Platform Competition and Broadband Uptake: Theory and Empirical Evidence from the European Union

Broadband access provides users with high speed, always-on connectivity to the Internet. Due to its superiority, broadband is seen as the way for consumers and firms to exploit the great potentials of new applications. This has generated a policy debate on how to stimulate adoption of broadband technology. One of the most disputed issues is about competition policies: these may be intended to promote competition in the Digital Subscriber Line (DSL) segment of the market (intra- platform competition), or to stimulate entry into the market for alternative platforms such as cable access or fiber optics (inter- platform competition). Using a model of oligopoly competition between differentiated products, our paper explicitly studies the effect of inter and intra platform competition on the diffusion of broadband access. The implications of the model are then tested using data from 14 European countries. The econometric evidence confirms the results of the theoretical model and indicates that while inter-platform competition drives broadband adoption, competition in the market for DSL services does not play a significant role. The results also confirm that lower unbundling prices stimulate broadband uptake.Broadband, inter-platform and intra-platform competition,local loop unbundling

Model-Free Estimation of Large Variance Matrices

This paper introduces a new method for estimating large variance matrices. Starting from the orthogonal decomposition of the sample variance matrix, we exploit the fact that orthogonal matrices are never ill-conditioned and therefore focus on improving the estimation of the eigenvalues. We estimate the eigenvectors from just a fraction of the data, then use them to transform the data into approximately orthogonal series that we use to estimate a well-conditioned matrix of eigenvalues. Our estimator is model-free: we make no assumptions on the distribution of the random sample or on any parametric structure the variance matrix may have. By design, it delivers well-conditioned estimates regardless of the dimension of problem and the number of observations available. Simulation evidence show that the new estimator outperforms the usual sample variance matrix, not only by achieving a substantial improvement in the condition number (as expected), but also by much lower error norms that measure its deviation from the true variance.variance matrices, ill-conditioning, mean squared error, mean absolute deviations, resampling

An I(d) Model with Trend and Cycles

This paper deals with models allowing for trending processes and cyclical component with error processes that are possibly nonstationary, nonlinear, and non-Gaussian. Asymptotic confidence intervals for the trend, cyclical component, and memory parameters are obtained. The confidence intervals are applicable for a wide class of processes, exhibit good coverage accuracy, and are easy to implement.fractional integration, trend, cycle, nonlinear process, Whittle objective function

Platform Competition and Broadband Uptake: Theory and Empirical Evidence from the European Union

Broadband access provides users with high speed, always-on connectivity to the Internet. Due to its superiority, broadband is seen as the way for consumers and firms to exploit the great potentials of new applications. This has generated a policy debate on how to stimulate adoption of broadband technology. One of the most disputed issues is about competition policies: these may be intended to promote competition in the Digital Subscriber Line (DSL) segment of the market (intra- platform competition), or to stimulate entry into the market for alternative platforms such as cable access or fiber optics (inter- platform competition). Using a model of oligopoly competition between differentiated products, our paper explicitly studies the effect of inter and intra platform competition on the diffusion of broadband access. The implications of the model are then tested using data from 14 European countries. The econometric evidence confirms the results of the theoretical model and indicates that while inter-platform competition drives broadband adoption, competition in the market for DSL services does not play a significant role. The results also confirm that lower unbundling prices stimulate broadband uptake.Broadband, inter-platform and intra-platform competition, local loop unbundling

Estimating and testing stochastic volatility models using realized measures

This paper proposes a procedure to test for the correct specification of the functional form of the volatility process, within the class of eigenfunction stochastic volatility models (Meddahi, 2001). The procedure is based on the comparison of the moments of realized volatility measures with the corresponding ones of integrated volatility implied by the model under the null hypothesis. We first provide primitive conditions on the measurement error associated with the realized measure, which allow to construct asymptotically valid specification tests. Then we establish regularity conditions under which realized volatility, bipower variation (Barndorff-Nielsen & Shephard, 2004d), and modified subsampled realized volatility (Zhang, Mykland & Aït Sahalia, 2003), satisfy the given primitive assumptions. Finally, we provide an empirical illustration based on three stock from the Dow Jones Industrial Average

Testing for one-factor models versus stochastic volatility models

This paper proposes a testing procedure in order to distinguish between the case where the volatility of an asset price is a deterministic function of the price itself and the one where it is a function of one or more (possibly unobservable) factors, driven by not perfectly correlated Brownian motions. Broadly speaking, the objective of the paper is to distinguish between a generic one-factor model and a generic stochastic volatility model. In fact, no specific assumption on the functional form of the drift and variance terms is required. The proposed tests are based on the difference between two different nonparametric estimators of the integrated volatility process. Building on some recent work by Bandi and Phillips (2003) and Barndorff-Nielsen and Shephard (2004a), it is shown that the test statistics converge to a mixed normal distribution under the null hypothesis of a one factor diffusion process, while diverge in the case of multifactor models. The findings from a Monte Carlo experiment indicate that the suggested testing procedure has good finite sample properties

Testing and modelling market microstructure effects with an application to the Dow Jones industrial average

It is a well accepted fact that stock returns data are often contaminated by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover, and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model free measures of volatility, such as realized volatility. In this paper we suggest two test statistics. The first is used to test for the null hypothesis of no microstructure noise. If the null is rejected, we proceed to perform a test for the hypothesis that the microstructure noise variance is independent of the sampling frequency at which data are recorded. We provide empirical evidence based on the stocks included in the Dow Jones Industrial Average, for the period 1997-2002. Our findings suggest that, while the presence of microstructure induces a severe bias when estimating volatility using high frequency data, such a bias grows less than linearly in the number of intraday observations

Predictive inference for integrated volatility

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on the unobservable volatility process, is provided. Asymptotic normality is also established. The efficacy of the estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided

Two estimators of the long-run variance: beyond short memory

This paper deals with the estimation of the long-run variance of a stationary sequence. We extend the usual Bartlett-kernel heteroskedasticity and autocorrelation consistent (HAC) estimator to deal with long memory and antipersistence. We then derive asymptotic expansions for this estimator and the memory and autocorrelation consistent (MAC) estimator introduced by Robinson [Robinson, P. M., 2005. Robust covariance matrix estimation: HAC estimates with long memory/antipersistence correction. Econometric Theory 21, 171–180]. We offer a theoretical explanation for the sensitivity of HAC to the bandwidth choice, a feature which has been observed in the special case of short memory. Using these analytical results, we determine the MSE-optimal bandwidth rates for each estimator. We analyze by simulations the finite-sample performance of HAC and MAC estimators, and the coverage probabilities for the studentized sample mean, giving practical recommendations for the choice of bandwidths