Identifying the source of dynamics in disaggregated import data

This paper uses Kennan's (1988) model to separately identify supply-side and demand-side dynamics in US import data. Dynamics arise from both autocorrelated shocks to supply- and demand-side fundamentals, and from lagged adjustment to these shocks. The model consists of a pair of partial adjustment models in which consumers and producers each attempt to follow a stochastic target level of imports subject to a quadratic adjustment cost. The model is applied to quarterly data on US imports of seven narrowly defined commodities: Autos, Beer, Cameras, Wine, Cigars, Tea, and Soap. Two main results emerge. First, adjustment costs are important on both sides of the market. Second, supply-side adjustment costs are larger than demand-side adjustment costs. © 1998 John Wiley & Sons, Ltd.     

Learning and time-varying macroeconomic volatility

This paper presents a DSGE model in which agents׳ learning about the economy can endogenously generate time-varying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors.The model is estimated using likelihood-based Bayesian methods. The endogenous gain is jointly estimated with the structural parameters of the system.The estimation results show that private agents appear to have often switched to constant-gain learning, with a high constant gain, during most of the 1970s and until the early 1980s, while reverting to a decreasing gain later on. As a result, the model can generate a pattern of volatility, which is increasing in the 1970s and falling in the second half of the sample, with a decline that can roughly match the magnitude of the so-called “Great Moderation” in the 1984–2007 period. The paper also documents how a failure to incorporate learning into the estimation may lead econometricians to spuriously find time-varying volatility in the exogenous shocks, even when these have constant variance by construction.     

Permanent and transitory wages of British men, 1975–2001: year,age and cohort effects

We examine the mean and variance–covariance structure of log-wages over calendar time and the life cycle of British men, hereby controlling for birth cohort effects. We attribute the strong increase in mean log-wage during the 1980s and 1990s to a rise in mean log-wage with the year of birth. This rise is diminishing with the year of birth, which implies lower wage inequality between cohorts with the year of birth. Wage inequality has increased during the 1980s and early 1990s and remained fairly stable in the second half of the 1990s. The year effects, however, show increasing wage inequality up to 2001, mainly due to a strong rise in transitory wage inequality. Transitory wages are strongly correlated over time and an increase in transitory wage inequality therefore has highly persistent inequality consequences. The stable wage inequality in the second half of the 1990s is attributed to lower within-cohort wage inequality for the younger cohorts. The age effects show that permanent wage inequality increases with age, in particular up to age 30 and over age 50. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting sales response for multiple time horizons and temporally aggregated data: A comparison of constant and stochastic coefficient models

Past research on time-varying sales-response models emphasized the application of different estimation techniques in examining variation in advertising effectiveness over time. This study focuses on comparing sales forecasts using constant and stochastic coefficients sales-response models. Selected constant and stochastic coefficient models are applied to six sets of bimonthly and one set of annual advertising and sales data to assess forecasting accuracy for time horizons of various lengths. Results show improved forecasting accuracy for a first-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.     

Macro Features of the Hungarian Economy since 1970

With the introduction in 1968 of the New Economic Mechanism, the reformers of the Hungarian economy hoped to solve the eficiency problem of the socialist economy. It is shown that the survival of the previous structure of prices and accumulation and their interrelationships hindered the necessary transformation of the economic structure. Quantitative aspects of this process are analysed using a closed input–output model. The computed price and production structures are in correlation, which is the consequence of such a development policy where industry's share in investments is high when the investment growth rate is high and vice versa. The ratio of the computed disaggregated and aggregated growth rate is defined as a certain efficiency measure, and this coefficient shows that the Hungarian economy could only absorb the import surplus at a decreasing rate of efficiency. Despite the improvement in the early 1980s the question remains whether the Hungarian economy is able to growth both faster and efficiently at the same time.     

CGE EVALUATION OF A UNIVERSITY'S EFFECTS ON A REGIONAL ECONOMY: AN INTEGRATED ASSESSMENT OF EXPENDITURE AND KNOWLEDGE IMPACTS

In recent years many universities have commissioned studies of the effects of their institutions on their local economies. Typically, these impact studies have concentrated on the demand-side stimuli to the regional economy that the university generates. Normally, the studies are undertaken with comparative-static input-output models. The present study employs a dynamic multiregional computable general equilibrium model to investigate supply-side as well as demand-side effects. There are a range of supply-side effects that have been investigated in the spatial econometrics literature. The supply-side impacts of the university that we examine in particular are a rise in the average skill level of the local workforce, and successful R&D outcomes. CGE modelling allows simulation of the associated productivity effects, while the dynamic features of the model allow for consequent effects on a region's population and capital stock growth rates to be taken into account.     

Fitting Regional Income Distributions in the European Union*

Cross‐sectional distribution of per capita (log) GDP across the European Union regions from 1977 to 1996 is analysed. Kernel density estimates reveal a multimodal structure of the distribution during the 1970s and early 1980s, and a tendency towards unimodality since the mid‐1980s. The distribution is further analysed by a mixture of normal densities. A two well‐separated component mixture fits the distributions in the 1970s and early 1980s. These two clusters tend to converge, supporting the idea of a process of catching up. In the mid‐1990s, a small group of very rich regions is generated by a separated component.     

Optimal inventories for repairable redundant systems with aging components

Complex systems that are required to perform very reliably are often designed to be “fault-tolerant,” so that they can function even though some component parts have failed. Often fault-tolerance is achieved through redundancy, involving the use of extra components. One prevalent redundant component configuration is the m-out-of-n system, where at least m of n identical and independent components must function for the system to function adequately.Often machines containing m-out-of-n systems are scheduled for periodic overhauls, during which all failed components are replaced, in order to renew the machine's reliability. Periodic overhauls are appropriate when repair of component failures as they occur is impossible or very costly. This will often be the case for machines which are sent on “missions” during which they are unavailable for repair. Examples of such machines include computerized control systems on space vehicles, military and commercial aircraft, and submarines.An interesting inventory problem arises when periodic overhauls are scheduled. How many spare parts should be stocked at the maintenance center in order to meet demands? Complex electronic equipment is rarely scrapped when it fails. Instead, it is sent to a repair shop, from which it eventually returns to the maintenance center to be used as a spare. A Markov model of spares availability at such a maintenance center is developed in this article. Steady-state probabilities are used to determine the initial spares inventory that minimizes total shortage cost and inventory holding cost. The optimal initial spares inventory will depend upon many factors, including the values of m and n, component failure rate, repair rate, time between overhauls, and the shortage and holding costs.In a recent paper, Lawrence and Schaefer [4] determined the optimal maintenance center inventories for fault-tolerant repairable systems. They found optimal maintenance center inventories for machines containing several sets of redundant systems under a budget constraint on total inventory investment. This article extends that work in several important ways. First, we relax the assumption that the parts have constant failure rates. In this model, component failure rates increase as the parts age. Second, we determine the optimal preventive maintenance policy, calculating the optimal age at which a part should be replaced even if it has not failed because the probability of subsequent failure has become unacceptably high. Third, we relax the earlier assumption that component repair times are independent, identically distributed random variables. In this article we allow congestion to develop at the repair shop, making repair times longer when there are many items requiring repair. Fourth, we introduce a more efficient solution method, marginal analysis, as an alternative to dynamic programming, which was used in the earlier paper. Fifth, we modify the model in order to deal with an alternative objective of maximizing the job-completion rate.In this article, the notation and assumptions of the earlier model are reviewed. The requisite changes in the model development and solution in order to extend the model are described. Several illustrative examples are included.     

Regression Models with Data‐based Indicator Variables*

Ordinary least squares estimation of an impulse‐indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a t‐distribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general‐to‐specific framework. Although White's (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse ‘intercept corrections’ is considered.     

Diversity Analysis of Structural Change Based on the Canadian Input–Output Tables

This article presents some of the results of a study conducted at Statistics Canada that involved the analysis of the variability through time of input–output structures. All structures have been analyzed in current and constant prices over the period 1961–84, but only the results about the industries' input structures in current prices are reported in this article. Structural changes are assessed over time horizons of 1, 2 and 5 years, using the Kullback, cross-entropy index formula. Structural changes in the current prices input structure are decomposed into a price and a quantity component, following a new decomposition of the entropy formula. It is shown from that decomposition that the traditional analysis of the variability of constant prices input–output structures may be quite misleading. The authors have found that structural changes generally follow a smooth path through time and tend to be cumulative in the long run, with some cyclical fluctuations in the short term. Some of the structural changes appear to be due to statistical events (establishment moves across industries, changes in methodologies, etc.) rather than reflecting real phenomena. The quantity component of structural change appears to be more important than the price component in almost all time periods and time spans, except when the Canadian economy was subjected to important price shocks during the 1970s.     

The short-run impact of the stock market appreciation of the 1980s and 1990s on U.S. income inequality

The paper uses 1980 to 2000 Panel Study of Income Dynamics (PSID) data to study the short-run effect of a stock market appreciation on U.S. household income inequality. Fixed-effects regressions suggest that a stock market appreciation raises the incomes of stockholder households more than non-stockholder households. The Gini coefficients derived from the regressions reveal a perceptible but rather volatile increase that can be attributed to the stock market appreciation, especially for the latter parts of the 1980s and 1990s. When averaged by decade, the stock market appreciation raises the Gini coefficient by about 2% for the 1980s and by 3% for the 1990s.     

M-estimators for single-index model using B-spline

The single-index model is an important tool in multivariate nonparametric regression. This paper deals with M-estimators for the single-index model. Unlike the existing M-estimator for the single-index model, the unknown link function is approximated by B-spline and M-estimators for the parameter and the nonparametric component are obtained in one step. The proposed M-estimator of unknown function is shown to attain the convergence rate as that of the optimal global rate of convergence of estimators for nonparametric regression according to Stone (Ann Stat 8:1348–1360, 1980; Ann Stat 10:1040–1053, 1982), and the M-estimator of parameter is $\sqrt{n}$ -consistent and asymptotically normal. A small sample simulation study showed that the M-estimators proposed in this paper are robust. An application to real data illustrates the estimator’s usefulness.     

Inversion copulas from nonlinear state space models with an application to inflation forecasting

We propose the construction of copulas through the inversion of nonlinear state space models. These copulas allow for new time series models that have the same serial dependence structure as a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas with flexible margins improve the fit and density forecasts of quarterly U.S. broad inflation and electricity inflation.     

The self-employed without employees—autonomous or atypical?

The 1980s and early 1990s have witnessed a substantial growth in the self-employed component of the national labour-force. Of these, around two-thirds are one-person businesses without employees. This article identifies an occupational grouping which occupies a position at the extreme point on a continuum of small business independence, virtually indistinguishable from that of employees.     

ON COMPARING MACROECONOMIC MODELS USING FORECAST ENCOMPASSING TESTS

ABSTRACT It is clearly of interest to macroeconomists to be able to evaluate whether one large-scale macroeconometric model ‘is better’ than another. Although comparisons between models are sometimes invidious, because the purposes for which the models were built differ, it is the case that formal comparisons of two models' statistical properties are rare. This is in spite of considerable theoretical advances in the econometric methodology, namely the development and use of non-nested and encompassing tests. Chong and Hendry (1986) advocate the use of the forecast encompassing regressions, where the outturns are regressed on competing (one-step-ahead) forecasts. This paper reports the findings of applying this rather easy-to-use method of comparing large scale macroeconometric models. The forecast data we use are those published by three macroeconometric modelling groups, namely: Liverpool; the National Institute; and The London Business School. Forecasts for up to three years ahead are published for unemployment, growth, and inflation, throughout the 1980's. Forecast encompassing tests fail to separate one model from another, based on one-year-ahead forecasts. Each model ‘wins’ once. However, the conclusions are not the same as using root-mean-square-forecast-error criteria, illustrating Clements and Hendry's (1994) observation that minimum root-mean-square-forecast-error is neither necessary nor sufficient for a model to have constant parameters, to provide accurate forecasts, or to encompass its rivals.     

Does the nano-patent ‘Gold rush’ lead to entrepreneurial-driven growth? Some policy lessons from China and Japan

In this study we compare the nano-patent landscapes of China and Japan in order to assess how their structures (type, fragmentation and concentration of patent ownership) affect the organization of the nanotechnology industry. We also analyze technology policy in order to assess how these interventions support technology transfer and nano-exploitation through university–industry collaboration and technology entrepreneurship. Drawing upon 20,365 patent families related to carbon nanotubes from 1994 to present, we found that two dissimilar technology policies boosting national innovation system emerged: China is more focused on forging an anchor-tenant model, while Japan exhibits a more industry-oriented model through demand-side policy intervention. Implications for firms? strategies and policy makers are discussed.     

Partially varying coefficient instrumental variables models

In this article, we study a new class of semiparametric instrumental variables models, in which the structural function has a partially varying coefficient functional form. Under this specification, the model is linear in the endogenous/exogenous components with unknown constant or functional coefficients. As a result, the ill‐posed inverse problem in a general non‐parametric model with continuous endogenous variables can be avoided. We propose a three‐step estimation procedure for estimating both constant and functional coefficients and establish their asymptotic properties such as consistency and asymptotic normality. We develop consistent estimators for their error variances. We demonstrate that the constant coefficient estimators achieve the optimal ‐convergence rate, and the functional coefficient estimators are oracle. In addition, efficiency issue of the parameter estimation is discussed and a simple efficient estimator is proposed. The proposed procedure is illustrated via a Monte Carlo simulation and an application to returns to education.     

A preference foundation for constant loss aversion

Following prospect theory and in particular the concept of loss aversion, introduced by Kahneman and Tversky (1979), we consider decision making under risk in which the decision maker’s preferences depend on a reference outcome. An outcome below this reference outcome is regarded as resulting from a loss: a loss decreases the decision maker’s basic utility more than a comparable gain increases this utility. An elegant and simple way to model this phenomenon was proposed by Shalev (2002): the utility of an outcome below the reference outcome is obtained from the basic utility by subtracting a multiple of the loss in basic utility: this multiple, the loss aversion coefficient, is constant across different reference outcomes. We provide a preference foundation for this loss aversion model.     

Instrumental variables estimation of a nearly nonstationary,heterogeneous error component model

This paper studies instrumental variables (IV) estimation for an error component model with stationary and nearly nonstationary regressors. It is assumed that the numbers of cross section and time series observations are infinite. Furthermore, autoregressive disturbances are assumed for the error component model, the structure of which may vary with individuals. The estimators considered are the Within-IV-OLS, Within-IV-GLS and IV-GLS estimators. The GLS estimators use Gohberg's formula, which is particularly useful when autoregressive structures are imposed on the disturbance terms. Sequential limit theories for the estimators are derived, and it is shown that all of the estimators have normal distributions in the limit. Additionally, Wald tests for coefficient vectors are shown to have chi-square distributions in the limit. Simulation results regarding the estimator efficiency and the size of the Wald tests are also reported. The results show that the Within-IV-GLS and IV-GLS estimators are more efficient than the Within-IV-OLS estimator in most cases and that the Wald tests keep nominal size reasonably well. The relation between the trade and budget deficits of 23 OECD nations is examined using the panel IV estimators. The empirical results support the view that the budget and trade deficits move in the same direction.