Econometrics
http://lists.repec.org/mailman/listinfo/nep-ecm
Econometrics
2019-12-02
Statistical Inference on Partially Linear Panel Model under Unobserved Linearity
http://d.repec.org/n?u=RePEc:arx:papers:1911.08830&r=ecm
A new statistical procedure, based on a modified spline basis, is proposed to identify the linear components in the panel data model with fixed effects. Under some mild assumptions, the proposed procedure is shown to consistently estimate the underlying regression function, correctly select the linear components, and effectively conduct the statistical inference. When compared to existing methods for detection of linearity in the panel model, our approach is demonstrated to be theoretically justified as well as practically convenient. We provide a computational algorithm that implements the proposed procedure along with a path-based solution method for linearity detection, which avoids the burden of selecting the tuning parameter for the penalty term. Monte Carlo simulations are conducted to examine the finite sample performance of our proposed procedure with detailed findings that confirm our theoretical results in the paper. Applications to Aggregate Production and Environmental Kuznets Curve data also illustrate the necessity for detecting linearity in the partially linear panel model.
Ruiqi Liu
Ben Boukai
Zuofeng Shang
2019-11
Causal Inference Under Approximate Neighborhood Interference
http://d.repec.org/n?u=RePEc:arx:papers:1911.07085&r=ecm
This paper studies causal inference in randomized experiments under network interference. Most existing models of interference posit that treatments assigned to alters only affect the ego's response through a low-dimensional exposure mapping, which only depends on units within some known network radius around the ego. We propose a substantially weaker "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small, but potentially nonzero, impact on the ego's response. Unlike the exposure mapping model, we can show that ANI is satisfied in well-known models of social interactions. Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal. For practical inference, we propose a new conservative variance estimator based on a network bootstrap and suggest a data-dependent bandwidth using the network diameter. Finally, we illustrate our results in a simulation study and empirical application.
Michael P. Leung
2019-11
Instrument-free inference under confined regressor endogeneity; derivations and applications
http://d.repec.org/n?u=RePEc:pra:mprapa:96839&r=ecm
A fully-fledged alternative to Two-Stage Least-Squares (TSLS) inference is developed for general linear models with endogenous regressors. This alternative approach does not require the adoption of external instrumental variables. It generalizes earlier results which basically assumed all variables in the model to be normally distributed and their observational units to be stochastically independent. Now the chosen underlying framework corresponds completely to that of most empirical cross-section or time-series studies using TSLS. This enables revealing empirically relevant replication studies, also because the new technique allows testing the earlier untestable exclusion restrictions adopted when applying TSLS. For three illustrative case studies a new perspective on their empirical findings results. The new technique is computationally not very demanding. It involves scanning least-squares-based results over all compatible values of the nuisance parameters established by the correlations between regressors and disturbances.
Kiviet, Jan
endogeneity robust inference, instrument validity tests, replication studies, sensitivity analysis, two-stage least-squares.
2019-11-06
Structural stability of infinite-order regression
http://d.repec.org/n?u=RePEc:arx:papers:1911.08637&r=ecm
We develop a class of tests for the structural stability of infinite-order models such as the infinite-order autoregressive model and the nonparametric sieve regression. When the number $ p $ of restrictions diverges, the traditional tests based on the suprema of Wald, LM and LR statistics or their exponentially weighted averages diverge as well. We introduce a suitable transformation of these tests and obtain proper weak limits under the condition that $p $ grows to infinity as the sample size $n $ goes to infinity. In general, this limit distribution is different from the sequential limit, which can be obtained by increasing the order of the standardized tied-down Bessel process in Andrews (1993). In particular, our joint asymptotic analysis discovers a nonlinear high order serial correlation, for which we provide a consistent estimator. Our Monte Carlo simulation illustrates the importance of robustifying the structural break test against the nonlinear serial correlation even when $ p $ is moderate. Furthermore, we also establish a weighted power optimality property of our tests under some regularity conditions. We examine finite-sample performance in a Monte Carlo study and illustrate the test with a number of empirical examples.
Abhimanyu Gupta
Myung Hwan Seo
2019-11
A New Strategy to Identify Causal Relationships: Estimating a Binding Average Treatment Effect
http://d.repec.org/n?u=RePEc:iza:izadps:dp12766&r=ecm
This paper proposes a new strategy to identify causal effects. Instead of finding a conventional instrumental variable correlated with the treatment but not with the confounding effects, we propose an approach which employs an instrument correlated with the confounders, but which itself is not causally related to the direct effect of the treatment. Utilizing such an instrument enables one to estimate the confounding endogeneity bias. This bias can then be utilized in subsequent regressions first to obtain a "binding" causal effect for observations unaffected by institutional barriers that eliminate a treatment's effectiveness, and second to obtain a population-wide treatment effect for all observations independent of institutional restrictions. Both are computed whether the treatment effects are homogeneous or heterogeneous. To illustrate the technique, we apply the approach to estimate sheepskin effects. We find the bias to be approximately equal to the OLS coefficient, meaning that the sheepskin effect is near zero. This result is consistent with Flores-Lagunes and Light (2010) and Clark and Martorell (2014). Our technique expands the econometrician's toolkit by introducing an alternative method that can be used to estimate causality. Further, one potentially can use both the conventional instrumental variable approach in tandem with our alternative approach to test the equality of the two estimators for a conventionally exactly identified causal model, should one claim to already have a valid conventional instrument.
Das, Tirthatanmoy
Polachek, Solomon
causality, OLS biases, sheepskin effects
2019-11
Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games
http://d.repec.org/n?u=RePEc:ehl:lserod:86858&r=ecm
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 all, of the payoff function. We show the discount factor can be generically identifed jointly with the payoff parameters. It is known the payoff function cannot 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 from Ryan (2012).
Komarova, Tatiana
Sanches, Fábio Adriano
Silva Junior, Daniel
Srisuma, Sorawoot
discount factor; dynamic discrete choice problem; identification; estimation; switching costs
2018-11-01
Mathematical Modeling and Inference for Degree-capped Ego-centric Network Sampling
http://d.repec.org/n?u=RePEc:osf:socarx:5kez8&r=ecm
The structure of social networks is usually inferred from limited sets of observations via suitable network sampling designs. In offline social network sampling, for practical considerations, researchers sometimes build in a cap on the number of social ties any respondent may claim. It is commonly known in the literature that using a cap on the degrees begets methodologically undesirable features because it discards information about the network connections. In this paper, we consider a mathematical model of this sampling procedure and seek analytical solutions to recover some of the lost information about the underlying network. We obtain closed-form expressions for several network statistics, including the first and second moments of the degree distribution, network density, number of triangles, and clustering. We corroborate the accuracy of these estimators via simulated and empirical network data. Our contribution highlights notable room for improvement in the analysis of some existing social network data sets.
Fotouhi, Babak
Rytina, Steven
2018-11-29
The Multivariate Random Preference Estimatorfor Switching Multiple Price List Data
http://d.repec.org/n?u=RePEc:uea:ueaeco:2019_04&r=ecm
The use of Multiple Price Lists to elicit individuals' risk preferences is widespread. To model data collected through this method, we introduce the Multivariate Random Preference (MRP) estimator, specifically designed for the \switching" variant of such lists. This is a new estimation approach that enables us to exploit all available information derived from subjects' switch points in the lists. Monte Carlo simulations show that our estimator is consistent and has good small-sample properties. The estimator is derived for a two-parameter model in a risky context.
Anna Conte
Peter G Moffatt
Mary Riddel
Risk Preference; Monte Carlo Simulations; Importance Sampling
2019-11-21
Semiparametric Estimation of Correlated Random Coefficient Models without Instrumental Variables
http://d.repec.org/n?u=RePEc:arx:papers:1911.06857&r=ecm
We study a linear random coefficient model where slope parameters may be correlated with some continuous covariates. Such a model specification may occur in empirical research, for instance, when quantifying the effect of a continuous treatment observed at two time periods. We show one can carry identification and estimation without instruments. We propose a semiparametric estimator of average partial effects and of average treatment effects on the treated. We showcase the small sample properties of our estimator in an extensive simulation study. Among other things, we reveal that it compares favorably with a control function estimator. We conclude with an application to the effect of malaria eradication on economic development in Colombia.
Samuele Centorrino
Aman Ullah
Jing Xue
2019-11
Mediation and Moderation in Statistical Network Models
http://d.repec.org/n?u=RePEc:osf:socarx:9bs4u&r=ecm
Statistical network methods have grown increasingly popular in the social sciences. However, like other nonlinear probability models, statistical network model parameters can only be identiﬁed to a scale and cannot be compared between groups or models ﬁt to the same network. This study addresses these issues by developing methods for mediation and moderation analyses in exponential random graph models (ERGM). It ﬁrst discusses ERGM as an autologistic regression to illustrate that ERGM estimates can be aﬀected by unobserved heterogeneity. Second, it develops methods for mediation analysis for both discrete and continuous mediators. Third, it provides recommendations and methods for interpreting interactions in ERGM. Finally, it considers scenarios where interactions are implicated in mediation analysis. The methodological discussion is accompanied with empirical applications and extensions to other classes of statistical network models are discussed.
Duxbury, Scott W
2019-07-17
Synthetic Controls with Imperfect Pre-Treatment Fit
http://d.repec.org/n?u=RePEc:arx:papers:1911.08521&r=ecm
We analyze the properties of the Synthetic Control (SC) and related estimators when the pre-treatment fit is imperfect. In this framework, we show that these estimators are generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre-treatment periods goes to infinity. Still, we also show that a modified version of the SC method can substantially improve in terms of bias and variance relative to the difference-in-difference estimator. We also consider the properties of these estimators in settings with non-stationary common factors.
Bruno Ferman
Cristine Pinto
2019-11
Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-residuals Approach
http://d.repec.org/n?u=RePEc:osf:socarx:86d2k&r=ecm
Abstract Analyses of causal mediation are often complicated by treatment-induced confounders of the mediator-outcome relationship. In the presence of such confounders, the natural direct and indirect effects of treatment on the outcome, into which the total effect can be additively decomposed, are not identified. An alternative but similar set of effects, known as randomized intervention analogues to the natural direct effect (R-NDE) and the natural indirect effect (R-NIE), can still be identified in this situation, but existing estimators for these effects require a complicated weighting procedure that is difficult to use in practice. In this paper, we introduce a new method for estimating the R-NDE and R-NIE that involves only a minor adaption of the comparatively simple regression methods used to perform effect decomposition in the absence of treatment-induced confounding. It involves fitting linear models for (a) the conditional mean of the mediator given treatment and a set of baseline confounders and (b) the conditional mean of the outcome given the treatment, mediator, baseline confounders, and the treatment-induced confounders after first residualizing them with respect to the observed past. The R-NDE and R-NIE are simple functions of the parameters in these models when they are correctly specified and when there are no unobserved variables that confound the treatment-outcome, treatment-mediator, or mediator-outcome relationships. We illustrate the method by decomposing the effect of education on depression symptoms at midlife into components operating through income versus alternative factors. R and Stata packages are available for implementing the proposed method.
Wodtke, Geoffrey
Zhou, Xiang
2019-05-15
Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis
http://d.repec.org/n?u=RePEc:arx:papers:1911.08662&r=ecm
This paper studies the theoretical predictive properties of classes of forecast combination methods. The study is motivated by the recently developed Bayesian framework for synthesizing predictive densities: Bayesian predictive synthesis. A novel strategy based on continuous time stochastic processes is proposed and developed, where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito's lemma. We show that a subclass of synthesis functions under Bayesian predictive synthesis, which we categorize as non-linear synthesis, entails an extra term that "corrects" the bias from misspecification and dependence in the predictive error process, effectively improving forecasts. Theoretical properties are examined and shown that this subclass improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions. We discuss the conditions for which this subclass outperforms others, and its implications for developing forecast combination methods. A finite sample simulation study is presented to illustrate our results.
Kosaku Takanashi
Kenichiro McAlinn
2019-11
Inference in Models of Discrete Choice with Social Interactions Using Network Data
http://d.repec.org/n?u=RePEc:arx:papers:1911.07106&r=ecm
This paper studies inference in models of discrete choice with social interactions when the data consists of a single large network. We provide theoretical justification for the use of spatial and network HAC variance estimators in applied work, the latter constructed by using network path distance in place of spatial distance. Toward this end, we prove new central limit theorems for network moments in a large class of social interactions models. The results are applicable to discrete games on networks and dynamic models where social interactions enter through lagged dependent variables. We illustrate our results in an empirical application and simulation study.
Michael P. Leung
2019-11
Rank Correction: A New Approach to Differential Non-Response in Wealth Survey Data
http://d.repec.org/n?u=RePEc:ico:wpaper:101&r=ecm
This paper develops a new approach for dealing with the under-reporting of wealth in household survey data (differential nonresponse). The current practice among researchers relying on household wealth survey data is one out of three approaches. First, simply ignore the problem. Second, fit a Pareto distribution to the tail of the survey data and use that distribution. Third, add rich list data to the sample and fit a Pareto distribution to the combined data (Vermeulen, 2018). We propose a fourth approach - the rank correction approach - which improves over the first two and does not require information drawn from publicly available rich lists. We show by means of Monte Carlo simulations that this rank correction approach substantially reduces nonresponse bias in the Pareto tail estimates. Applying the procedure to wealth survey data (HFCS, SCF, WAS) yields substantial increases in aggregate wealth and top wealth shares, which are closely in line with wealth summary statistics from other sources such as the World Inequality Database. As such the rank correction approach can serve as a complement and robustness check to Vermeulenâ€™s (2018) rich list approach and as an attractive alternative to the second approach in situations where rich list data is not available or of poor quality.
Jakob Kapeller
Rafael Wildauer
Wealth distribution, differential nonresponse, Pareto distribution
2019-11
The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely
http://d.repec.org/n?u=RePEc:nbr:nberwo:26463&r=ecm
A common challenge in estimating the long-term impacts of treatments (e.g., job training programs) is that the outcomes of interest (e.g., lifetime earnings) are observed with a long delay. We address this problem by combining several short-term outcomes (e.g., short-run earnings) into a \surrogate index," the predicted value of the long-term outcome given the short-term outcomes. We show that the average treatment effect on the surrogate index equals the treatment effect on the long-term outcome under the assumption that the long-term outcome is independent of the treatment conditional on the surrogate index. We then characterize the bias that arises from violations of this assumption, deriving feasible bounds on the degree of bias and providing simple methods to validate the key assumption using additional outcomes. Finally, we develop efficient estimators for the surrogate index and show that even in settings where the long-term outcome is observed, using a surrogate index can increase precision. We apply our method to analyze the long-term impacts of a multi-site job training experiment in California. Using short-term employment rates as surrogates, one could have estimated the program's impacts on mean employment rates over a 9 year horizon within 1.5 years, with a 35% reduction in standard errors. Our empirical results suggest that the long-term impacts of programs on labor market outcomes can be predicted accurately by combining their short-term treatment effects into a surrogate index.
Susan Athey
Raj Chetty
Guido W. Imbens
Hyunseung Kang
2019-11
Strategic judgment: its game-theoretic foundations,its econometric elicitation
http://d.repec.org/n?u=RePEc:sap:wpaper:wp190&r=ecm
We provide a new frequentist methodology that detects forecasting bias due to strategic interaction. This is based on a new environment, named "Scoring Structure", where a Forecast User interacts with a Forecast Producer and Reality. A formal test for the null hypothesis of linearity in Scoring Structure is introduced. Linearity implies that forecasts are strategically coherent with evaluations and vice-versa. The new test has good small-sample properties and behaves consistently with theoretical requirements. We illustrate the use of the Scoring Structure and the coherence test via two case studies on the assessment of the probability of recessions for the U.S. economy and the evaluation of Norges Bankâ€™s Fan Charts of Output Gap. These support the endemic nature of the strategic judgment in Macroeconomics. Finally, we discuss the economic interpretation of the results obtained by our approach.
Emilio Zanetti Chini
Business Cycle, Predictive Density, Forecast Evaluation, Coherence Testing,Scoring Rules and Structures
2019-10
Robust Estimation of Risk-Neutral Moments
http://d.repec.org/n?u=RePEc:usg:sfwpfi:2019:02&r=ecm
This study provides an in-depth analysis of how to estimate risk-neutral moments robustly. A simulation and an empirical study show that estimating risk-neutral moments presents a trade-off-between (1) the bias of estimates caused by a limited strike price domain and (2) the variance of estimates induced by mirco-structural noise. The best trade-off is offered by option-implied quantile moments estimated from a volatility surface interpolated with a local-linear kernel regression and extrapolated linearly. A similarly good trade-off is achieved by estimating regular central option-implied moments from a volatility surface interpolated with a cubic smoothing spline and flat extrapolation.
Manuel Ammann
Alexander Feser
risk-neutral moments, risk-neutral distribution
2019-03
Bayesian regularized artificial neural networks for the estimation of the probability of default
http://d.repec.org/n?u=RePEc:ehl:lserod:101029&r=ecm
Artificial neural networks (ANN) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANN are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANN and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.
Sariev, Eduard
Germano, Guido
Artificial neural networks; Bayesian regularization; Credit risk; Probability of default; ES/K002309/1
2019-10-31
Can p-values be meaningfully interpreted without random sampling?
http://d.repec.org/n?u=RePEc:osf:socarx:yazr8&r=ecm
Besides the inferential errors that abound in the interpretation of p-values, the probabilistic pre-conditions (i.e. random sampling or equivalent) for using them at all are not often met by observa-tional studies in the social sciences. This paper systematizes different sampling designs and discusses the restrictive requirements of data collection that are the sine-qua-non for using p-values.
Hirschauer, Norbert
Gruener, Sven
Mußhoff, Oliver
Becker, Claudia
Jantsch, Antje
2019-08-15