Ownership Unbundling in Electricity Distribution: Empirical Evidence from New Zealand

New Zealand is the only country to date to have implemented forced ownership unbundling of electricity distribution from the rest of the electricity supply industry (in 1998). This paper examines the impact of this policy on electricity prices, quality of service and costs. We find that ownership unbundling did not achieve its objectives of facilitating greater competition in the electricity supply industry but that it did lead to lower costs and higher quality of service. We suggest that this experience indicates the potential benefits of ownership unbundling in Europe but also the danger of unintended consequences.     

Too Good to be True: Board Structural Independence as a Moderator of CEO Pay‐for‐Firm‐Performance

Whether voluntary or mandatory in nature, most recent corporate governance codes of best practice assume that board structural independence, and the application by boards of outcome‐based incentive plans, are important boundary conditions for the enforcement of Chief Executive Officer (CEO) pay‐for‐firm‐performance; that is, for optimal contracting between owners and executive agents. We test this logic on a large Australian sample using a system Generalized Method of Moments (GMM) approach to dynamic panel data estimation. We find that Australian boards exhibiting best practice structural arrangements – those chaired by non‐executives and dominated by non‐executive directors at the full board and compensation committee levels – are no more adept at enforcing CEO pay‐for‐firm‐performance than are executive‐dominated boards. These findings suggest that policy makers' faith in incentive plans and the moderating influence of structural independence per se may be misplaced. Our findings also hold significant implications for corporate governance theory. Specifically, the findings lend further support to a contingency‐based understanding of board composition, reward choice and monitoring; an approach integrating the insights afforded by behavioural approaches to Agency Theory and by social‐cognitive and institutional understandings of director outlook, decision‐making and behaviour.     

Behavioural Orientations of Non‐Profit Boards as a Factor in Entrepreneurial Performance: Does Governance Matter?

Relatively little is understood about factors triggering entrepreneurial behaviour within organizations not driven by profit motives. Governance plays an important role in non‐profits, particularly boards of directors. Integrating resource‐based theory and entrepreneurial orientation research, we examine the influence of non‐profit boards as strategic resources shaping the organization's entrepreneurial orientation and performance. In particular, we focus on the non‐profit board's underlying behavioural orientations, or the extent to which the board is strategic, activist, conservative, and cohesive. Findings from a cross‐sectional survey on arts and culture organizations demonstrate that three of these behavioural orientations impact levels of entrepreneurship occurring within non‐profits. Higher levels of entrepreneurship affect social performance, but not financial performance.     

How accurate are government forecasts of economic fundamentals? The case of Taiwan

A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate.In this paper, we develop a methodology for evaluating non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that the undocumented knowledge of the Taiwanese government reduces forecast errors substantially.     

Particle filters for continuous likelihood evaluation and maximisation

In this paper, a method is introduced for approximating the likelihood for the unknown parameters of a state space model. The approximation converges to the true likelihood as the simulation size goes to infinity. In addition, the approximating likelihood is continuous as a function of the unknown parameters under rather general conditions. The approach advocated is fast and robust, and it avoids many of the pitfalls associated with current techniques based upon importance sampling. We assess the performance of the method by considering a linear state space model, comparing the results with the Kalman filter, which delivers the true likelihood. We also apply the method to a non-Gaussian state space model, the stochastic volatility model, finding that the approach is efficient and effective. Applications to continuous time finance models and latent panel data models are considered. Two different multivariate approaches are proposed. The neoclassical growth model is considered as an application.     

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based realized volatilities indicates significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.     

Multivariate contemporaneous-threshold autoregressive models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex-ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific noise covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.     

Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

Parental job loss and the education enrollment of youth

Parental job loss from layoffs and business failures that occur when youth complete high school are found to be negatively related with enrollment at university and community college. Estimates using longitudinal data on Canadian youth and their parents are employed to identify both immediate and lagged effects of parental job loss on education enrollment. Parental job losses are also followed by significant falls in parental income. If the main pathway by which parental job loss affects youth education outcomes is via these income reductions, it that implies financial constraints on post-secondary education enrollment are important.