Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for a particular type of diffuse, for Minnesota-type and for hierarchical priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.

An Introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

Industrial Location and Space: New Insights

This paper tries to resolve some of the main shortcomings in the empirical literature of location decisions for new plants, i.e. spatial effects and overdispersion. Spatial effects are omnipresent, being a source of overdispersion in the data as well as a factor shaping the functional relationship between the variables that explain a firm’s location decisions. Using Count Data models, empirical researchers have dealt with overdispersion and excess zeros by developments of the Poisson regression model. This study aims to take this a step further, by adopting Bayesian methods and models in order to tackle the excess of zeros, spatial and non-spatial overdispersion and spatial dependence simultaneously. Data for Catalonia is used and location determinants are analysed to that end. The results show that spatial effects are determinant. Additionally, overdispersion is descomposed into an unstructured iid effect and a spatially structured effect. Keywords: Bayesian Analysis, Spatial Models, Firm Location. JEL Classification: C11, C21, R30.

Panel index VAR models: Specification, Estimation, Testing and Leading Indicators

This paper proposes a method to conduct inference in panel VAR models with cross unit interdependencies and time variations in the coefficients. The approach can be used to obtain multi-unit forecasts and leading indicators and to conduct policy analysis in a multiunit setups. The framework of analysis is Bayesian and MCMC methods are used to estimate the posterior distribution of the features of interest. The model is reparametrized to resemble an observable index model and specification searches are discussed. As an example, we construct leading indicators for inflation and GDP growth in the Euro area using G-7 information.

Policy and the Evolution of the US Economy: 1948-2002

This paper investigates the relationship between monetary policy and the changes experienced by the US economy using a small scale New-Keynesian model. The model is estimated with Bayesian techniques and the stability of policy parameter estimates and of the transmission of policy shocks examined. The model fits well the data and produces forecasts comparable or superior to those of alternative specifications. The parameters of the policy rule, the variance and the transmission of policy shocks have been remarkably stable. The parameters of the Phillips curve and of the Euler equations are varying.

Monetary policy with signal extraction from the bond market

Monetary policy is conducted in an environment of uncertainty. This paper sets upa model where the central bank uses real-time data from the bond market togetherwith standard macroeconomic indicators to estimate the current state of theeconomy more efficiently, while taking into account that its own actions influencewhat it observes. The timeliness of bond market data allows for quicker responsesof monetary policy to disturbances compared to the case when the central bankhas to rely solely on collected aggregate data. The information content of theterm structure creates a link between the bond market and the macroeconomythat is novel to the literature. To quantify the importance of the bond market asa source of information, the model is estimated on data for the United Statesand Australia using Bayesian methods. The empirical exercise suggests that thereis some information in the US term structure that helps the Federal Reserve toidentify shocks to the economy on a timely basis. Australian bond prices seemto be less informative than their US counterparts, perhaps because Australia is arelatively small and open economy.

The dynamics of US inflation: Can monetary policy explain the changes?

We investigate the relationship between monetary policy and inflation dynamics in theUS using a medium scale structural model. The specification is estimated with Bayesiantechniques and fits the data reasonably well. Policy shocks account for a part of the declinein inflation volatility; they have been less effective in triggering inflation responses overtime and qualitatively account for the rise and fall in the level of inflation. A number ofstructural parameter variations contribute to these patterns.

Estimating overidentified, nonrecursive, time-varying coefficients structural VARs

This paper provides a method to estimate time varying coefficients structuralVARs which are non-recursive and potentially overidentified. The procedureallows for linear and non-linear restrictions on the parameters, maintainsthe multi-move structure of standard algorithms and can be used toestimate structural models with different identification restrictions. We studythe transmission of monetary policy shocks and compare the results with thoseobtained with traditional methods.

Similarities and convergence in G-7 cycles

This paper examines the properties of G-7 cycles using a multicountry Bayesian panelVAR model with time variations, unit specific dynamics and cross country interdependences.We demonstrate the presence of a significant world cycle and show that country specificindicators play a much smaller role. We detect differences across business cycle phasesbut, apart from an increase in synchronicity in the late 1990s, find little evidence of major structural changes. We also find no evidence of the existence of an Euro area specific cycle or of its emergence in the 1990s.

How much structure in empirical models?

This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecificationof the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.

ClubMed? Cyclical fluctuations in the Mediterranean basin

We investigate macroeconomic fluctuations in the Mediterranean basin, their similarities and convergence. A model with four indicators, roughly covering theWest, the East and the Middle East and the North Africa portions of theMediterranean, characterizes well the historical experience since the early 1980.Idiosyncratic causes still dominate domestic cyclical fluctuations in many countries. Convergence and divergence coexist are local and transitory. The cyclicaloutlook for the next few years is rosier for the East than for the West.

What explains the Great Moderation in the US? A structural analysis

This paper investigates what has caused output and inflation volatility to fall in the USusing a small scale structural model using Bayesian techniques and rolling samples. Thereare instabilities in the posterior of the parameters describing the private sector, the policyrule and the standard deviation of the shocks. Results are robust to the specification ofthe policy rule. Changes in the parameters describing the private sector are the largest,but those of the policy rule and the covariance matrix of the shocks explain the changes most.

Price differentials in monetary unions: The role of fiscal shocks

We study the effect of regional expenditure and revenue shocks on price differentials for47 US states and 9 EU countries. We identify shocks using sign restrictions on the dynamicsof deficits and output and construct two estimates for structural price differentials dynamics which optimally weight the information contained in the data for all units. Fiscal shocks explain between 14 and 23 percent of the variability of price differentials both in the US and in the EU. On average, expansionary fiscal disturbances produce positive price differential responses while distortionary balance budget shocks produce negative price differential responses. In a number of units, price differential responses to expansionary fiscal shocks are negative. Spillovers and labor supply effects partially explain this pattern while geographical, political, and economic indicators do not.

Fixed and random effects in Classical and Bayesian regression

This paper proposes a common and tractable framework for analyzingdifferent definitions of fixed and random effects in a contant-slopevariable-intercept model. It is shown that, regardless of whethereffects (i) are treated as parameters or as an error term, (ii) areestimated in different stages of a hierarchical model, or whether (iii)correlation between effects and regressors is allowed, when the sameinformation on effects is introduced into all estimation methods, theresulting slope estimator is also the same across methods. If differentmethods produce different results, it is ultimately because differentinformation is being used for each methods.