Employment Growth in Italian Local Labour Systems: Issues of Model Specification and Sectoral Aggregation

Abstract

In this paper we construct a model to estimate local employment growth in Italian local labour markets for the period 1991–2001. The model is constructed in a similar manner to the original models of Glaeser et al. (1992), Henderson et al. (1995) and Combes (2000). Our objective is to identify the extent to which the results estimated by these types of models are themselves sensitive to the model specification. In order to do this we extend the basic models by successively incorporating new explanatory variables into the model framework. In addition, and for the first time, we also estimate these same models at two different levels of sectoral aggregation, for the same spatial structure. Our results indicate that these models are highly sensitive to sectoral aggregation and classification and our results therefore strongly support the use of highly disaggregated data.     

Aggregation in Input–Output Models: Prices and Quantities

This paper develops a joint theory of aggregation of input–output quantity and price models. The main emphasis is on the problem of aggregation of industries in the models. While aggregation of quantity models is a familiar topic in economic literature, the aggregation of price models is a largely unexplored subject. Here, however, quantity and price models are considered as two parts of a single, composite flow model. This understanding implies that each result in the quantity model has dual counterpart in the price model and vice versa. Through consistent use of this duality principle, significant results are developed for both model types, and a number of well-known results can be stated in a simpler way. Specifically, a number of conditions for perfect aggregation of final demands, primary inputs and industries in price models as well as quantity models are formulated. On this basis a new indicator of aggregation bias is suggested. The indicator can be decomposed into the contributions of each detailed industry, enabling the user to identify atypical industries in each group. Furthermore, the indicator can be computed even with no knowledge of the coefficients of the detailed models.     

Time aggregation and the distributional shape of unemployment duration

This paper reports empirical evidence on the sensitivity of unemployment duration regression estimates to distributional assumptions and to time aggregation. The results indicate that parameter estimates are robust to distributional assumptions, while estimates of duration dependence are not. Time aggregation does not seem to have drastic effects on the estimates in a simple parametric model like the Weibull, but can produce dramatic changes in the more complicated extended generalized gamma model. Semiparametric models for grouped data produce stable estimates, and perform much better than continuous-time models in terms of significance at high levels of time aggregation.     

A re-examination of the q theory of investment using u.s. firm data

Investment models based on Tobin's q are theoretically appealing, but they have been an empirical disappointment when applied to aggregate time-series data. This paper explores two potential explanations for the poor empirical performance of q investment models, problems arising from aggregation and imperfect competition. The results suggest that aggregation is responsible for spurious evidence of dynamic misspecification and at least partially responsible for an upward bias in estimated adjustment costs. The evidence also suggests that imperfect competition in output markets may have an effect on the investment behaviour of some firms.     

CONDITIONALLY HETEROSKEDASTIC FACTOR MODELS WITH SKEWNESS AND LEVERAGE EFFECTS

Conditional heteroskedasticity, skewness and leverage effects are well‐known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional moment conditions are proposed allowing inference by the generalized method of moments (GMM). The model is also shown to be closed under temporal aggregation. An application to daily excess returns on sectorial indices from the UK stock market provides strong evidence for dynamic conditional skewness and leverage with a sharp efficiency gain resulting from accounting for both effects. The estimated volatilitypersistence from the proposed model is lower than that estimated from models that rule out such effects. I also find that the longer the returns' horizon, the fewer conditionally heteroskedastic factors may be required for suitable modeling and the less strong is the evidence for dynamic leverage. Some of these results are in line with the main findings of Harvey and Siddique ( 1999 ) and Jondeau and Rockinger ( 2003 ), namely that accounting for conditional skewness impacts the persistence in the conditional variance of the return process. Copyright © 2012 John Wiley & Sons, Ltd.     

供应链网格资源服务和服务聚合模型研究

在研究供应链网格(SCGrid)的体系结构和模型的基础上,深入的分析了SCGrid中的资源的表示和聚合方法,论述了该层中应用到的两个重要模型:SOA资源服务模型和服务聚合模型。     

计量模型中的加总偏误与内生性：一种数值模拟方法

理论文献已经证明了细分数据模型的内生性和加总过程产生的内生性是加总偏误的根本原因。但是由于内生性涉及误差项与回归量之间的相关性问题,试图通过实证方法审视这两类内生性对加总偏误的影响变得比较困难,而数值模拟却是一个较为理想的方法。在数值模拟中,我们通过控制随机变量的分布形式以及随机变量之间的相关程度,进而对两类内生性因素产生的加总偏误进行全面细致的考察。本文的研究将为加总偏误的内生性解释提供有力证据。     

Further model-based estimates of US total manufacturing production capital and technology, 1949–2005

Production capital and technology (i.e., total factor productivity) in US manufacturing are fundamental for understanding output and productivity growth of the US economy but are unobserved at this level of aggregation and must be estimated before being used in empirical analysis. Previously, we developed a method for estimating production capital and technology based on an estimated dynamic structural economic model and applied the method using annual SIC data for 1947–1997 to estimate production capital and technology in US total manufacturing. In this paper, we update this work by reestimating the model and production capital and technology using annual SIC data for 1949–2001 and partly overlapping NAICS data for 1987–2005.     

Discrete choice models and the housing price and travel to work elasticities of location demand

Multinomial logit and nested logit models of mode choice in travel to work and housing location choice are estimated from 1970 U.S. census data aggregated to small zones of the Chicago SMSA. The estimated models are then used to derive the “housing rent,” “travel time,” and “travel cost” elasticities of location demand. The effects of sampling variation, sample size, attribute inclusion, model specification, and estimation method on the estimated elasticities are evaluated and found to be important. The elasticities are also compared and found to agree with those obtained from other discrete choice models and, in the case of “housing rent,” with estimates obtained from models based on other theoretical structure.     

Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty

Macroeconomic data are subject to data revisions. Yet, the usual way of generating real-time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real-time vintages. Second, the BVAR model is jointly estimated with a model of data revisions such that forecasts are conditioned on estimates of the ‘true’ values. We find that this second method generally improves upon conventional practice for density forecasting, especially for the United States.     

The Extended Generalized Gamma Model and its Special Cases: Applications to Modeling Marriage Durations

The paper demonstrates how various parametric models for duration data such as the exponential, Weibull, and log-normal may be embedded in a single framework, and how such competing models may be assessed relative to a more comprehensive one. To illustrate the issues addressed, the survival patterns of marriages among 1203 Swedish men born 1936–1964 are studied by parametric and non-parametric survival methods. In particular, we study the sensitivity of model-choice with respect to level of aggregation of the time variable; and of covariate-effects with respect to the model chosen. In accordance with previous works our empirical results indicate that the choice of a parametric model for the duration variable is affected by the level of time aggregation. In contrast to previous results, however, our analysis shows that estimates of covariate effects are not always robust to distributional assumptions for the duration variable.     

An examination of the equilibrium specification and structure of production for Canadian telecommunications

Multiple-output models of Canadian telecommunications production are estimated under different production equilibria. A specification test is conducted between the short- and long-run equilibrium models and the long-run equilibrium is rejected. In order to capture the nature of the disequilibrium, a dynamic cost of adjustment model is estimated for Bell Canada. There are significant adjustment costs and it is estimated that for $1.00 of marginal capital costs the carrier must incur an additional cost of $0.30 to install the new capital into the production process.     

A disaster response model driven by spatial–temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts.     

Comparative stability analysis of demand-side and supply-side input-output models

This paper presents the results of a comparison of technical coefficient stability in demand-side and supply-side input-output models using a seven-sector aggregation of the 1947, 1958, 1963, 1967, 1972, and 1977 U.S. input-output tables. Sectoral and total output forecasts of the two models, generated with known final demand and value added figures from all subsequent tables, are compared to known output figures. Although the demand-side model performs somewhat better in terms of total output forecasts, the supply-side model provides better forecasts for a larger number of sectors. This analysis suggests that both models should be used in order to determine the extent to which an economy or a sector are demand- or supply-driven.     

Hedging with the Nikkei index futures: The convential model versus the error correction model

This study estimates and compares the hedge ratios of the conventional and the error correction models using Japan's Nikkei Stock Average (NSA) index and the NSA index futures with different time intervals. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the hedge ratios obtained by using the error correction model do a better job in reducing the risk of the cash position than those from the conventional model. In addition, this paper evaluates the effects of temporal aggregation on hedge ratios. It is found that temporal aggregation has important effects on the hedge ratio estimates.     

An econometric procedure for estimation of a generalized systemic gravity model under incomplete information about the system

Gravity spatial interaction models have a long history and a wide scope of empirical applications, Alonso (1973) proposed a generalized model whose structure subsumes major versions of the gravity model in the literature as special cases. Little attention has been given to how the general model might be estimated, or if it is estimable at all. This paper develops an econometric procedure for situations where all places in a geographic system are not included in the observations. The general model and various constrained versions are empirically estimated on a sample of intermetropolitan migration data and compared.     

Forecasting discount window borrowing

This paper compares the predictive performance of five recent models of banks' demand for borrowed reserves using post-sample simulation. The findings indicate that models which incorporate observed nonlinearity and switching in the borrowings-interest rate spread relationship outperform the linear nonswitching model. However, the best performance is obtained from the model in which switching and aggregation are considered in the theoretical derivation. Forecasting the level of borrowed reserves is critical to the FOMC reserve targeting procedure. Hence, a comparison of model robustness and stability using post-sample simulation provides useful information to the FOMC in its search for a reliable borrowed reserves demand model.     

Cross-sectional tests of asset pricing models with full-rank mimicking portfolios

In the standard tests of asset pricing models, factor risk premia are estimated on a test asset span so that models are tested with degrees of freedom reduced by the number of factors. Risk premia of traded factors can be further restricted to be equal to their expected returns, but such restrictions cannot be imposed on models with nontraded factors, which may create a problem of testing without full restrictions or on unequal asset spans across models. We propose a full-rank mimicking portfolio approach by projecting nontraded factors onto a combined span of test assets and benchmark traded factors. Under the Hansen-Jagannathan distance framework, we demonstrate that full-rank mimicking portfolios can provide improved power and fair performance comparison against a benchmark model in both specification and model comparison tests.     

Epidemic models on social networks—With inference

This article considers stochastic models for the spread of an infection in a structured community, where this structured community is itself described by a random network model. Some common network models and transmission models are defined and large population properties of them are presented. The focus is then shifted to statistical methodology: what can be estimated and how, depending on the underlying network, transmission model, and the available data? This survey article discusses several different scenarios, giving references to publications where more details can be found, and identifies important open problems.     

Temporal aggregation of volatility models

In this paper, we consider temporal aggregation of volatility models. We introduce semiparametric volatility models, termed square-root stochastic autoregressive volatility (SR-SARV), which are characterized by autoregressive dynamics of the stochastic variance. Our class encompasses the usual GARCH models and various asymmetric GARCH models. Moreover, our stochastic volatility models are characterized by multiperiod conditional moment restrictions in terms of observables. The SR-SARV class is a natural extension of the class of weak GARCH models. This extension has four advantages: (i) we do not assume that fourth moments are finite; (ii) we allow for asymmetries (skewness, leverage effect) that are excluded from weak GARCH models; (iii) we derive conditional moment restrictions and (iv) our framework allows us to study temporal aggregation of IGARCH models.