Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial/synthetic counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this paper, we investigate the consequences of applying such methodologies when the data comprise integrated processes of order 1, I(1), or are trend-stationary. We find that for I(1) processes without a cointegrating relationship (spurious case) the estimator of the effects of the intervention diverges, regardless of its existence. Although spurious regression is a well-known concept in time-series econometrics, they have been ignored in most of the literature on counterfactual estimation based on artificial/synthetic controls. For the case when at least one cointegration relationship exists, we have consistent estimators for the intervention effect albeit with a non-standard distribution. Finally, we discuss a test based on re-sampling which can be applied when there is at least one cointegration relationship or when the data is trend-stationary.

While the smooth transition (ST) model has become popular in business and economics, the treatment of unobserved heterogeneity within these models has received limited attention. We propose a ST finite mixture (STFM) model which simultaneously estimates the presence of time-varying effects and unobserved heterogeneity in a panel data context. Our objective is to accurately recover the heterogeneous effects of our independent variables of interest while simultaneously allowing these effects to vary over time. Accomplishing this objective may provide valuable insights for managers and policy makers. The STFM model nests several well-known ST and threshold models. We develop the specification, estimation, and model selection criteria for the STFM model using Bayesian methods. We also provide a theoretical assessment of the flexibility of the STFM model when the number of regimes grows with the sample size. In an extensive simulation study, we show that ignoring unobserved heterogeneity can lead to distorted parameter estimates, and that the STFM model is fairly robust when underlying model assumptions are violated. Empirically, we estimate the effects of in-game promotions on game attendance in Major League Baseball. Empirical results show that the STFM model outperforms all its nested versions. Supplementary materials for this article are available online.

In 2008, the Brazilian government made the concession of rural credit in the Amazon conditional upon stricter requirements as an attempt to curb forest clearings. This article studies the impact of this innovative policy on deforestation. Difference-in-differences estimations based on a panel of municipalities show that the policy change led to a substantial reduction in deforestation, mostly in municipalities where cattle ranching is the leading economic activity. The results suggest that the mechanism underlying these effects was a restriction in access to rural credit, one of the main support mechanisms for agricultural production in Brazil.

We provide evidence on three mechanisms that can reconcile frequent individual price changes with sluggish aggregate price dynamics. To that end, we estimate a semi-structural model that can extract information about real rigidities and the distribution of price stickiness from aggregate data. Hence, the model can also speak to the debate about the aggregate implications of sales. Our estimates indicate large real rigidities and substantial heterogeneity in price stickiness. Moreover, the cross-sectional distribution of price stickiness implied by aggregate data is in line with an empirical distribution obtained from micro price data that factors out sales and product substitutions.

We develop a multisector sticky-price DSGE model that can endogenously deliver differential responses of prices to aggregate and sectoral shocks. Input-output production linkages and a (standard) monetary policy rule contribute to a slow response of prices to aggregate shocks. In turn, labor market segmentation at the sectoral level induces withinsector strategic substitutability in price-setting decisions, which helps the model deliver a fast response of prices to sector-specific shocks. We estimate the model using aggregate and sectoral price and quantity data for the U.S., and find that it accounts well for a range of sectoral price facts.

Since the mid 1990s, theories of speculative attacks have argued that fixed exchange rate regimes induce excessive borrowing in foreign currency as an optimal response to implicit guarantees that the government will not devalue the domestic currency. Using data on Brazilian firms before and after the end of the fixed exchange rate regime in 1999, we estimate the relevance of the government guarantees by comparing the changes in foreign debt of two groups of firms: those that hedged their foreign currency debt prior to the exchange rate float and those that did not. Using the difference-in-differences approach, in which firm-specific characteristics are introduced as control variables, we exclude the macroeconomic effects of the change in the exchange rate regime and the possible differences in foreign debt trends of the two groups of firms, thus obtaining an estimate of the impact of the government guarantees on borrowing in foreign currency. The results suggest that the guarantees do not induce excessive borrowing in foreign currency.

Despite the fall in the interest rate observed in Brazil in recent decades, and specific regulations on the private pension segment that encourage long-term risk taking, institutions in this segment appear to be considerably sensitive to short-term factors, while avoiding exposure to long-term risk factors. With portfolio allocation data from large entities, we implemented a VAR model to evaluate the impact of interest rate changes on portfolio management decisions and performed a counterfactual analysis to define the causal effect of regulation on additional risk taking. Results indicate that interest rate increases lead to significant and persistent reduction of investment in riskier assets with longer maturities, while the implemented regulation was not able to force greater risk-taking by institutions, in addition to generating distortions in segments of the Brazilian financial market

This paper introduces a version of the interdependent value model of Milgrom and Weber (1982), where the signals are given by an index gathering signal shifters observed by the econometrician and private ones specific to each bidders. The model primitives are shown to be nonparametrically identified from first-price auction bids under a testable mild rank condition. Identification holds for all possible signal values. This allows to consider a wide range of counterfactuals where this is important, as expected revenue in second-price auction. An estimation procedure is briefly discussed.

Using a choice experiment in the lab, we assess the relative importance of different attitudes to income inequality. We elicit subjects’ preferences regarding pairs of payoff distributions within small groups, in a firm-like setting. We find that distributions that satisfy the Pareto-dominance criterion attract unanimous suffrage: all subjects prefer larger inequality provided it makes everyone weakly better off. This is true no matter whether payoffs are based on merit or luck. Unanimity only breaks once subjects’ positions within the income distribution are fixed and known ex-ante. Even then, 75% of subjects prefer Pareto-dominant distributions, but 25% of subjects engage in money burning at the top in order to reduce inequality, even when it does not make anyone better off. A majority of subjects embrace a more equal distribution if their own income or overall efficiency is not at stake. When their own income is at stake and the sum of payoffs remains unaffected, 20% of subjects are willing to pay for a lower degree of inequality.

Inflation forecasting is an important but difficult task. Here, we explore advances in machine learning (ML) methods and the availability of new datasets to forecast U.S. inflation. Despite the skepticism in the previous literature, we show that ML models with a large number of covariates are systematically more accurate than the benchmarks. The ML method that deserves more attention is the random forest model, which dominates all other models. Its good performance is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation. Supplementary materials for this article are available online.

We study how the diffusion of flex (bi-fuel) cars affected competition on ethanol and gasoline retail markets. We propose a model of price competition in which the two fuels become closer substitutes as flex cars penetration grows. We use a large panel of weekly prices at the station level to show that fuel prices and margins have fallen in response to this change. This finding is evidence of market power in fuel retail and indicates that innovations that increase consumer choice benefit even those who choose not to adopt them.

This paper studies the relationship between geographic heterogeneity and technology adoption in the context of the direct planting system (DPS) in Brazil. The DPS is a no-till farming technique that increases productivity and decreases soil degradation. However, it requires adaptation to local conditions to be profitably used. Combining detailed geographic and agricultural data, we show that geographic heterogeneity reduces DPS adoption. This effect is robust to the inclusion of controls and not observed for technologies that do not require local adaptation. These findings are consistent with models in which geographic heterogeneity increases the cost of adapting technologies to local conditions

Using a rich Brazilian panel dataset and an occupation-task mapping, we investigate whether returns to experience depend on the types of jobs performed by workers. We find that returns to experience in non-routine tasks, especially returns to analytical tasks, are much larger than returns to routine tasks. This gap increases with schooling, suggesting that schooling and nonroutine tasks are complementary in the human capital production function. These are important findings for developing countries similar to Brazil, where approximately 70% of workers’ tasks are routine

This paper considers an optimal renegotiation-proof dynamic Bayesian mechanism in which two privately informed players repeatedly have to take a joint action without resorting to side-payments. We provide a general framework which accommodates as special cases committee decision and collective insurance problems. Thus, we formally connect these separate strands of literature. We show: (i) first-best values can be arbitrarily approximated (but not achieved) when the players are sufficiently patient; (ii) our main result, the provision of intertemporal incentives necessarily leads to a dictatorial mechanism: in the long run the optimal scheme converges to the adoption of one player's favorite action. This can entail one agent becoming a permanent dictator or a possibility of having sporadic “regime shifts.”

The objective of this paper is to evaluate the Brazilian Apprenticeship program adopted at a large scale since 2000. In particular, we investigate whether the program is a better stepping stone to permanent jobs when compared to other forms of temporary jobs. Similar to other apprenticeship initiatives around the world, the Brazilian program trains young workers under special temporary contracts aiming to help them successfully complete the transition from school to work. We make use of a matched employee-employer dataset covering all formal employees in Brazil, including apprentices. Our identification strategy exploits a discontinuity in the eligibility to enter the program in the early 2000s, when 17 was the age limit to take part in the program. This strategy allows us to consider selection based on unobservable characteristics. We find that the program increases the probability of employment in permanent jobs and decreases turnover rates and formal labor market experience in 2-3- and 4-5-year horizons. These results are consistent with a positive effect of the program on reservation utilities of workers and on their efforts to expand skills. This is also confirmed by the data as we find substantial impacts on schooling attainment. We also find evidence that the skill requirements of the apprentices’ occupation affect the likelihood of obtaining an open-ended job in the short run and the education achievement in the medium run. The results also evince much larger effects of the program for workers who had their first job in large firms