optimal Linear GMM (OLGMM) estimator for the inclusive model which is asymptotically equivalent to the optimal nonlinear GMM estimator of Ahn and Schmidt (1997). Then the paper examines the properties of the GMM estimators for both versions of the model when the data are persistent. Among other things, we find that the OLGMM estimator is superefficient in the unit root case. Furthermore, under stationarity the covariances of the instruments of the Arellano-Bond estimator and the first differences of the dependent variable are not weak. We also derive new approximations to the finite sample distributions of the Arellano-Bond estimator (for both versions of the model), the Arellano-Bover estimator, and the System estimator. We employ local-to-zero asymptotics (cf. Staiger and Stock (1997)) for the Arellano-Bond estimator for the conditional model, because its instruments are weak in this context, and we employ local-to-unity asymptotics, which is developed in this paper, for the estimators for the stationary model. The new approximations agree well with the Monte Carlo evidence in terms of bias and variance. Finally, various GMM based unit root tests against stationary and conditional alternatives are proposed.Dynamic panel data models, Fixed effects, Generalized Method of Moments, Weak moment conditions, Local-to-zero asymptotics, Local-to-unity asymptotics, Redundancy, Unit root tests, Superefficiency

Non-Gaussian dynamic Bayesian modelling for panel data

A first order autoregressive non-Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and we pay specific attention to the elicitation of the prior. As the prior structure chosen is not proper, we derive conditions for the existence of the posterior. By considering a model with individual dynamic parameters we are also able to formally test whether the dynamic behaviour is common to all units in the panel. The methodology is illustrated with two applications involving earnings data and one on growth of countries.autoregressive modelling; growth convergence; individual effects; labour earnings; prior elicitation; posterior existence; skewed distributions

Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor, Kelejian, and Prucha (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the dynamic instruments by lagged and weighted exogenous variables as suggested by Kelejian and Robinson (1993) yields new spatial dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of Monte Carlo simulations. We show that differences in bias as well as root mean squared error between spatial GMM estimates and corresponding GMM estimates in which spatial error correlation is ignored are small.Dynamic panel models;spatial lag;spatial error;GMM estimation

Dynamic panel data: A useful technique in experiments

Numerous experimental studies use a panel approach to analyze repeated experiments involving a large number of periods. They use “static” panel techniques and do not incorporate any temporal dependency (lags) of the dependent variable. This paper introduces dynamic panel data techniques to experimental economists. This is a standard tool in many other fields of economics and might also be useful in our discipline. It uses the lags of the dependent variable as explanatory variables. Although the coefficients on lagged dependent variables might be far from our interest, the introduction of these lags becomes crucial to control for the dynamics of the process. To show the advantages of this technique, we have compared two datasets using static and dynamic panel data. We conclude that the use of dynamic panel data models in the context of experiments allows to unravel new relationships between experimental variables and highlighting new paths in behaviors

A Dynamic “Fixed Effects” Model for Heterogeneous Panel Data

This paper introduces a dynamic panel data model in which the intercepts and the coefficients on the lagged endogenous variables are specific to the cross section units, while the coefficients on the exogenous variables are assumed to be normally distributed across the cross section. Thus the model includes mixture of fixed coefficients and random coefficients, which I call the “MFR” model. The paper shows that this model has several desirable characteristics. In particular, the model allows for a considerable degree of heterogeneity across the cross section both in the dynamics and in the relationship between the independent and dependent variables. Estimation of the MFR model produces an estimate of the variance of the coefficients across the cross section units which can be used as a diagnostic tool to judge how widespread a relationship is and whether pooling of the data is appropriate. In addition, unlike LSDV estimation of dynamic panel models, the MFR model does not produce severely biased estimates when T is small.dynamic fixed effects panel data, heterogenous coefficients

Dynamic Interrelation of Births and Deaths: Evidence from Plant Level Data

In this paper, the dynamic panel data method is used to investigate the dynamic interrelation of plant births and plant deaths. The dynamic panel data method considers the endogenous problem and individual effects. Empirical findings support the multiplier effect. In addition, exit does not cause entry, whereas entry causes exit.