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This paper deals with the estimation of continuous time diffusion processes describing the dynamics of electricity spot prices. Different parametric models have been proposed in the literature, each attempting to capture empirical characteristics and stylized facts of the electricity market like the spiky behavior of the spot prices. Although jump-diffusion and regime-switching models perform reasonably well, there is always a trade-off between model parsimony and adequacy. The results in the literature indicate that none of the models seem to consistently outperform its counterparts. This paper avoids making parametric assumption about the drift and the diffusion coefficient functions of the underlying electricity spot prices, and estimates these functions together with the market price of risk in a nonparametric way. The latter allows us to price futures contracts written on electricity spots. Using electricity spot prices and futures data from the regional electricity markets in Australia, we show that besides offering a convenient way of estimating the continuous-time models for electricity spot prices, our nonparametric estimation procedure performs well in- and out-of-sample when dealing with pricing of future contracts.

We formally define a concept of functional cointegration linking the dynamics of two time series via a functional coefficient. This is achieved through the use of a concept of summability as an alternative to I(1)’ness which is no longer suitable under nonlinear dynamics. We subsequently introduce a nonparametric approach for estimating the unknown functional coefficients. Our method is based on a piecewise local least squares principle and is computationally simple to implement. We establish its consistency properties and evaluate its performance in finite samples. We subsequently illustrate its usefulness through an application that explores linkages between stock prices and dividends via a sentiment indicator.

We study long run carbon dioxide emissions-economic development relationships for advanced countries grouped in policy relevant groups: North America and Oceania, South Europe, North Europe. By relying on recent advances on Generalized Additive Mixed Models (GAMMs) and adopting interaction models, we handle simultaneously three main econometric issues, named here as functional form bias, heterogeneity bias and omitted time related factors bias , which have been proved to be relevant but have been addressed separately in previous papers. The model incorporates nonlinear effects, eventually heterogeneous across countries, for both income and time. We also handle serial correlation by using autoregressive moving average (ARMA) processes. We find that country-specific time related factors weight more than income in driving the northern EU Environmental Kuznets. Overall, the countries differ more on their carbon-time relation than on the carbon-income relation which is in almost all cases monotonic positive. Once serial correlation and (heterogeneous) time effects have been accounted for, only three Scandinavian countries – Denmark, Finland and Sweden – present some threshold effect on the CO 2 -development relation.

This article analyses the stability properties of the steady-state and the transitional dynamics of an endogenous growth model with human capital, increasing-varieties R&D, and quality-ladders R&D [Strulik, H. 2005. “The Role of Human Capital and Population Growth in R&D-Based Models of Economic Growth.” Review of International Economics 13 (1): 129–145.]. We show that when spillovers within R&D sectors are higher than spillovers across the two R&D sectors, the equilibrium is unstable. However, when spillovers between sectors are higher than within, the equilibrium is a saddle-path. This result emphasizes the need for empirical research that compares quantitatively the importance of these two types of spillovers and highlights the importance of studying intersectoral effects between the two R&D sectors. We describe plausible paths of economic development after changes in crucial parameters and uncover some transitional effects that were impossible to detect in the steady-state analysis. We also show that this model’s transition dynamics can mimic the main features of the process of productivity slowdown that began in the 1970s.

This paper proposes a probabilistic model based on comovements and nonlinearities useful to assess the type of shock affecting each phase of the business cycle. By providing simultaneous inferences on the phases of real activity and inflation cycles, contractionary episodes are dated and categorized into demand, supply and mix recessions. The impact of shocks originated in the housing market over the business cycle is also assessed, finding that recessions are usually accompanied by housing deflationary pressures, while expansions are mainly influenced by housing demand shocks, with the only exception occurred during the period surrounding the “Great Recession,” affected by expansionary housing supply shocks.