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At least 217 records · Page 12Linked to original sources
arXiv2018

A General Method for Demand Inversion

This paper describes a numerical method to solve for mean product qualities which equates the real market share to the market share predicted by a discrete choice model. The method covers a general class of discrete choice model, including the pure characteristics model in Berry and Pakes(2007) and the random coefficient logit model in Berry et al.(1995) (hereafter BLP). The method transforms the original market share inversion problem to an unconstrained convex minimization problem, so that any convex programming algorithm can be used to solve the inversion. Moreover, such results also imply that the computational complexity of inverting a demand model should be no more than that of a convex programming problem. In simulation examples, I show the method outperforms the contraction mapping algorithm in BLP. I also find the method remains robust in pure characteristics models with near-zero market shares.

econ.EM↗
arXiv2018

Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors

In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second- or higher-order properties. Under this framework, the limiting null distributions of the conventional unit root test statistics are derived and shown to contain a number of unknown parameters. To circumvent the difficulty of direct consistent estimation, we propose to use the dependent wild bootstrap to approximate the non-pivotal limiting null distributions and provide a rigorous theoretical justification for bootstrap consistency. The proposed method is compared through finite sample simulations with the recolored wild bootstrap procedure, which was developed for errors that follow a heteroscedastic linear process. Further, a combination of autoregressive sieve recoloring with the dependent wild bootstrap is shown to perform well. The validity of the dependent wild bootstrap in a nonstationary setting is demonstrated for the first time, showing the possibility of extensions to other inference problems associated with locally stationary processes.

econ.EM↗
arXiv2018

The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.

econ.EM↗
arXiv2020

On the iterated estimation of dynamic discrete choice games

We study the asymptotic properties of a class of estimators of the structural parameters in dynamic discrete choice games. We consider K-stage policy iteration (PI) estimators, where K denotes the number of policy iterations employed in the estimation. This class nests several estimators proposed in the literature such as those in Aguirregabiria and Mira (2002, 2007), Pesendorfer and Schmidt-Dengler (2008), and Pakes et al. (2007). First, we establish that the K-PML estimator is consistent and asymptotically normal for all K. This complements findings in Aguirregabiria and Mira (2007), who focus on K=1 and K large enough to induce convergence of the estimator. Furthermore, we show under certain conditions that the asymptotic variance of the K-PML estimator can exhibit arbitrary patterns as a function of K. Second, we establish that the K-MD estimator is consistent and asymptotically normal for all K. For a specific weight matrix, the K-MD estimator has the same asymptotic distribution as the K-PML estimator. Our main result provides an optimal sequence of weight matrices for the K-MD estimator and shows that the optimally weighted K-MD estimator has an asymptotic distribution that is invariant to K. The invariance result is especially unexpected given the findings in Aguirregabiria and Mira (2007) for K-PML estimators. Our main result implies two new corollaries about the optimal 1-MD estimator (derived by Pesendorfer and Schmidt-Dengler (2008)). First, the optimal 1-MD estimator is optimal in the class of K-MD estimators. In other words, additional policy iterations do not provide asymptotic efficiency gains relative to the optimal 1-MD estimator. Second, the optimal 1-MD estimator is more or equally asymptotically efficient than any K-PML estimator for all K. Finally, the appendix provides appropriate conditions under which the optimal 1-MD estimator is asymptotically efficient.

econ.EM↗
arXiv2018

The Security of the United Kingdom Electricity Imports under Conditions of High European Demand

Energy policy in Europe has been driven by the three goals of security of supply, economic competitiveness and environmental sustainability, referred to as the energy trilemma. Although there are clear conflicts within the trilemma, member countries have acted to facilitate a fully integrated European electricity market. Interconnection and cross-border electricity trade has been a fundamental part of such market liberalisation. However, it has been suggested that consumers are exposed to a higher price volatility as a consequence of interconnection. Furthermore, during times of energy shortages and high demand, issues of national sovereignty take precedence over cooperation. In this article, the unique and somewhat peculiar conditions of early 2017 within France, Germany and the United Kingdom have been studied to understand how the existing integration arrangements address the energy trilemma. It is concluded that the dominant interests are economic and national security; issues of environmental sustainability are neglected or overridden. Although the optimisation of European electricity generation to achieve a lower overall carbon emission is possible, such a goal is far from being realised. Furthermore, it is apparent that the United Kingdom, and other countries, cannot rely upon imports from other countries during periods of high demand and/or limited supply.

econ.EM↗
arXiv2018

Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics

Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

econ.EM↗
arXiv2022

De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular functionals include average treatment effects, policy effects, and derivatives conditional on a covariate subvector fixed at a point. We construct a Neyman orthogonal equation for the target parameter that is approximately invariant to small perturbations of the nuisance parameters. To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter. Our analysis yields weak ``double sparsity robustness'': either the approximation to the regression or the approximation to the representer can be ``completely dense'' as long as the other is sufficiently ``sparse''. Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models, translating into honest confidence bands for both global and local parameters.

stat.ML↗
arXiv2018

Measuring the Demand Effects of Formal and Informal Communication : Evidence from Online Markets for Illicit Drugs

I present evidence that communication between marketplace participants is an important influence on market demand. I find that consumer demand is approximately equally influenced by communication on both formal and informal networks- namely, product reviews and community forums. In addition, I find empirical evidence of a vendor's ability to commit to disclosure dampening the effect of communication on demand. I also find that product demand is more responsive to average customer sentiment as the number of messages grows, as may be expected in a Bayesian updating framework.

stat.AP↗
arXiv2019

Kernel Estimation for Panel Data with Heterogeneous Dynamics

This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the cross-sectional sample size (N) and the time-series length (T). In particular, it makes the condition on N and T stronger and more complicated than those typically observed in the long-panel literature without kernel smoothing. We also consider a split-panel jackknife method to correct bias and construction of confidence intervals. An empirical application and Monte Carlo simulations illustrate our procedure in finite samples.

econ.EM↗
arXiv2018

Identifying the occurrence or non occurrence of cognitive bias in situations resembling the Monty Hall problem

People reason heuristically in situations resembling inferential puzzles such as Bertrand's box paradox and the Monty Hall problem. The practical significance of that fact for economic decision making is uncertain because a departure from sound reasoning may, but does not necessarily, result in a "cognitively biased" outcome different from what sound reasoning would have produced. Criteria are derived here, applicable to both experimental and non-experimental situations, for heuristic reasoning in an inferential-puzzle situations to result, or not to result, in cognitively bias. In some situations, neither of these criteria is satisfied, and whether or not agents' posterior probability assessments or choices are cognitively biased cannot be determined.

econ.EM↗
arXiv2018

Forecasting the impact of state pension reforms in post-Brexit England and Wales using microsimulation and deep learning

We employ stochastic dynamic microsimulations to analyse and forecast the pension cost dependency ratio for England and Wales from 1991 to 2061, evaluating the impact of the ongoing state pension reforms and changes in international migration patterns under different Brexit scenarios. To fully account for the recently observed volatility in life expectancies, we propose mortality rate model based on deep learning techniques, which discovers complex patterns in data and extrapolated trends. Our results show that the recent reforms can effectively stave off the "pension crisis" and bring back the system on a sounder fiscal footing. At the same time, increasingly more workers can expect to spend greater share of their lifespan in retirement, despite the eligibility age rises. The population ageing due to the observed postponement of death until senectitude often occurs with the compression of morbidity, and thus will not, perforce, intrinsically strain healthcare costs. To a lesser degree, the future pension cost dependency ratio will depend on the post-Brexit relations between the UK and the EU, with "soft" alignment on the free movement lowering the relative cost of the pension system compared to the "hard" one. In the long term, however, the ratio has a rising tendency.

econ.EM↗
arXiv2018

On the solution of the variational optimisation in the rational inattention framework

I analyse the solution method for the variational optimisation problem in the rational inattention framework proposed by Christopher A. Sims. The solution, in general, does not exist, although it may exist in exceptional cases. I show that the solution does not exist for the quadratic and the logarithmic objective functions analysed by Sims (2003, 2006). For a linear-quadratic objective function a solution can be constructed under restrictions on all but one of its parameters. This approach is, therefore, unlikely to be applicable to a wider set of economic models.

econ.EM↗
arXiv2018

Partial Identification of Expectations with Interval Data

A conditional expectation function (CEF) can at best be partially identified when the conditioning variable is interval censored. When the number of bins is small, existing methods often yield minimally informative bounds. We propose three innovations that make meaningful inference possible in interval data contexts. First, we prove novel nonparametric bounds for contexts where the distribution of the censored variable is known. Second, we show that a class of measures that describe the conditional mean across a fixed interval of the conditioning space can often be bounded tightly even when the CEF itself cannot. Third, we show that a constraint on CEF curvature can either tighten bounds or can substitute for the monotonicity assumption often made in interval data applications. We derive analytical bounds that use the first two innovations, and develop a numerical method to calculate bounds under the third. We show the performance of the method in simulations and then present two applications. First, we resolve a known problem in the estimation of mortality as a function of education: because individuals with high school or less are a smaller and thus more negatively selected group over time, estimates of their mortality change are likely to be biased. Our method makes it possible to hold education rank bins constant over time, revealing that current estimates of rising mortality for less educated women are biased upward in some cases by a factor of three. Second, we apply the method to the estimation of intergenerational mobility, where researchers frequently use coarsely measured education data in the many contexts where matched parent-child income data are unavailable. Conventional measures like the rank-rank correlation may be uninformative once interval censoring is taken into account; CEF interval-based measures of mobility are bounded tightly.

econ.EM↗
arXiv2018

Synthetic Control Methods and Big Data

Many macroeconomic policy questions may be assessed in a case study framework, where the time series of a treated unit is compared to a counterfactual constructed from a large pool of control units. I provide a general framework for this setting, tailored to predict the counterfactual by minimizing a tradeoff between underfitting (bias) and overfitting (variance). The framework nests recently proposed structural and reduced form machine learning approaches as special cases. Furthermore, difference-in-differences with matching and the original synthetic control are restrictive cases of the framework, in general not minimizing the bias-variance objective. Using simulation studies I find that machine learning methods outperform traditional methods when the number of potential controls is large or the treated unit is substantially different from the controls. Equipped with a toolbox of approaches, I revisit a study on the effect of economic liberalisation on economic growth. I find effects for several countries where no effect was found in the original study. Furthermore, I inspect how a systematically important bank respond to increasing capital requirements by using a large pool of banks to estimate the counterfactual. Finally, I assess the effect of a changing product price on product sales using a novel scanner dataset.

econ.EM↗
arXiv2018

Deep Learning for Causal Inference

In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the use of autoencoders for dimensionality reduction while maintaining the local neighborhood structure among the data points in the embedding space. This deep learning based technique is shown to perform better than simple k nearest neighbor matching for estimating treatment effects, especially when the data points have several features/covariates but reside in a low dimensional manifold in high dimensional space. We also observe better performance than manifold learning methods for neighbor matching. 2. Propensity score matching is one specific and popular way to perform matching in order to estimate average and individual treatment effects. We propose the use of deep neural networks (DNNs) for propensity score matching, and present a network called PropensityNet for this. This is a generalization of the logistic regression technique traditionally used to estimate propensity scores and we show empirically that DNNs perform better than logistic regression at propensity score matching. Code for both methods will be made available shortly on Github at: https://github.com/vikas84bf

econ.EM↗
arXiv2021

Permutation Tests for Equality of Distributions of Functional Data

Economic data are often generated by stochastic processes that take place in continuous time, though observations may occur only at discrete times. For example, electricity and gas consumption take place in continuous time. Data generated by a continuous time stochastic process are called functional data. This paper is concerned with comparing two or more stochastic processes that generate functional data. The data may be produced by a randomized experiment in which there are multiple treatments. The paper presents a method for testing the hypothesis that the same stochastic process generates all the functional data. The test described here applies to both functional data and multiple treatments. It is implemented as a combination of two permutation tests. This ensures that in finite samples, the true and nominal probabilities that each test rejects a correct null hypothesis are equal. The paper presents upper and lower bounds on the asymptotic power of the test under alternative hypotheses. The results of Monte Carlo experiments and an application to an experiment on billing and pricing of natural gas illustrate the usefulness of the test.

econ.EM↗
arXiv2018

An Note on Why Geographically Weighted Regression Overcomes Multidimensional-Kernel-Based Varying-Coefficient Model

It is widely known that geographically weighted regression(GWR) is essentially same as varying-coefficient model. In the former research about varying-coefficient model, scholars tend to use multidimensional-kernel-based locally weighted estimation(MLWE) so that information of both distance and direction is considered. However, when we construct the local weight matrix of geographically weighted estimation, distance among the locations in the neighbor is the only factor controlling the value of entries of weight matrix. In other word, estimation of GWR is distance-kernel-based. Thus, in this paper, under stationary and limited dependent data with multidimensional subscripts, we analyze the local mean squared properties of without any assumption of the form of coefficient functions and compare it with MLWE. According to the theoretical and simulation results, geographically-weighted locally linear estimation(GWLE) is asymptotically more efficient than MLWE. Furthermore, a relationship between optimal bandwith selection and design of scale parameters is also obtained.

econ.EM↗