Firm Size and Growth Rate Variance: the Effects of Data Truncation

This paper discusses the effects of the existence of natural and/or exogenously imposed thresholds in firm size distributions, on estimations of the relation between firm size and variance in firm growth rates. We explain why the results in the literature on this relationship are not consistent. We argue that a natural threshold (0 number of employees or 0 total sales) and/or the existence of truncating thresholds in the dataset, can lead to upwardly biased estimations of the relation. We show the potential impact of the bias on simulated data, suggest a methodology to improve these estimations, and present an empirical analysis based on a comprehensive dataset of Dutch manufacturing and service firms. The only stable relation between firm size and growth rate variance is negative regardless of how we define the measure of firm growth.firm growth, growth rates variance; truncation; thresholds

A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance

We test the importance of multivariate information for modelling and forecasting in- flation's conditional mean and variance. In the literature, the existence of inflation's conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of economy-wide dynamic common fac- tors, some of which are conditionally heteroskedastic and some are not. Modelling the conditional heteroskedasticity of the common factors can thus improve the forecasts of inflation's conditional mean and variance. Moreover, it allows to detect and predict con- ditional correlations between inflation and other macroeconomic variables, correlations that might be exploited when planning monetary policies. The Dynamic Factor GARCH (DF-GARCH) by Alessi et al. [2006] is used here to exploit the relations between inflation and the other macroeconomic variables for inflation fore- casting purposes. The DF-GARCH is a dynamic factor model as the one by Forni et al. [2005], with the addition of an equation for the evolution of static factors as in Giannone et al. [2004] and the assumption of heteroskedastic dynamic factors. When comparing the Dynamic Factor GARCH with univariate models and with the classical dynamic factor models, the DF-GARCH is able to provide better forecasts both of inflation and of its conditional variance.Inflation, Factor Models, GARCH

Spatial differentiation in industrial dynamics A core-periphery analysis based on the Pavitt-Miozzo-Soete taxonomy

We compare the industrial dynamics in the core, semi-periphery and periphery in The Netherlands in terms of firm entry-exit, size, growth and sectoral location patterns. The contribution of our work is to provide the first comprehensive study on spatial differentiation in industrial dynamics for all firm sizes and all sectors, including services. We find that at the aggregate level the spatial pattern of industrial dynamics is consistent with the spatial product lifecycle thesis: entry and exit rates are highest in the core and lowest in the periphery, while the share of persistently growing firms is higher in the periphery than in the core. Disaggregating the analysis to the sectoral level following the Pavitt-Miozzo-Soete taxonomy, findings are less robust. Finally, sectoral location patterns are largely consistent with the spatial product lifecycle model: Fordist sectors are over-represented in the periphery, while sectors associated with the ICT paradigm are over-represented in the core, with the notable exception of science-based manufacturing.Entry, exit, spatial product lifecycle, Fordist paradigm, ICT paradigm

Framing the empirical findings on firm growth

This paper proposes a general framework to account for the divergent results in the empirical literature on the relation between firm sizes and growth rates, and on many results on growth autocorrelation. In particular, we provide an explanation for why traces of the LPE sometimes occur in conditional mean (i.e. OLS) autoregressions of firm size or firm growth, and in conditional median (i.e. least absolute deviation) autoregressions, but never in high or low quantile autoregressions. Based on an original empirical analysis of the population of manufacturing firms in the Netherlands between 1994 and 2004, we find that there is no peculiar role played by the median of the growth distribution, which is approximately equal to zero independent of firm size. In economic terms, this is equivalent to saying that most of the phenomena of interest for industrial dynamics can be studied without reference to the behaviour of the median firm, and many "average" relations retrieved in the literature, starting from the negative relation between average size and average growth, are driven by the few dynamic firms in the sample rather than the many stable ones. Moreover, we observe the tent shape of the empirical firm growth rate distribution and confirm the skewness-size and the variance-size relations. The identified quantile regression patterns - autoregressive coefficients above 1 for fast decliners, and below 1 for fast growers - can be obtained by assuming negative variance-size scaling and Laplace growth rate distributions, and are robust to a mild positive relationship between skewness and size. A relationship between quantile regression patterns and previous findings is therefore uncovered.Firm growth; Law of Proportionate Effect; quantile regression; heterogeneity; variance-size scaling.

Do some Firms Persistently Outperform?

This study analyses persistence in growth rates of the entire population of Dutch manufacturing firms. Previous literature on firm growth rates shows that extreme growth events are likely to be negatively correlated over time. A rebound effect following an extreme growth event questions the existence of persistent outperformers, indicated by a positive correlation over time. By supplementing the quantile regression analyses with transition probability matrices, our study shows that `bouncing' firms co-exist with persistent outperformers. This result is robust if we exclude firms involved in acquisitions or spin offs. Differentiating among different size classes, we find that the existence of persistent outperformers is especially pronounced in micro firms. We interpret this finding as supporting the notion of a Schumpeter Mark I regime, with small firms displaying strong heterogeneity in their growth patterns, versus a Schumpeter Mark II regime, with large firms displaying less heterogeneity of growth.firm growth; heterogeneity, persistence, transition probability matrices, quantile regression

Spatial Differentiation in Industrial Dynamics. A Core-Periphery Analysis Based on the Pavitt-Miozzo-Soete Taxonomy

We compare the industrial dynamics in the core, semi-periphery and periphery in The Netherlands in terms of firm entry-exit, size, growth and sectoral location patterns. The contribution of our work is to provide the first comprehensive study on spatial differentiation in industrial dynamics for all firm sizes and all sectors, including services. We find that at the aggregate level the spatial pattern of industrial dynamics is consistent with the spatial product lifecycle thesis: entry and exit rates are highest in the core and lowest in the periphery, while the share of persistently growing firms is higher in the periphery than in the core. Disaggregating the analysis to the sectoral level following the Pavitt-Miozzo-Soete taxonomy, findings are less robust. Finally, sectoral location patterns are largely consistent with the spatial product lifecycle model: Fordist sectors are over-represented in the periphery, while sectors associated with the ICT paradigm are over-represented in the core, with the notable exception of science-based manufacturing.Entry, exit, spatial product lifecycle, Fordist paradigm, ICT paradigm

A robust criterion for determining the number of static factors in approximate factor models.

We propose a refinement of the criterion by Bai and Ng [2002] for determining the number of static factors in factor models with large datasets. It consists in multi-plying the penalty function by a constant which tunes the penalizing power of the function itself as in the Hallin and Liška [2007] criterion for the number of dynamic factors. By iteratively evaluating the criterion for different values of this constant, we achieve more robust results than in the case of fixed penalty function. This is shown by means of Monte Carlo simulations on seven data generating processes, including heteroskedastic processes, on samples of different size. Two empirical applications are carried out on a macroeconomic and a financial dataset. JEL Classification: C52Approximate factor models, Information criterion, Number of factors

A Review of Nonfundamentalness and Identification in Structural VAR Models

We review, under a historical perspective, the developement of the problem of non- fundamentalness of Moving Average (MA) representations of economic models, starting from the work by Hansen and Sargent [1980]. Nonfundamentalness typically arises when agents' information space is larger than the econometrican's one. Therefore it is impos- sible for the latter to use standard econometric techniques, as Vector AutoRegression (VAR), to estimate economic models. We re-state the conditions under which it is pos- sible to invert an MA representation in order to get an ordinary VAR, and we consider how the latter is used in the literature to assess the validity of Dynamic Stochastic Gen- eral Equilibrium models, providing some interesting examples. We believe that possible nonfundamental representations of considered models are too often neglected in the liter- ature. We consider how factor models can be seen as an alternative to VAR for assessing the validity of an economic model without having to deal with the problem of nonfun- damentalness. We then review the works by Lippi and Reichlin [1993] and Lippi and Reichlin [1994] which are the first attempts to give to nonfundamental representations the economic relevance that they deserve, and to outline a method to obtain such repre- sentations starting from an estimated VAR.Nonfundamentalness, Structural VAR, Dynamic Stochastic General Equilibrium Models, Factor Models

Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic. After presenting the model, we propose a multi-step estimation technique which combines asymptotic principal components and multivariate GARCH. We also prove consistency of the estimated conditional covariances. We present simulation results in order to assess the finite sample properties of the estimation technique. Finally, we carry out two empirical applications respectively on macroeconomic series, with a particular focus on different measures of inflation, and on financial asset returns. Our model outperforms the benchmarks in fore-casting the inflation level, its conditional variance and the volatility of returns. Moreover, we are able to predict all the conditional covariances among the observable series. JEL Classification: C52, C53Conditional Covariance, Dynamic Factor Models, Inflation forecasting, multivariate GARCH, Volatility Forecasting

A review of nonfundamentalness and identification in structural VAR models

We review, under a historical perspective, the development of the problem of nonfundamentalness of Moving Average (MA) representations of economic models. Nonfundamentalness typically arises when agents’ information space is larger than the econometrician’s one. Therefore it is impossible for the latter to use standard econometric techniques, as Vector AutoRegression (VAR), to estimate economic models. We restate the conditions under which it is possible to invert an MA representation in order to get an ordinary VAR and identify the shocks, which in a VAR are fundamental by construction. By reviewing the work by Lippi and Reichlin [1993] we show that nonfundamental shocks may be very different from fundamental shocks. Therefore, nonfundamental representations should not be ruled out by assumption and indeed methods to detect nonfundamentalness have been recently proposed in the literature. Moreover, Structural VAR (SVAR) can be legitimately used for assessing the validity of Dynamic Stochastic General Equilibrium models only if the representation associated with the economic model is fundamental. Factor models can be an alternative to SVAR for validation purposes as they do not have to deal with the problem of nonfundamentalness. JEL Classification: C32, C51, C52dynamic stochastic general equilibrium models, Factor models, Nonfundamentalness, Structural VAR