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The task of this work is to discuss issues conceming the specification, estimation, inference and forecasting in multivariate dynamic heterogeneous panel data models from a Bayesian perspective. Three essays linked by a few conraion ideas compose the work. Multivariate dynamic models (mainly VARs) based on micro or macro panel data sets have become increasingly popular in macroeconomics, especially to study the transmission of real and monetary shocks across economies. This great use of the panel VAR approach is largely justified by the fact that it allows the docimientation of the dynamic impact of shocks on key macroeconomic variables in a framework that simultaneously considers shocks emanating from the global enviromnent (world interest rate, terms of trade, common monetary shock) and those of domestic origin (supply shocks, fiscal and monetary policy, etc.). Despite this empirical interest, the theory for panel VAR is somewhat underdeveloped. The aim of the thesis is to shed more light on the possible applications of the Bayesian framework in discussing estimation, inference, and forecasting using multivariate dynamic models where, beside the time series dimensión we can also use the information contained in the cross sectional dimensión. The Bayesian point of view provides a natural environment for the models dlscussed in this work, due to its flexibility in combining diíferent sources of information. Moreover, it has been recently shown that Bayes estimates of hierachical dynamic panel data models have a reduced small sample bias, and help in improving the forecasting performance of these models.

The question whether real money causes real output appears to
be important for many economists working in the area of macroeconomics
and, has been subjected to a variety of modern econometric techniques,
producing conflicting results. One often applied method to investigate
the empirical relationship between money and real activity is Granger
causality analysis [Granger (1969)]. Using this approach, the causality
question can be sharply posed as whether past values of money help to
predict current values of output. This concept, however, should be
clearly distinguished from any richer philosophical notion of causality
[cf. Holland (1986)]. Present paper examines the relationship between
money (both M1 and M2) and income (Real GDP) for 15 developing countries
using a newly developed heterogeneous dynamic panel data approach.1 Sims
(1972) postulated "the hypothesis that causality is unidirectional from
money to income agrees with the post war U.S. data, whereas the
hypothesis that causality is unidirectional from income to money is
rejected". Since then a voluminous literature has emerged testing the
direction of causality.2 Some studies have tested the relationship
between these variables and the direction of causality for a particular
country using time series techniques [e.g., Hsiao (1979) for Canada,
Stock and Watson (1989) for U.S. data, Friedman and Kuttner (1992, 1993)
for U.S. data, Thoma (1994) for U.S. data, Christiana and Ljungquist
(1988) for U.S. data, Davis and Tanner (1997) for U.S. data, Jusoh
(1986) for Malaysia, Zubaidi, et al. (1996) for Malaysia, Biswas and
Saunders (1998) for India, and Bengali, et al. (1999) for Pakistan].
Other studies have tested the above on a number of countries, for
example Krol and Ohanian (1990) used the data for Canada, Germany, Japan
and the U.K. Hayo (1999) using data from 14 European Union (EU)
countries plus Canada, Japan, and the United States. More recently Hafer
and Kutan (2002) used a sample of 20 industrialised and developing
countries. This paper contributes to this later strand of the
literature, which it extends in three directions. First, it employed a
newly developed panel cointegration technique [Larsson, et al. (2001)],
to examine the long-run relationship between money and income. Second,
the study performs panel causality test, recently developed by Hurlin
and Venet (2001), to explore the direction of causality between the said
variables. Third, the important contribution of the present study is to
test whether relationship between money and income is homogeneous or
heterogeneous across countries.

This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section averages must be at least as large as the number of unobserved common factors. We establish consistency rates, derive the asymptotic distribution, suggest using covariates to deal with the effects of multiple unobserved common factors, and consider jackknife and recursive de-meaning bias correction procedures to mitigate the small sample time series bias. Theoretical findings are accompanied by extensive Monte Carlo experiments, which show that the proposed estimators perform well so long as the time series dimension of the panel is sufficiently large.

Using industry data for the United States and the United Kingdom, we provide new evidence on the impact of information and communications technology (ICT) capital on real output growth. The traditional industry panel data analysis fails to find a positive contribution. We argue that this is due to heterogeneity across industries, particularly in the time dimension. Pooling the data for the two countries and using a dynamic panel data estimation method yield a positive and significant effect of ICT on output growth. Individual country estimates suggest a strong impact in the United States, while results are less conclusive in the United Kingdom.

Introduction / Badi H. Baltagi, Thomas B. Fomby, R. Carter Hill -- Testing for common cyclical features in nonstationary panel data models / Alain Hecq, Franz C. Palm, Jean-Pierre Urbain -- The local power of some unit root tests for panel data / J(c)·org Breitung -- On the estimation and inference of a cointegrated regression in panel data / Chihwa Kao, Min-Hsien Chiang -- Testing for unit roots in panels in the presence of structural change with an application to OECD unemployment / Christian J. Murray, David H. Papell -- Panel data limit theory and asymptotic analysis of a panel regression with near integrated regressors / Heikki Kauppi -- Stationarity tests in heterogeneous panels / Yong Yin, Shaowen Wu -- Instrumental variable estimation of semiparametric dynamic panel data models : Monte Carlo results on several new and existing estimators / M. Douglas Berg, Qi Li, Aman Ullah -- Small sample performance of dynamic panel data estimators in estimating the growth-convergence equation : a Monte Carlo study / Nazrul Islam -- Estimation in dynamic panel data models : improving on the performance of the standard GMM estimator / Richard Blundell, Stephen Bond, Frank Windmeijer -- Nonstationary panels, cointegration in panels and dynamic panels : a survey / Badi H. Baltagi, Chihwa Kao -- Fully modified OLS for heterogeneous cointegrated panels / Peter Pedroni. - This volume is dedicated to two recent intensive areas of research in the econometrics of panel data, namely nonstationary panels and dynamic panels. It includes a comprehensive survey of the nonstationary panel literature including panel unit root tests, spurious panel regressions and panel cointegration tests. In addition, it provides recent developments in the estimation of dynamic panel data models using generalized method of moments. The volume includes eleven chapters written by twenty authors. These chapters: investigate better methods of estimating dynamic panels; develop methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels; extend the concept of serial correlation common features analysis to nonstationary panel data models; study the local power of panel unit root test statistics; derive the asymptotic distributions of various estimators for the panel cointegrated regression model; propose a unit root test in the presence of structural change; develop a new limit theory for panel data that may be cross-sectionally heterogeneous; propose stationarity tests for a heterogeneous panel data model; derive instrumental variable estimators for a semiparametric partially linear dynamic panel data model; and conduct Monte Carlo experiments to study the small sample properties of a growth convergence equation. This collection of papers should prove useful for practitioners and researchers working with panel data

In this paper we note the pitfall of cohort‐aggregated dynamic modelling from short pseudo panels when the slope parameters vary across cohorts, and this is not allowed for in estimation. By contrast, quasi‐differencing across pairs of individuals that belong to the same cohort is shown to help circumvent this problem. Some Monte Carlo simulations are then conducted to assess the relative merits of the two types of estimators under alternative data generating mechanisms.

In this paper we analyse the short- and long-run relationship between employment growth, inflation and output growth in Phillips' tradition. For this purpose we apply FMOLS, DOLS, PMGE, MGE, DFE, and VECM methods to a nonstationary heterogeneous dynamic panel including annual data for 119 countries over the period 1970-2010, and also carry out multivariate Granger causality tests. The empirical results strongly support the existence of a single cointegrating relationship between employment growth, inflation and output growth with bidirectional causality between employment growth and inflation as well as output growth, giving support to Phillips' Golden Triangle theory.