Convergence in European GDP series: a multivariate common converging trend–cycle decomposition

Convergence in the gross domestic product series of five European countries is empirically identified using multivariate time series models that are based on unobserved components with dynamic converging properties. We define convergence in terms of a decrease in dispersion over time and model this decrease via mechanisms that allow for gradual reductions in the ranks of covariance matrices associated with the disturbance vectors driving the unobserved components of the model. The inclusion of such convergence mechanisms within the formulation of unobserved components makes the identification of various types of convergence possible. The common converging component model is estimated for the per capita gross domestic product of five European countries: Germany, France, Italy, Spain and the Netherlands. It is found that convergence features in trends and cycles are present and are associated with some key events in the history of European integration. Copyright © 2004 John Wiley & Sons, Ltd.     

Corruption clubs: endogenous thresholds in corruption and development

The relationship between corruption and economic development is characterised by three stylised facts: (i) a strong negative correlation between corruption and development (ii) countries can remain trapped in high corruption-low development or low corruption-high development equilibria (iii) amongst intermediate levels of development corruption levels are more variable, some countries have high corruption and others low corruption. This paper argues that existing models are consistent with the first two only and demonstrates how these models might be extended to capture all three. The paper searches for the location of corruption clubs within the data and provides some explanation of their cause.     

Much ado about making money: the impact of disclosure,news and rumors on the formation of security market prices over time

Journal of Economic Interaction and Coordination - This article develops an agent-based model of security market pricing process, capable to capture main stylised facts. It features collective...     

On the problem of calibrating an agent based model for financial markets

Agent based models are very widely used in different disciplines. In financial markets, they can be used to explain well known features called stylised facts and fit statistical properties of data. For this reason, they can model price movements better than standard models using gaussianity. Calibration and validation are essential issues in agent-based modeling. However, calibrating such models is not yet sufficiently considered in the literature. In this paper, a Nelder–Mead simplex algorithm coupled with threshold accepting algorithm (Gilli and Winker in Comput Stat Data Anal 42:299–312, 2003) and a genetic algorithm have been implemented to calibrate the model presented by Farmer and Joshi (J Econ Behav Org 49:149–171, 2002) and the outcomes have been compared and discussed. The data used are closing prices of S&P500 Composite index and a particular attention has been devoted to the choice of the objective function.     

Heterogeneity in Economics

This paper argues that heterogeneity of agents’ characteristics plays a fundamental role in the economy and should do so in economic models. Three aspects are considered. Firstly the notion that assuming heterogeneity was a solution to the problem posed by the results of Sonnenschein Mantel and Debreu is considered and it is suggested that the more pragmatic approach adopted by Hildenbrand is likely to be more productive. Next the role of adaptation and evolution which necessarily involve variety or heterogeneity is examined. It is suggested that heterogeneity will persist since agents will only slowly learn to adapt and that in the meantime the environment will change. Lastly the role of heterogeneity in financial markets is examined. It is suggested that heterogeneous and varying expectations may account for many of the stylised facts which do not seem to be consistent with the standard financial markets model.“Variety is the spice of life”     

Mean Reversion of Interest Rates in the Eurocurrency Market

One stylised fact to emerge from the empirical analysis of interest rates is that the unit-root hypothesis in nominal interest rates cannot be rejected. However, using the panel date unit-root test IM, Pesaran and Shin (1997), we find support for the mean-reverting property of Eurocurrency rates. Thus, neither a vector-error-correction model nor a vector autoregressive model in differences is appropriate for modelling Eurocurrency rates. Instead, conventional modelling strategies with level data are appropriate. Furthermore, the finding of stationary interest rates supports uncovered interest parity, and hence the convergence hypothesis of interest rates. This in turn suggests a limited role for a monetary authority to affect domestic interest rates.     

Detrending,stylized facts and the business cycle

The stylized facts of macroeconomic time series can be presented by fitting structural time series models. Within this framework, we analyse the consequences of the widely used detrending technique popularised by Hodrick and Prescott (1980). It is shown that mechanical detrending based on the Hodrick–Prescott filter can lead investigators to report spurious cyclical behaviour, and this point is illustrated with empirical examples. Structural time-series models also allow investigators to deal explicitly with seasonal and irregular movements that may distort estimated cyclical components. Finally, the structural framework provides a basis for exposing the limitations of ARIMA methodology and models based on a deterministic trend with a single break.     

Traces of the Iron Curtain: A multivariate analysis of regional cohesion in Europe

The present study aims to test relative welfare differences among regions in Europe, so as to examine whether the post-communist era has led to more socio-economic cohesion in Europe. The performance of European regions is analysed, compared, and assessed by using the Regional Competitiveness Index (RCI) and stylised fixed nominal categories. The current status of regional cohesion is tested on the basis of detailed data on 268 NUTS 2 European regions by using a robust methodology oriented towards univariate comparison of location parameters, multivariate classification by the decision tree and CHAID algorithm, and comparison of nominal variables with four values based on density plots.Multivariate classification appears to offer statistically excellent results with an overall correct prediction rate for post-socialist and capitalist regions in Europe of 99.6%. The research results from the Higher education and Innovation pillars, reveal a convergence of capitalist and post-socialist regions with capital cities and a divergence of regions with administrative capitals and other regions. Relatively, the two groups which perform best are both groups with capitals, while the group of capitalist regions with a capital city is significantly better in almost all pillars.The key message is that the transition of post-socialist regions is not yet over. Capitalist regions in Europe perform better than post-socialist regions in eight of the nine pillars of regional competitiveness. Our research results also reveal that the group of post-socialist regions without capital cities are significantly lagging behind the rest of the regions in Europe, and thus form the most vulnerable group of European regions. As there is data continuity in the official RCI classification and measurement, policy makers will be able to compare the performance of their own regions over time and to design appropriate concerted strategies accordingly. From this perspective, our study draws several interesting lessons and results for policy makers at various levels. Place-based regional planning and policy based on our analysis framework may be helpful in developing effective measures to cope with the socio-economic legacy of the “Iron Curtain” and get closer to regional cohesion in Europe.     

Economic Integration and Regional Business Cycles: Evidence from the Iberian Regions*

This paper provides evidence of the positive impact of economic integration on EU regions’ business cycles convergence by focusing on two neighbouring countries: Spain and Portugal. We show that while a rise in cross‐country business cycle correlation has also been experienced by other European countries, it has been relatively more pronounced for Iberian regions. Econometric evidence suggests that the existence of an administrative border, the economic size of regions and their industrial structures can explain a substantial proportion of regional cycles.     

Trends and cycles in economic time series: A Bayesian approach

Trends and cyclical components in economic time series are modeled in a Bayesian framework. This enables prior notions about the duration of cycles to be used, while the generalized class of stochastic cycles employed allows the possibility of relatively smooth cycles being extracted. The posterior distributions of such underlying cycles can be very informative for policy makers, particularly with regard to the size and direction of the output gap and potential turning points. From the technical point of view a contribution is made in investigating the most appropriate prior distributions for the parameters in the cyclical components and in developing Markov chain Monte Carlo methods for both univariate and multivariate models. Applications to US macroeconomic series are presented.     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

Housing, credit, and real activity cycles: Characteristics and comovement

This paper describes the characteristics and comovement of cycles in house prices, residential investment, credit, interest rates, and real activity in advanced economies during the past 25 years. Stylized facts and regularities are uncovered using a dynamic generalized factor model and spectral techniques. House price cycles are found to lead credit and real activity over the long term, while in the short to medium term the relationship varies across countries. Interest rates tend to lag other cycles at all time horizons. Although global factors are important, the US business cycle, housing cycle and interest rate cycle generally lead the respective cycles in other countries over all time horizons, while the US credit cycle leads mainly over the long term.     

The housing bubble in real-time: the end of innocence

Market agents suffering through unanticipated boom-bust cycles would find extremely useful analytical techniques capable of serving as an early warning system. Unobserved components models and cointegration analysis are valuable in this respect. The stylized facts from unobserved components models alone do not suffice, but coupled with results from the Johansen cointegration test provided early evidence of the housing bubble and of its denouement. The paper uses real-time data vintages and shows that by 1998 the relationship between the smoothed growth rates of house prices and of per capita income was in uncharted territory. Moreover, the actual growth rates are cointegrated. This is important, as it establishes that any disequilibrium between the two becomes less tenable as its magnitude increases. By 2003, the disequilibrium was spectacular, yet it grew for another 4 years. In effect, we did not have to wait until 2008; the gruesome ending was predictable ex ante. Ironically, the greatest financial delusion of all occurred in an age that revered rationality, market efficiency, and the financial enlightenment of the TBTF actors. The empirical findings of this paper are a major problem for the rational expectations hypothesis and the remnants of the EMH.     

The advantage of avoiding the Armington assumption in multi-region models

Most computable general equilibrium models that describe interactions between multiple regions employ the so-called Armington assumption that goods that are produced in different regions are imperfect substitutes for each other. The paper describes an alternative algorithm based on the assumption of perfect substitutability that accommodates the observation that each region produces all traded goods. For models with many regions and less-that-perfectly mobile factors, the alternative algorithm greatly reduces convergence time. The algorithm is illustrated by comparing two static multi-region models of the 50 U.S. states and the District of Columbia, with and without the Armington assumption. The model without the Armington assumption is then extended to a dynamic 51-region model with agents that have perfect foresight over 6 periods.     

A multivariate unobserved component analysis of US housing market

This study explores price dynamics and price relationships in the US housing market with a focus on four regions: Northeast, Midwest, South, and West. It applies a multivariate state-space model to identify the common trends and common cycles in US regional markets. The study finds that the principal source of secular price variability in the Northeast and West markets is due to two common stochastic trends, while a large share of transitional price variability in the Northeast, West and Midwest originates from three common stochastic cycles. The study estimates the relationships between the common unobserved components and economic variables and finds that unemployment, federal funds rate, corporate default risk, economic expansion, unanticipated inflation in the construction market are significant underlying economic phenomena that impact the evolution of the common movements in both the short run and the long run housing dynamics. Authorship is equally shared between the authors.     

Chaos theory and social science: A methodological analysis

This article investigates the relevance of chaos theory for social science. The application of chaos models in the analysis of social phenomena is accompanied by some important scientific problems. First, whether observations of social phenomena are generated by nonlinear dynamics cannot be ascertained beyond considerable doubt, especially when these observations contain measurement errors; i.e., there is a problem of external validity. Secondly, and more important, as a theory of irregular cyclical social behaviour is lacking, inductive-statistical theoryformation about such behaviour, which is based on fitting a mathematical model of chaos to observations of social phenomena, is impossible unless additional information is used concerning the context and circumstances wherein the social phenomena occur; i.e., the internal validity of any theoretical explanation that is derived from only a fitted mathematical model (of chaos) cannot be assessed. So, research into the suggestion derived from mathematical chaos theory that irregular cycles may be present in the development of social phenomena over time requires theory-formation about irregular cyclical social behaviour on the basis of established theoretical insights and empirical evidence instead of fitting sophisticated mathematical models of chaos to observations of social phenomena.     

Heckscher–Ohlin and agglomeration

We argue that embedding endowment-based comparative advantage within a standard NEG framework helps solve the indeterminacy due to multiple equilibria and the ambiguity concerning the relation between integration and specialisation (monotonicity versus non-monotonicity) typical of the NEG literature. In particular, we show that if endowments are in the FPE set, the process of trade integration involves an overshooting of international specialisation and relative factor prices with respect to the free trade level determined by factor abundance. In contrast, if endowments are outside the FPE set, then, even in the presence of agglomeration forces, specialisation and factor prices are monotonically related to trade costs, as implied by the standard trade theory. We argue that the model can shed light on some puzzling stylised facts.     

Does Evidence on Regional Economic Convergence Depend on the Estimation Strategy? Outcomes from Analysis of a Set of NUTS2 EU Regions

Abstract

The regional economic convergence/divergence issue has been discussed extensively recently, but results obtained are not always interpretable unequivocally as a consequence of the different estimation strategies used. As it is widely recognized, the most common theoretical framework applied to measure the speed of economic convergence among countries or regions remains the β-convergence approach, linked to the neoclassical Solow model. There have been many attempts to consider variations of the basic cross-sectional specification ranging from panel data models to Bayesian spatial econometric techniques. The application of spatial econometric methodologies is an essential tool for proper statistical inference on regional data. In this context, the aim of this paper is to connect the different results obtained in the literature. More specifically, we address whether or not evidence on convergence depends upon the estimation strategy, by taking the same set of data and systematically comparing the results obtained from different estimation strategies. The results from a set of NUTS2 EU regions conclude that both the model implied by the cross-sectional analysis and the one referring to the space-time dynamics incorporated in the panel specification point to convergence. The concept of convergence implied is, however, quite different, as demonstrated throughout the paper.     

Toward an understanding of market resilience: market liquidity and heterogeneity in the investor decision cycle

During liquidity shocks such as occur when margin calls force the liquidation of leveraged positions, there is a widening disparity between the reaction speed of the liquidity demanders and the liquidity providers. Those who are forced to sell typically must take action within the span of a day, while those who are providing liquidity do not face similar urgency. Indeed, the flurry of activity and increased volatility of prices during the liquidity shocks might actually reduce the speed with which many liquidity providers come to the market. To analyze these dynamics, we build upon previous agent-based models of financial markets, and specifically the Preis et. al (Europhys Lett 75(3):510–516, 2006) model, to develop an order-book model with heterogeneity in trader decision cycles. The model demonstrates an adherence to important stylized facts such as a leptokurtic distribution of returns, decay of autocorrelations over moderate to long time lags, and clustering volatility. Consistent with empirical analysis of recent market events, we demonstrate the impact of heterogeneous decision cycles on market resilience and the stochastic properties of market prices.     

The relationship between the Beveridge–Nelson decomposition and other permanent–transitory decompositions that are popular in economics

The Beveridge–Nelson (BN) decomposition is a model-based method for decomposing time series into permanent and transitory components. When constructed from an ARIMA model, it is closely related to decompositions based on unobserved components (UC) models with random walk trends and covariance stationary cycles. The decomposition when extended to I(2)$I\left(2\right)$ models can also be related to non-model-based signal extraction filters such as the HP filter. We show that the BN decomposition provides information on the correlation between the permanent and transitory shocks in a certain class of UC models. The correlation between components is known to determine the smoothed estimates of components from UC models. The BN decomposition can also be used to evaluate the efficacy of alternative methods. We also demonstrate, contrary to popular belief, that the BN decomposition can produce smooth cycles if the reduced form forecasting model is appropriately specified.