Panel Data Econometrics in R: The plm Package

Panel data econometrics is obviously one of the main fields in the profession, but most of the models used are difficult to estimate with R. plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference.

Non-Ricardian Households and Fiscal Policy in an Estimated DSGE Model of the Euro Area

In this paper, we revisit the effects of government spending shocks on private aggregate consumption within an estimated New-Keynesian DSGE model of the euro area featuring non-Ricardian households and a relatively detailed fiscal policy set up. Employing Bayesian inference methods, we show that the presence of non-Ricardian households is in general conducive to raising the level of aggregate consumption in response to government spending shocks when compared with the benchmark specification without non-Ricardian households. As a practical matter, however, we find that there is only a fairly small chance that government spending shocks crowd in aggregate consumption, mainly because the estimated share of non-Ricardian households is relatively low, but also due to the large negative wealth effect induced by the highly persistent nature of government spending shocksfiscal policy, DSGE models, non-Ricardian households.

What tames the Celtic tiger? portfolio implications from a multivariate Markov switching model

We use multivariate regime switching vector autoregressive models to characterize the time-varying linkages among the Irish stock market, one of the top world performers of the 1990s, and the US and UK stock markets. We find that two regimes, characterized as bear and bull states, are required to characterize the dynamics of excess equity returns both at the univariate and multivariate level. This implies that the regimes driving the small open economy stock market are largely synchronous with those typical of the major markets. However, despite the existence of a persistent bull state in which the correlations among Irish and UK and US excess returns are low, we find that state comovements involving the three markets are so relevant to reduce the optimal mean variance weight carried by ISEQ stocks to at most one-quarter of the overall equity portfolio. We compute time-varying Sharpe ratios and recursive mean-variance portfolio weights and document that a regime switching framework produces out-of-sample portfolio performance that outperforms simpler models that ignore regimes. These results appear robust to endogenizing the effects of dynamics in spot exchange rates on excess stock returns.Stock exchanges

Adjustment of Inputs and Measurement of Technical Efficiency: A Dynamic Panel Data Analysis of the Egyptian Manufacturing Sectors

The purpose of this paper is to construct a dynamic stochastic production frontier incorporating the sluggish adjustment of inputs, to measure the speed of adjustment of output, and to compare the technical efficiency estimates from this dynamic model to those from a static model. By assuming instantaneous adjustment of all inputs, a static model may underestimate technical efficiency of a production unit in the short-run. However, in this paper I show that under the assumption of similar adjustment speed for all inputs, a linear partial adjustment scheme for output characterizes the dynamic production frontier. The dynamic frontier with time-invariant technical efficiency is estimated using the system GMM (generalized method of moments) estimator. Applying the model and estimation method on a panel dataset spanning nine years of data on private manufacturing establishments in Egypt, I find that 1) the speed of adjustment of output is significantly lower than unity, 2) the static model underestimates technical efficiency by 4.5 percentage points on average, and 3) the ranking of production units based on their technical efficiency measures changes when the lagged adjustment process of inputs is taken into account.

Does government spending crowd in private consumption? Theory and empirical evidence for the euro area

In this paper, we revisit the effects of government spending shocks on private consumption within an estimated New-Keynesian DSGE model of the euro area featuring non-Ricardian households. Employing Bayesian inference methods, we show that the presence of non-Ricardian households is in general conducive to raising the level of consumption in response to government spending shocks when compared with the benchmark specification without non-Ricardian households. However, we find that there is only a fairly small chance that government spending shocks crowd in consumption, mainly because the estimated share of non-Ricardian households is relatively low, but also due to the large negative wealth effect induced by the highly persistent nature of government spending shocks. JEL Classification: E32, E62DSGE modelling, euro area, Fiscal Policy, non-Ricardian households

Random Walks and Non-Linear Paths in Macroeconomic Time Series: Some Evidence and Implications

This paper investigates whether the inherent non-stationarity of macroeconomic time series is entirely due to a random walk or also to non-linear components. Applying the numerical tools of the analysis of dynamical systems to long time series for the US, we reject the hypothesis that these series are generated solely by a linear stochastic process. Contrary to the Real Business Cycle theory that attributes the irregular behavior of the system to exogenous random factors, we maintain that the fluctuations in the time series we examined cannot be explained only by means of external shocks plugged into linear autoregressive models. A dynamical and non-linear explanation may be useful for the double aim of describing and forecasting more accurately the evolution of the system. Linear growth models that find empirical verification on linear econometric analysis, are therefore seriously called in question. Conversely non-linear dynamical models may enable us to achieve a more complete information about economic phenomena from the same data sets used in the empirical analysis which are in support of Real Business Cycle Theory. We conclude that Real Business Cycle theory and more in general the unit root autoregressive models are an inadequate device for a satisfactory understanding of economic time series. A theoretical approach grounded on non-linear metric methods, may however allow to identify non-linear structures that endogenously generate fluctuations in macroeconomic time series.Random Walks, Real Business Cycle Theory, Chaos

DSGE Models in a Data-Rich Environment.

Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.DSGE models ; Measurement error ; Large data sets ; Factor models ; Forecasting ; MCMC ; Bayesian estimation.

Is the New Keynesian Phillips curve flat?

Macroeconomic data suggest that the New Keynesian Phillips curve is quite flat - despite microeconomic evidence implying frequent price adjustments. While real rigidities may help to account for the conflicting evidence, we propose an alternative explanation: if price markup/cost-push shocks are persistent and negatively correlated with the labor share, the latter being a widely used measure for marginal costs, the estimated pass-through of measured marginal costs into inflation is limited, even if prices are fairly flexible. Using a standard New Keynesian model, we show that the GMM approach to the New Keynesian Phillips curve leads to inconsistent and upward biased estimates if cost-push shocks indeed are persistent. Monte Carlo experiments suggest that the bias is quite sizeable: we find average price durations estimated as high as 12 quarters, when the true value is about 2 quarters. Moreover, alternative estimators appear to be biased as well, while standard diagnostic tests fail to signal a misspecification of the model. JEL Classification: E30, C15Cost-push shocks, GMM estimation, New Keynesian Phillips curve, Price Rigidities

Determining the number of breaks in a piecewise linear regression model

In this paper we propose a sequential method for determining the number of breaks in piecewise linear structural break models. An advantage of the method is that it is based on standard statistical inference. Tests available for testing linearity against switching regression type nonlinearity are applied sequentially to determine the number of regimes in the structural break model. A simulation study is performed in order to investigate the finite-sample behaviour of the procedure and to compare it with other alternatives. We find that our method works well in comparison for both single and multiple break cases.Model specification; multiple structural breaks.

DSGE Models in a Data-Rich Environment

Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.