Instrumental Variables Estimation of Stationaryand Nonstationary Cointegrating Regressions

Instrumental variables estimation is classically employed to avoid simultaneousequations bias in a stable environment. Here we use it to improve upon ordinaryleast squares estimation of cointegrating regressions between nonstationaryand/or long memory stationary variables where the integration orders of regressorand disturbance sum to less than 1, as happens always for stationary regressors,and sometimes for mean-reverting nonstationary ones. Unlike in the classicalsituation, instruments can be correlated with disturbances and/or uncorrelated withregressors. The approach can also be used in traditional non-fractionalcointegrating relations. Various choices of instrument are proposed. Finite sampleperformance is examined.Cointegration, Instrumental variables estimation, I(d) processes.

Distribution dynamics: a spatial perspective

It is quite common in cross-sectional convergence analyses that data exhibit spatial dependence. Within the literature adopting the distribution dynamics approach, authors typically opt for spatial prefiltering. We follow an alternative route and propose a procedure based on an estimate of the mean function of a conditional density for which we develop a two-stage non-parametric estimator that allows for spatial dependence estimated via a spline estimator of the spatial correlation function. The finite sample performance of this estimator is assessed via Monte Carlo simulations. We apply the procedure that incorporates the proposed spatial non-parametric estimator to data on per capita personal income in US states and metropolitan statistical areas

Spurious long memory, uncommon breaks and the implied–realized volatility puzzle

One of the puzzles in international finance is the frequent finding that implied volatility is a biased predictor of realized volatility. However, given asset price volatility is often characterized as possessing long memory, the recent literature has shown that allowing for long-range dependence removes this bias. Of course, the appearance of long memory can be generated by the presence of structural breaks. This paper discusses the effect of structural breaks on the implied-realized volatility relation. Simulations show that if significant structural breaks are omitted, testing can spuriously show the typical patterns of fractional cointegration found in the literature. Next, empirical results show that foreign exchange implied and realized volatility contains structural breaks. The breaks in the implied series never closely anticipate or co-occur with those of the realized series, suggesting that the market has no ability to forecast structural change. When breaks are accounted for in the bi-variate framework, the point estimate of the slope parameter falls and the null of unbiasedness can be rejected. Allowing for structural breaks suggests that the implied-realized volatility puzzle might not be solved after all

Distribution Dynamics in the US. A spatial perspective.

It is quite common in cross-sectional convergence analyses that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools for addressing the issue, research is only at a very initial stage within the distribution dynamics approach. In particular, in the continuous state-space framework, a few authors opted for spatial pre-filtering the data in order to guarantee the statistical properties of the estimates. In this paper, we follow an alternative route that starts from the idea that spatial dependence is not just noise but can be a substantive element of the data generating process. In particular, we develop a tool that, building on a mean-bias adjustment procedure established in the literature, explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for pre-filtering. Using this tool, we then reconsider the evidence on convergence across US states

The effect of immigration on convergence dynamics in the US

This paper analyzes the impact of immigration on the dynamics of the cross-sectional distribution of GSP per capita and per worker. To achieve this we combine different approaches: on the one hand, we establish via Instrumental Variable estimation the effect of the inflow of foreign- born workers on output per worker, employment and population; on the other hand, using the Distribution Dynamics approach, we reconstruct the consequences of migration flows on convergence dynamics across US states

Urban Governance Structure and Wage Disparities across US Metropolitan Areas

This paper analyses the determinants of spatial wage disparities in the US context for the period 1980-2000. Agglomeration benefits are estimated based on city productivity premia which are computed after controlling for the skills distribution among metropolitan areas as well as industry fixed effects. The drivers of productivity differentials that are taken into consideration are the size of the local economy, the spatial interactions among local autonomous economic systems and the structure of urban governance as well as the policy responses to the fragmentation issue. A metropolitan area with ten percentage more administrative units than another of the same size, experiences wages that are between 2.0% and 3.0% lower. The presence of a voluntary governance body is found to mitigate the problem of fragmentation only marginally, while the existence of special purpose districts have a negative impact on regional productivity. The implementation of a metropolitan government with a regional tax system is expected to increase productivity by around 6%

Instrumental variables estimation of stationary and nonstationary cointegrating regressions

Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least squares estimation of cointegrating regressions between nonstationary and/or long memory stationary variables where the integration orders of regressor and disturbance sum to less than 1, as happens always for stationary regressors, and sometimes for mean-reverting nonstationary ones. Unlike in the classical situation, instruments can be correlated with disturbances and/or uncorrelated with regressors. The approach can also be used in traditional non-fractional cointegrating relations. Various choices of instrument are proposed. Finite sample performance is examined

Frequency domain bootstrap for the fractional cointegration regression

In this paper a bootstrap approach in the frequency domain is proposed to compute the empirical distribution of the Narrow Band Least Squares Estimator [Robinson, P.M., 1994. Semiparametric analysis of long memory time series. The Annals of Statistics 22, 515–539.] of the fractional cointegration parameter. A Monte Carlo experiment illustrates the finite sample performance

A different perspective on clustering time series

In this paper we intend to shed further light on time series clustering. Firstly, we aim at clarifying, via Monte Carlo simulations, to which extent the choice of the measure of dissimilarity can affect the results of time series cluster analysis. Then we move to a different point of view and tackle the issue of classifying time series using the Self Organizing Maps (Kohonen, 2001), typically employed in pattern recognition for cross-sectional data