A consistent nonparametric test of parametric regression functional form in fixed effects panel data models

We propose a consistent test for a linear functional form against a nonparametric alternative in a fixed effects panel data model. We show that the test has a limiting standard normal distribution under the null hypothesis, and show that the test is a consistent test. We also establish the asymptotic validity of a bootstrap procedure which is used to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test performs well for panel data with a large number of cross-sectional units and a finite number of observations across time.     

An adaptive empirical likelihood test for parametric time series regression models

We propose an adaptive empirical likelihood (EL) test for a parametric regression model against a class of alternatives for weakly dependent time series observations. The test is formulated by maximizing a standardized version of the EL statistic over a set of smoothing bandwidths. It is demonstrated that the proposed test is able to distinguish the null hypothesis from a series of local alternatives at an optimal rate.     

A rationale for law of technological substitution

This paper links the popular S-shaped curves often used to represent the path of technological substitution with the evolutionary model of a self-organizing industrial system. In particular, the empirical Fisher-Pry law of technological substitution is given a conceptual rationale by treating technological innovations as structural fluctuations which affect an industry's growth path as defined by a set of non-linear differential equations. In so doing, the paper draws heavily on the articles by Allen and Batten.     

A simple approach to the parametric estimation of potentially nonstationary diffusions

A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic differential equations. We specify a parametric class of diffusions and estimate the parameters of interest by minimizing criteria based on the integrated squared difference between kernel estimates of the drift and diffusion functions and their parametric counterparts. The procedure does not require simulations or approximations to the true transition density and has the simplicity of standard nonlinear least-squares methods in discrete time. A complete asymptotic theory for the parametric estimates is developed. The limit theory relies on infill and long span asymptotics and is robust to deviations from stationarity, requiring only recurrence.     

Positively negative: the impact of negativity upon the political consumer

• For years there has been an ongoing debate as to the role and impact that ‘marketing’ has had on politics. Yet, it is the case that many of the concepts associated with the field of marketing have real relevance and have, in fact, been employed within the field of political campaigning for many decades. This is an empirical paper that focuses upon the concept of political brands and the impact that current trends in campaign strategy, and in particular the growth and continued use of negative campaigning, have upon them. Using a constructivist grounded theory approach, the implications associated with such activity are examined and, in particular, its effect upon the consumer/brand relationship.
• Within the consideration of the results, it becomes clear that political brands' use of negative campaigning is somewhat shortsighted; offering short‐term gains but at the cost of long‐term damage not only to their brand image but also to the wider democratic system as it stands. In a search for political ‘brands’ that more effectively meet their needs, there appears a tendency for political consumers to now look outside of the conventional political sector. The emergent concept of the ‘negative brand’ is explored together with the implications for political consumers, political brands and the wider field of conventional party politics.

Copyright © 2007 John Wiley & Sons, Ltd.     

A minimal characterization of the covariance matrix

Summary LetX be ak-dimensional random vector with mean vectorμ and non-singular covariance matrix Σ. We show that among all pairs (a, Δ),a ∈ IR ^k , Δ ∈ IR ^k×k positive definite and symmetric andE(X−a)′ Δ⁻¹(X−a)=k, (μ, Σ) is the unique pair which minimizes det Δ. This motivates certain robust estimators of location and scale. Research supported by the Nuffield Foundation.     

An exact test for a column of the covariance matrix based on a single observation

In this paper, we derive an exact test for a column of the covariance matrix. The test statistic is calculated by using a single observation. The exact distributions of the test statistic are derived under both the null and alternative hypotheses. We also obtain an analytical expression of the power function of the test for the equality of a column of the covariance matrix to a given vector. It is shown that the information contained in a single vector is large enough to ensure a good performance of the test. Moreover, the suggested test can be applied for time-dependent multivariate Gaussian processes.     

Building logistic models by means of a non parametric goodness of fit test: a case study

A sequence of logistic models is fitted to data from a Dutch follow-up study on preterm infants (POPS). To examine the adequacy of the model, a recently developed non parametric method to check goodness of fit is applied (le Cessie and Van Houwelingen (1991)). This method uses a test statistic based upon kernel regression methods.
In this paper the problem of choosing a "best" bandwidth, corresponding to the greatest power of the test statistic, is avoided by computing the test statistic for a range of different bandwidths. Testing is then based upon the asymptotic distribution of the maximum of the test statistics.
The testing method is used as a goodness of fit criterion, and the contribution of each individual observation to the test statistic is used as a diagnostic tool to localize deviations of the model, and to determine directions in which the model can be improved.     

Generic substitution in the UK pharmaceutical industry: A markovian analysis

This paper examines the impact of competition on product market shares in the UK pharmaceutical industry. The topic is of particular interest since, although it is well known that such products are already subject to competition by innovation and by price (Cooper, 1966, Ch. 3; Reekie, 1978), the possible introduction in the UK of generic substitution (Greenfield, 1982, p. 24), could further increase competitive pressures. Our aim is to apply Markov probabilistic analysis to existing market data. This will enable us (after Adelman, 1958) to analyse the structure which the industry will ‘eventually reach if certain current trends were to continue’ (Adelman, p. 893). In particular, we apply the technique to sub-samples of the data, to infer how the competitive process influences the industry's configuration when generic competition is present. The outcome can be used as a conservative indicator of the impact on the industry if generic substitution was to be adopted in the National Health Service (NHS). The plan of the paper is as follows. In the first section a brief resumt of the debate which has surrounded generic substitution proposals is presented. The second section details the data and sample used in our empirical work. The third section applies Markov chain analysis to that data and reports our results. The final section summarizes our conclusions and discusses some of the problems and limitations of our results.     

A refined parametric model for short term load forecasting

We present a refined parametric model for forecasting electricity demand which performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating the electricity demand as a function of the temperature and day of the data. We then set out a series of refinements of the model, explaining the rationale for each, and using the competition scores to demonstrate that each successive refinement step increases the accuracy of the model’s predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data, and special treatments of public holidays.     

A parametric simplex-like algorithm for a fractional programming problem

A few theoretical results are given for the fractional programming problem where the objective function is the product of an affine function and a linear fractional function and where the feasible region is a polytope. Moreover, a simplex-like algorithm is proposed to solve the same problem when the feasible region is bounded. The algorithm is based on the exploration of a set of suitably defined optimal level solutions.

---

Riassunto Nel lavoro vengono esposti alcuni risultati teorici per un problema di programmazione frazionaria nel quale la funzione oggetto è data dal prodotto tra una funzione affine e una funzione lineare definita su una regione ammissibile poliedrica. Viene inoltre formulato un algoritmo di tipo simplesso che consente la risoluzione del problema qualora la regione ammissibile sia limitata. Tale algoritmo è basato sulla esplorazione di un insieme opportunamente definito di soluzioni ottime di livello.

---

---

This research was partially supported by MURST.     

A fast resample method for parametric and semiparametric models

We propose a fast resample method for two step nonlinear parametric and semiparametric models, which does not require recomputation of the second stage estimator during each resample iteration. The fast resample method directly exploits the score function representations computed on each bootstrap sample, thereby reducing computational time considerably. This method is used to approximate the limit distribution of parametric and semiparametric estimators, possibly simulation based, that admit an asymptotic linear representation. Monte Carlo experiments demonstrate the desirable performance and vast improvement in the numerical speed of the fast bootstrap method.     

A matrix exponential spatial specification

We introduce the matrix exponential as a way of modelling spatially dependent data. The matrix exponential spatial specification (MESS) simplifies the log-likelihood allowing a closed form solution to the problem of maximum-likelihood estimation, and greatly simplifies the Bayesian estimation of the model. The MESS can produce estimates and inferences similar to those from conventional spatial autoregressive models, but has analytical, computational, and interpretive advantages. We present maximum likelihood and Bayesian approaches to the estimation of this spatial model specification along with methods of model comparisons over different explanatory variables and spatial specifications.     

The role of the elasticity of substitution in economic growth: A cross-country investigation

Although the importance of the elasticity of substitution between capital and labor (σ) has long been recognized in several branches of economics, it has not received enough attention in the growth literature. de La Grandville (1989) showed theoretically that at any stage of an economy's development, the growth rate of income per capita is increasing with σ. The higher is σ, the greater the similarity between capital and labor in the production function, and thus diminishing returns set in very slowly. To the best of our knowledge, this is the first paper that tests the hypothesis that growth rate is increasing with the value of σ at the cross-country level. We estimate σ for 90 countries from direct estimation of the normalized CES production function and then include these estimators as an explanatory variable in cross-country growth regression. We investigate the sign and significance of the coefficient of σ conditioning on country characteristics, initial conditions, and a set of policy variables. After accounting for endogeneity and the fact that σ is a “generated” regressor, we find strong support for the hypothesis. The result is robust to both Leamer's (1983) extreme value analysis and Bayesian model averaging. About a fifth to a quarter of the growth rate differential between East Asia and Sub-Saharan Africa can be explained by σ alone.     

Heterogeneity dependence between oil prices and exchange rate: Evidence from a parametric test of Granger causality in quantiles

This paper re-examines the nexus between crude oil price and exchange rate by investigating their heterogeneity dependence structure within the framework of Granger causality in quantiles for a sample of developed and emerging economies (namely UK, Canada, Brazil, Russia, Mexico, Norway, India, Japan, South Africa, South Korea and European Union (EU)). The results indicate no distinct causality between the crude oil price changes and the real exchange rate returns for all countries besides Russia at the median of the conditional distribution. Besides, the crude oil price changes influence the exchange rate returns in all countries, except Norway and EU, particularly around the tails of the conditional distributions of exchange rate returns. This suggests that the oil price changes influence the real exchange rate returns when the real exchange rate returns are either in extreme appreciation or depreciation. Moreover, the crude oil price movement can be explained by the exchange rate returns for most oil importers only when the crude oil market is bearish or bullish. By contrast, the real exchange rate can permanently affect the crude oil price for most oil-importing countries irrespective of the crude oil market's state. Finally, our findings provide an essential reference for managing the extreme risk dependence between the exchange rate market and the crude oil market.     

Steady-state growth and the elasticity of substitution

In a neoclassical economy with endogenous capital- and labor-augmenting technical change the steady-state growth rate of output per worker is shown to increase in the elasticity of substitution between capital and labor. This confirms the assessment of Klump and de La Grandville (2000) that a greater elasticity of substitution allows for faster of economic growth. However, unlike their findings my result applies to the steady-state growth rate. Moreover, it does not hinge on particular assumptions on how aggregate savings come about. It holds for any household sector allowing savings to grow at the same rate as aggregate output.