Bounds on policy relevant parameters with discrete policy variation

When estimating policy parameters, also known as treatment effects, the assignment to treatment mechanism almost always causes endogeneity and thus bias many of these policy parameters estimates. Additionally, heterogeneity in program impacts is more likely to be the norm than the exception for most social programs. In situations where these issues are present, the Marginal Treatment Effect (MTE) parameter estimation makes use of an instrument to avoid assignment bias and simultaneously to account for heterogeneous effects throughout individuals. Although this parameter is point identified in the literature, the assumptions required for identification may be strong. Given that, we use weaker assumptions in order to partially identify the MTE, i.e. to stablish a methodology for MTE bounds estimation, implementing it computationally and showing results from Monte Carlo simulations. The partial identification we perfom requires the MTE to be a monotone function over the propensity score, which is a reasonable assumption on several economics' examples, and the simulation results shows it is possible to get informative even in restricted cases where point identification is lost. Additionally, in situations where estimated bounds are not informative and the traditional point identification is lost, we suggest a more generic method to point estimate MTE using the Moore-Penrose Pseudo-Invese Matrix, achieving better results than traditional methods.

Forecasting electricity demand using generalized long memory

This paper studies the electricity hourly load demand in the area covered by a utility situated in the southeast of Brazil. We propose a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load. The model is proposed for sectional data, that is, each hour’s load is studied separately as a single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is compared with a SARIMA benchmark using the years of 1999 and 2000 as the out-of-sample. The model clearly outperforms the benchmark. We conclude for general long memory in the series.

Can unobserved heterogeneity in farmer ability explain the inverse relationship between farm size and productivity?

The well-known inverse relationship between farm-size and productivity is usually explained in terms of diminishing returns with respect to land and other inputs coupled with various types of market frictions that prevent the efficient allocation of land across farms. We show that even if in the absence of diminishing returns one can provide an alternative explanation for this phenomenon using endogenous occupational choice and heterogeneity with respect to farming skills.

The not so generalized effects of nonreciprocal trade agreements

This dissertation uses an empirical gravity equation approach to study the relationship between nonreciprocal trade agreements (NRTAs) and members’ trade flows. Estimations relate bilateral imports to trade policy variables using a very comprehensive dataset with over fifty years of data. Results show that meager average trade effects exist only if members are excluded from the world trading system or if they are very poor. As trade flows between NRTA members are already rising before their creation, results also suggest a strong endogeneity concerning their formation. Moreover, estimations show that uncertainty and discretion tend to critically hinder NRTA’s performance. On the other hand, reciprocal trade agreements show the opposite pattern regardless of members’ income status.Encouraging developing countries’ openness to trade through reciprocal liberalization emerges consequently as a possible policy implication.