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arXiv2018

Inference on Local Average Treatment Effects for Misclassified Treatment

We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is non-classical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.

econ.EM↗

arXiv2018

Moment Inequalities in the Context of Simulated and Predicted Variables

This paper explores the effects of simulated moments on the performance of inference methods based on moment inequalities. Commonly used confidence sets for parameters are level sets of criterion functions whose boundary points may depend on sample moments in an irregular manner. Due to this feature, simulation errors can affect the performance of inference in non-standard ways. In particular, a (first-order) bias due to the simulation errors may remain in the estimated boundary of the confidence set. We demonstrate, through Monte Carlo experiments, that simulation errors can significantly reduce the coverage probabilities of confidence sets in small samples. The size distortion is particularly severe when the number of inequality restrictions is large. These results highlight the danger of ignoring the sampling variations due to the simulation errors in moment inequality models. Similar issues arise when using predicted variables in moment inequalities models. We propose a method for properly correcting for these variations based on regularizing the intersection of moments in parameter space, and we show that our proposed method performs well theoretically and in practice.

econ.EM↗

arXiv2019

Large Sample Properties of Partitioning-Based Series Estimators

We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, econometrics, and machine learning. First, we obtain a general characterization of their leading asymptotic bias. Second, we establish integrated mean squared error approximations for the point estimator and propose feasible tuning parameter selection. Third, we develop pointwise inference methods based on undersmoothing and robust bias correction. Fourth, employing different coupling approaches, we develop uniform distributional approximations for the undersmoothed and robust bias-corrected t-statistic processes and construct valid confidence bands. In the univariate case, our uniform distributional approximations require seemingly minimal rate restrictions and improve on approximation rates known in the literature. Finally, we apply our general results to three partitioning-based estimators: splines, wavelets, and piecewise polynomials. The supplemental appendix includes several other general and example-specific technical and methodological results. A companion R package is provided.

math.ST↗

arXiv2018

Shapley Value Methods for Attribution Modeling in Online Advertising

This paper re-examines the Shapley value methods for attribution analysis in the area of online advertising. As a credit allocation solution in cooperative game theory, Shapley value method directly quantifies the contribution of online advertising inputs to the advertising key performance indicator (KPI) across multiple channels. We simplify its calculation by developing an alternative mathematical formulation. The new formula significantly improves the computational efficiency and therefore extends the scope of applicability. Based on the simplified formula, we further develop the ordered Shapley value method. The proposed method is able to take into account the order of channels visited by users. We claim that it provides a more comprehensive insight by evaluating the attribution of channels at different stages of user conversion journeys. The proposed approaches are illustrated using a real-world online advertising campaign dataset.

econ.EM↗

arXiv2020

Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects

To estimate the dynamic effects of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effects heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.

econ.EM↗

arXiv2019

Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious 'present' times $t_2$ before the crashes, we employ a clustering method to group the predicted critical times $t_c$ of the LPPLS fits over different time scales, where $t_c$ is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

econ.EM↗

arXiv2018

Estimating Treatment Effects in Mover Designs

Researchers increasingly leverage movement across multiple treatments to estimate causal effects. While these "mover regressions" are often motivated by a linear constant-effects model, it is not clear what they capture under weaker quasi-experimental assumptions. I show that binary treatment mover regressions recover a convex average of four difference-in-difference comparisons and are thus causally interpretable under a standard parallel trends assumption. Estimates from multiple-treatment models, however, need not be causal without stronger restrictions on the heterogeneity of treatment effects and time-varying shocks. I propose a class of two-step estimators to isolate and combine the large set of difference-in-difference quasi-experiments generated by a mover design, identifying mover average treatment effects under conditional-on-covariate parallel trends and effect homogeneity restrictions. I characterize the efficient estimators in this class and derive specification tests based on the model's overidentifying restrictions. Future drafts will apply the theory to the Finkelstein et al. (2016) movers design, analyzing the causal effects of geography on healthcare utilization.

econ.EM↗

arXiv2018

Transaction Costs in Collective Waste Recovery Systems in the EU

The study aims to identify the institutional flaws of the current EU waste management model by analysing the economic model of extended producer responsibility and collective waste management systems and to create a model for measuring the transaction costs borne by waste recovery organizations. The model was approbated by analysing the Bulgarian collective waste management systems that have been complying with the EU legislation for the last 10 years. The analysis focuses on waste oils because of their economic importance and the limited number of studies and analyses in this field as the predominant body of research to date has mainly addressed packaging waste, mixed household waste or discarded electrical and electronic equipment. The study aims to support the process of establishing a circular economy in the EU, which was initiated in 2015.

econ.EM↗

arXiv2020

Empirical Equilibrium

We study the foundations of empirical equilibrium, a refinement of Nash equilibrium that is based on a non-parametric characterization of empirical distributions of behavior in games (Velez and Brown,2020b arXiv:1907.12408). The refinement can be alternatively defined as those Nash equilibria that do not refute the regular QRE theory of Goeree, Holt, and Palfrey (2005). By contrast, some empirical equilibria may refute monotone additive randomly disturbed payoff models. As a by product, we show that empirical equilibrium does not coincide with refinements based on approximation by monotone additive randomly disturbed payoff models, and further our understanding of the empirical content of these models.

econ.EM↗

arXiv2018

Price Competition with Geometric Brownian motion in Exchange Rate Uncertainty

We analyze an operational policy for a multinational manufacturer to hedge against exchange rate uncertainties and competition. We consider a single product and single period. Because of long-lead times, the capacity investment must done before the selling season begins when the exchange rate between the two countries is uncertain. we consider a duopoly competition in the foreign country. We model the exchange rate as a random variable. We investigate the impact of competition and exchange rate on optimal capacities and optimal prices. We show how competition can impact the decision of the home manufacturer to enter the foreign market.

econ.EM↗

arXiv2018

Econometric Modeling of Regional Electricity Spot Prices in the Australian Market

Wholesale electricity markets are increasingly integrated via high voltage interconnectors, and inter-regional trade in electricity is growing. To model this, we consider a spatial equilibrium model of price formation, where constraints on inter-regional flows result in three distinct equilibria in prices. We use this to motivate an econometric model for the distribution of observed electricity spot prices that captures many of their unique empirical characteristics. The econometric model features supply and inter-regional trade cost functions, which are estimated using Bayesian monotonic regression smoothing methodology. A copula multivariate time series model is employed to capture additional dependence -- both cross-sectional and serial-- in regional prices. The marginal distributions are nonparametric, with means given by the regression means. The model has the advantage of preserving the heavy right-hand tail in the predictive densities of price. We fit the model to half-hourly spot price data in the five interconnected regions of the Australian national electricity market. The fitted model is then used to measure how both supply and price shocks in one region are transmitted to the distribution of prices in all regions in subsequent periods. Finally, to validate our econometric model, we show that prices forecast using the proposed model compare favorably with those from some benchmark alternatives.

econ.EM↗

arXiv2018

Economic inequality and Islamic Charity: An exploratory agent-based modeling approach

Economic inequality is one of the pivotal issues for most of economic and social policy makers across the world to insure the sustainable economic growth and justice. In the mainstream school of economics, namely neoclassical theories, economic issues are dealt with in a mechanistic manner. Such a mainstream framework is majorly focused on investigating a socio-economic system based on an axiomatic scheme where reductionism approach plays a vital role. The major limitations of such theories include unbounded rationality of economic agents, reducing the economic aggregates to a set of predictable factors and lack of attention to adaptability and the evolutionary nature of economic agents. In tackling deficiencies of conventional economic models, in the past two decades, some new approaches have been recruited. One of those novel approaches is the Complex adaptive systems (CAS) framework which has shown a very promising performance in action. In contrast to mainstream school, under this framework, the economic phenomena are studied in an organic manner where the economic agents are supposed to be both boundedly rational and adaptive. According to it, the economic aggregates emerge out of the ways agents of a system decide and interact. As a powerful way of modeling CASs, Agent-based models (ABMs) has found a growing application among academicians and practitioners. ABMs show that how simple behavioral rules of agents and local interactions among them at micro-scale can generate surprisingly complex patterns at macro-scale. In this paper, ABMs have been used to show (1) how an economic inequality emerges in a system and to explain (2) how sadaqah as an Islamic charity rule can majorly help alleviating the inequality and how resource allocation strategies taken by charity entities can accelerate this alleviation.

econ.EM↗

arXiv2018

Deep Learning for Predicting Asset Returns

Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term-memory (LSTM) for time-series effects. State-of-the-art algorithms including stochastic gradient descent (SGD), TensorFlow and dropout design provide imple- mentation and efficient factor exploration. To illustrate our methodology, we revisit the equity market risk premium dataset of Welch and Goyal (2008). We find the existence of nonlinear factors which explain predictability of returns, in particular at the extremes of the characteristic space. Finally, we conclude with directions for future research.

stat.ML↗

arXiv2018

New HSIC-based tests for independence between two stationary multivariate time series

This paper proposes some novel one-sided omnibus tests for independence between two multivariate stationary time series. These new tests apply the Hilbert-Schmidt independence criterion (HSIC) to test the independence between the innovations of both time series. Under regular conditions, the limiting null distributions of our HSIC-based tests are established. Next, our HSIC-based tests are shown to be consistent. Moreover, a residual bootstrap method is used to obtain the critical values for our HSIC-based tests, and its validity is justified. Compared with the existing cross-correlation-based tests for linear dependence, our tests examine the general (including both linear and non-linear) dependence to give investigators more complete information on the causal relationship between two multivariate time series. The merits of our tests are illustrated by some simulation results and a real example.

stat.ME↗

arXiv2018

Interpreting Quantile Independence

How should one assess the credibility of assumptions weaker than statistical independence, like quantile independence? In the context of identifying causal effects of a treatment variable, we argue that such deviations should be chosen based on the form of selection on unobservables they allow. For quantile independence, we characterize this form of treatment selection. Specifically, we show that quantile independence is equivalent to a constraint on the average value of either a latent propensity score (for a binary treatment) or the cdf of treatment given the unobservables (for a continuous treatment). In both cases, this average value constraint requires a kind of non-monotonic treatment selection. Using these results, we show that several common treatment selection models are incompatible with quantile independence. We introduce a class of assumptions which weakens quantile independence by removing the average value constraint, and therefore allows for monotonic treatment selection. In a potential outcomes model with a binary treatment, we derive identified sets for the ATT and QTT under both classes of assumptions. In a numerical example we show that the average value constraint inherent in quantile independence has substantial identifying power. Our results suggest that researchers should carefully consider the credibility of this non-monotonicity property when using quantile independence to weaken full independence.

econ.EM↗

arXiv2018

Identifying Effects of Multivalued Treatments

Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold-crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.

econ.EM↗

arXiv2018

Aide et Croissance dans les pays de l'Union Economique et Mon{é}taire Ouest Africaine (UEMOA) : retour sur une relation controvers{é}e

The main purpose of this paper is to analyze threshold effects of official development assistance (ODA) on economic growth in WAEMU zone countries. To achieve this, the study is based on OECD and WDI data covering the period 1980-2015 and used Hansen's Panel Threshold Regression (PTR) model to "bootstrap" aid threshold above which its effectiveness is effective. The evidence strongly supports the view that the relationship between aid and economic growth is non-linear with a unique threshold which is 12.74% GDP. Above this value, the marginal effect of aid is 0.69 points, "all things being equal to otherwise". One of the main contribution of this paper is to show that WAEMU countries need investments that could be covered by the foreign aid. This later one should be considered just as a complementary resource. Thus, WEAMU countries should continue to strengthen their efforts in internal resource mobilization in order to fulfil this need.

econ.EM↗

arXiv2018

Endogenous growth - A dynamic technology augmentation of the Solow model

In this paper, I endeavour to construct a new model, by extending the classic exogenous economic growth model by including a measurement which tries to explain and quantify the size of technological innovation ( A ) endogenously. I do not agree technology is a "constant" exogenous variable, because it is humans who create all technological innovations, and it depends on how much human and physical capital is allocated for its research. I inspect several possible approaches to do this, and then I test my model both against sample and real world evidence data. I call this method "dynamic" because it tries to model the details in resource allocations between research, labor and capital, by affecting each other interactively. In the end, I point out which is the new residual and the parts of the economic growth model which can be further improved.

econ.EM↗