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.

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.”

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.

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

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.

Most empirical and theoretical econometric studies of dynamic discrete choice models assume the discount factor to be known. We show the knowledge of the discount factor is not necessary to identify parts, or even all, of the payoff function. We show the discount factor can be generically identified jointly with the payoff parameters. On the other hand, it is known the payoff function cannot be nonparametrically identified without any a priori restrictions. Our identification of the discount factor is robust to any normalization choice on the payoff parameters. In IO applications, normalizations are usually made on switching costs, such as entry costs and scrap values. We also show that switching costs can be nonparametrically identified, in closed‐form, independently of the discount factor and other parts of the payoff function. Our identification strategies are constructive. They lead to easy to compute estimands that are global solutions. We illustrate with a Monte Carlo study and the dataset used in Ryan (2012).

Hong and Shum (2006) show equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observed prices alone. These quantiles can be difficult to estimate in practice. This paper uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least squares problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogeneous markets are available. We propose a two-step sieve estimator for that case. The first step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues' matches (as prices) and find evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely.

In this paper we introduce the ArCo package for R which consists of a set of functions to implement the the Artificial Counterfactual (ArCo) methodology to estimate causal effects of an intervention (treatment) on aggregated data and when a control group is not necessarily available. The ArCo method is a two-step procedure, where in the first stage a counterfactual is estimated from a large panel of time series from a pool of untreated peers. In the second-stage, the average treatment effect over the post-intervention sample is computed. Standard inferential procedures are available. The package is illustrated with both simulated and real datasets. 

This paper studies the effect of changes in economic conditions on crime. We exploit the 1990s trade liberalization in Brazil as a natural experiment generating exogenous shocks to local economies. We document that regions exposed to larger tariff reductions experienced a temporary increase in crime following liberalization. Next, we investigate through what channels the trade-induced economic shocks may have affected crime. We show that the shocks had significant effects on potential determinants of crime, such as labor market conditions, public goods provision, and income inequality. We propose a novel framework exploiting the distinct dynamic responses of these variables to obtain bounds on the effect of labor market conditions on crime. Our results indicate that this channel accounts for 75 to 93 percent of the effect of the trade-induced shocks on crime

This paper examines the extent to which government audits of public resources can reduce corruption by enhancing political and judiciary accountability. We do so in the context of Brazil’s anticorruption program, which randomly audits municipalities for their use of federal funds. We find that being audited in the past reduces future corruption by 8 percent, while also increasing the likelihood of experiencing a subsequent legal action by 20 percent. We interpret these reduced-form findings through a political agency model, which we structurally estimate. Our results suggest that the reduction in corruption comes mostly from the audits increasing the perceived nonelectoral costs of engaging in corruption.

We consider a new, flexible and easy-to-implement method to estimate thecausal effects of an intervention on a single treated unit when a control group is not available and which nests previous proposals in the literature. It is a two-step methodology where in the first stage, a counterfactual is estimated based on a large-dimensional set of variables from a pool of untreated units by means of shrinkage methods, such as the least absolute shrinkage and selection operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. We show that these results hold even when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are derived. By a simple transformation of the variables, it is possible to test for multivariate intervention effects on several moments of the variables of interest. Existing methods in the literature usually test for intervention effects on a single variable and assume that the time of the intervention is known. In addition, high-dimensionality is frequently ignored and inference is either conducted under a set of more stringent hypotheses and/or by permutation tests. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives. As an application, we evaluate the effects on inflation, GDP growth, retail sales and credit of an anti tax-evasion program.

We study the interaction between dispersed and sticky information by assuming that firms receive private noisy signals about the state in an otherwise standard model of price setting with sticky information. We compute the unique equilibrium of the game induced by the firms’ pricing decisions and derive the resulting Phillips curve. The main effect of dispersion is to magnify the immediate impact of a given shock when the degree of stickiness is small. Its effect on persistence is minor: even when information is largely dispersed, a substantial amount of informational stickiness is needed to generate persistence in aggregate prices and inflation.

We study the revenue maximization problem of a seller who is partially informed about the distribution of buyer's valuations, only knowing its first N moments. The seller chooses the mechanism generating the best revenue guarantee based on the information available, that is, the optimal mechanism is chosen according to maxmin expected revenue. We show that the transfer function in the optimal mechanism is given by non-negative monotonic hull of a polynomial of degree N. This enables us to transform the seller's problem into a much simpler optimization problem over Nvariables. The optimal mechanism is found by choosing the coefficients of the polynomial subject to a resource constraint. We show that knowledge of the first moment does not guarantee strictly positive revenue for the seller, characterize the solution for the cases of two moments and derive some characteristics of the solution for the general case.

An English auction is studied in which bidders can acquire information during the bidding process, allowing for heterogeneity both in ex-ante private information and the cost of information acquisition. The best response has a simple characterization where the optimal information acquisition time is unaffected by the other bidders’ strategies. We prove the existence of an equilibrium in a novel way by characterizing it as a fixed point in the space of bid distributions rather than the space of bid functions. Furthermore, we show that when bidders have homogeneous ex-ante private information about valuations: (1) The English auction generates more revenue than the Vickrey auction when the number of bidders is sufficiently large; and (2) the English auction is more efficient than the Vickrey auction when the information acquisition cost are relatively small. We present numerical simulations that show that these effects can be large. Our findings provide an additional explanation for the popularity of the English auction, even in settings where the bidders’ valuations are independent.

A three-sector model with a suitably chosen distribution of price stickiness can closely approximate the response to aggregate shocks of New Keynesian models with a much larger number of sectors, allowing for their estimation at much reduced computational cost.