This paper provides new evidence on the sustainability of public finances in German states (Laender) by exploiting a newly compiled database covering the years 1950]2011. Unlike previous studies on Germany, we analyze fiscal sustainability by applying gsecond generationh panel cointegration...

In this paper we analyze the sustainability of public finances in the states (Laender) of the Federal Republic of Germany using an unprecedentedly comprehensive fiscal dataset for the time period from 1950 to 2011 for West German Laender and 1991 to 2011 for East German Laender, respectively. In...

The paper examines the sustainability of U.S. fiscal policy, finding substantial evidence in favor. I summarize the U.S. fiscal record from 1792-2003, critically review sustainability conditions and their testable implications, and apply them to U.S. data. I particularly emphasize the...

This study assesses fiscal sustainability in contemporary Spain at the regional level. Spain consists of 17 autonomous regions, two fiscal regimes differing in taxing autonomy, and two path-dependent types of communities with more and less legislatively recognized autonomy. Three of the 17...

Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a...

Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural...

This paper develops a unified framework for fixed and random effects estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroskedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and...

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...

One important goal of this study is to develop a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first...

This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to...