STRUCTURAL SHIFTS IN UK UNEMPLOYMENT 1979–2005: THE TWIN IMPACTS OF FINANCIAL DEREGULATION AND COMPUTERIZATION

This paper analyses UK unemployment in the period 1979–2005. Structural breaks are identified endogenously and they coincide with key institutional changes associated with financial deregulation and computerization in the New Economy. A vector error correction model is estimated and it confirms that computerization and financial deregulation have had counteracting impacts on UK unemployment. The results are consistent with three hypotheses: technological advances associated with computerization have moderated inflationary struggles between firms and insiders by increasing total factor productivity; financial deregulation has generated financial fragility fostering rises in unemployment; financial deregulation and computerization together have been associated with shifts from manufacturing towards services, fostering structural unemployment.     

Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution

We propose a computationally efficient and statistically principled method for kernel smoothing of point pattern data on a linear network. The point locations, and the network itself, are convolved with a two‐dimensional kernel and then combined into an intensity function on the network. This can be computed rapidly using the fast Fourier transform, even on large networks and for large bandwidths, and is robust against errors in network geometry. The estimator is consistent, and its statistical efficiency is only slightly suboptimal. We discuss bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation and adaptive smoothing. The methods are used to analyse spatially varying frequency of traffic accidents in Western Australia and the relative risk of different types of traffic accidents in Medellín, Colombia.     

Estimating the J function without edge correction

The interaction between points in a spatial point process can be measured by its empty space function F , its nearest-neighbour distance distribution function G , and by combinations such as the J function J = (1 G )/(1 F ). The estimation of these functions is hampered by edge effects: the uncorrected, empirical distributions of distances observed in a bounded sampling window W give severely biased estimates of F and G . However, in this paper we show that the corresponding uncorrected estimator of the function J = (1 G )/(1 F ) is approximately unbiased for the Poisson case, and is useful as a summary statistic. Specifically, consider the estimate _W of J computed from uncorrected estimates of F and G . The function J _W ( r ), estimated by _W , possesses similar properties to the J function, for example J _W ( r ) is identically 1 for Poisson processes. This enables direct interpretation of uncorrected estimates of J , something not possible with uncorrected estimates of either F , G or K . We propose a Monte Carlo test for complete spatial randomness based on testing whether J _W ( r ) 1. Computer simulations suggest this test is at least as powerful as tests based on edge corrected estimators of J .     

Non- and semi-parametric estimation of interaction in inhomogeneous point patterns

We develop methods for analysing the 'interaction' or dependence between points in a spatial point pattern, when the pattern is spatially inhomogeneous. Completely non-parametric study of interactions is possible using an analogue of the K -function. Alternatively one may assume a semi-parametric model in which a (parametrically specified) homogeneous Markov point process is subjected to (non-parametric) inhomogeneous independent thinning. The effectiveness of these approaches is tested on datasets representing the positions of trees in forests.     

A nonparametric measure of spatial interaction in point patterns

The strength and range of interpoint interactions in a spatial point process can be quantified by the function J = (1 - G)/(1 - F), where G is the nearest-neighbour distance distribution function and F the empty space function of the process. J(r) is identically equal to 1 for a Poisson process; values of J(r) smaller or larger than 1 indicate clustering or regularity, respectively. We show that, for a large class of point processes, J(r) is constant for distances r greater than the range of spatial interaction. Hence both the range and type of interaction can be inferred from J without parametric model assumptions. It is also possible to evaluate J(r) explicitly for many point process models, so that J is also useful for parameter estimation. Various properties are derived, including the fact that the J function of the superposition of independent point processes is a weighted mean of the J functions of the individual processes. Estimators of J can be constructed from standard estimators of F and G. We compute estimates of J for several standard point pattern datasets and implement a Monte Carlo test for complete spatial randomness.     

Behind the black box: a survey of real-world investment appraisal approaches

This paper assesses business investment appraisal techniques and expectations formation. The paper begins with a comparative analysis of the links between fixed asset investment theories and real world investment appraisal techniques, focusing on the underlying assumptions about rationality and expectations. In the empirical sections, these ideas are tested via an analysis of business behaviour based upon survey evidence from a sample of Cambridgeshire manufacturing firms. The statistical analysis focuses on hypothesis testing, ordered probit estimations and simulations. The evidence presented reveals that whilst conventional production function analysis does provide some explanatory power in describing the objective determinants of firm investment activity, subjective and behavioural factors are also important.The author is grateful to Geoff Harcourt, Philip Arestis and an anonymous referee for their encouraging advice and suggestions.     

THE RELATIONSHIP BETWEEN CAPITAL STOCK, UNEMPLOYMENT AND WAGES IN NINE EMU COUNTRIES

The focus of this paper is to investigate the importance of the capital stock in the determination of wages and unemployment in a range of EMU countries and to compare the results across countries. A time‐series analysis is conducted in the case of nine euro area countries, which were selected solely on the basis of data availability and consistency: Austria, Belgium, Finland, France, Germany, Italy, Ireland, the Netherlands and Spain. The paper begins with a short review of the literature on capital stock and unemployment, before it deals with the theoretical model. This is followed by estimation and testing of the theoretical model put forward, using both time‐series and panel data. The results are supportive of the main hypothesis of the paper: capital stock is an important determinant of unemployment and wages in the countries considered for the purposes of the paper.