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on Econometrics |
| By: | Ozabaci, Deniz (Binghamton University, New York); Henderson, Daniel J. (University of Alabama); Su, Liangjun (Singapore Management University) |
| Abstract: | In this paper we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within groups, we are able to contradict many findings in the literature (e.g., we do not find any significant differences in returns between boys and girls or for formal versus informal child care). |
| Keywords: | oracle estimation, generated regressors, endogeneity, additive regression, structural equation, child care, nonparametric regression, test scores |
| JEL: | C14 C36 I21 J13 |
| Date: | 2014–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp8144&r=ecm |
| By: | Heni Boubaker; Nadia Sghaier |
| Abstract: | In this paper, we propose a time-varying long memory model where the fractional integration parameter varies nonlinearly according to Smooth Transition Regressive (STR) model. To estimate the fractional integration parameter, we suggest a new estimation method based on wavelet approach. In particular, we consider the instan- taneous least squares estimator (ILSE). We conduct some simulation experiments and provide an empirical application to modeling the dynamics of volatilities of some fi- nancial time series. The obtained results show that the model proposed offers an interesting framework to describe time-varying long range dependence of volatilities and provide evidence of regime change in persistence to shocks. |
| Date: | 2014–04–29 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-284&r=ecm |
| By: | Nonejad, Nima |
| Abstract: | Particle Gibbs with ancestor sampling (PG-AS) is a new tool in the family of sequential Monte Carlo methods. We apply PG-AS to the challenging class of unobserved component time series models and demonstrate its flexibility under different circumstances. We also combine discrete structural breaks within the unobserved component model framework. We do this by modeling and forecasting time series characteristics of postwar US inflation using a long memory autoregressive fractionally integrated moving average model with stochastic volatility where we allow for structural breaks in the level, long and short memory parameters contemporaneously with breaks in the level, persistence and the conditional volatility of the volatility of inflation. |
| Keywords: | Ancestor sampling, Bayes, Particle filtering, Structural breaks |
| JEL: | C11 C22 C52 C63 |
| Date: | 2014–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:55664&r=ecm |
| By: | Andini, Corrado (University of Madeira) |
| Abstract: | A well-established empirical literature suggests that individual wages are persistent. Several theoretical arguments support this empirical finding. Yet, the standard approach to the estimation of schooling returns does not account for this fact. This paper investigates the consequences of disregarding earnings persistence. In particular, it shows that the most commonly used static-model estimators of schooling coefficients are subject to an omitted-variable bias which can be named "persistence bias". |
| Keywords: | schooling, wages, dynamic panel-data models |
| JEL: | C23 I21 J31 |
| Date: | 2014–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp8143&r=ecm |
| By: | Daniel J. Henderson (Department of Economics, State University of New York at Binghamton); Qi Li (Department of Economics, Texas A&M University); Christopher F. Parmeter (Department of Economics, University of Miami) |
| Abstract: | Uncovering gradients is of crucial importance across a broad range of economic environments. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. The procedure developed here is automatic and does not require initial estimation of unknown functions with pilot bandwidths. We prove that it delivers bandwidths which have the optimal rate of convergence for the gradient. Both simulated and empirical examples showcase the finite sample attraction of this new mechanism. |
| Keywords: | Gradient Estimation, Kernel Smoothing, Least Squares Cross Validation |
| JEL: | C1 |
| Date: | 2013–10–30 |
| URL: | http://d.repec.org/n?u=RePEc:mia:wpaper:2014-01&r=ecm |
| By: | Alexander Razen; Wolfgang Brunauer; Nadja Klein; Thomas Kneib; Stefan Lang; Nikolaus Umlauf |
| Abstract: | The Basel II framework strictly defines the conditions under which financial institutions are authorized to accept real estate as collateral in order to decrease their credit risk. A widely used concept for its valuation is the hedonic approach. It assumes, that a property can be characterized by a bundle of covariates that involves both individual attributes of the building itself and locational attributes of the region where the building is located in. Each of these attributes can be assigned an implicit price, summing up to the value of the entire property. With respect to value-at-risk concepts financial institutions are often not only interested in the expected value but also in different quantiles of the distribution of real estate prices. To meet these requirements, we develop and compare multilevel structured additive regression models based on GAMLSS type approaches and quantile regression, respectively. Our models involve linear, nonlinear and spatial effects. Nonlinear effects are modeled with P-splines, spatial effects are represented by Gaussian Markov random fields. Due to the high complexity of the models statistical inference is fully Bayesian and based on highly efficient Markov chain Monte Carlo simulation techniques. |
| Keywords: | Bayesian hierarchical models, hedonic pricing models, GAMLSS, distributional regression quantile regression, multilevel models, MCMC, P-splines, value-at-risk |
| Date: | 2014–04 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2014-12&r=ecm |
| By: | Liang, Che-Yuan (Uppsala Center for Fiscal Studies) |
| Abstract: | I develop a structural method for evaluating labor supply in nonlinear budget sets that does not require any distributional assumptions. The model only requires that preferences are convex on the budget frontier. It can be extended to account for features such as fixed costs of work and the stigma cost of welfare participation. It can also be adapted for estimation of earnings, hours of work, and functions that depend on the labor supply distribution, including tax revenue and cumulative distribution functions. The method is applied to estimate the effects of taxes on various labor supply outcomes in the U.S. and Sweden. |
| Keywords: | nonlinear budget sets; structural models; distribution-free estimation; labor supply |
| JEL: | D04 H24 J22 |
| Date: | 2014–04–14 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:uufswp:2014_004&r=ecm |
| By: | Korkmaz, E.; Fok, D.; Kuik, R. |
| Abstract: | Buy-till-you-defect [BTYD] models are built for companies operating in a non- contractual setting to predict customers’ transaction frequency, amount and timing as well as customer lifetime. These models tend to perform well, although they often predict unrealistically long lifetimes for a substantial fraction of the customer base. This obvious lack of face validity limits the adoption of these models by practitioners. Moreover, it highlights a flaw in these models. Based on a simulation study and an empirical analysis of different datasets, we argue that such long lifetime predictions can result from the existence of multiple segments in the customer base. In most cases there are at least two segments: one consisting of customers who purchase the service or product only a few times and the other of those who are frequent purchasers. Customer heterogeneity modeling in the current BTYD models is insufficient to account for such segments, thereby producing unrealistic lifetime predictions. We present an extension over the current BTYD models to address the extreme lifetime prediction issue where we allow for segments within the customer base. More specifically, we consider a mixture of log-normals distribution to capture the heterogeneity across customers. Our model can be seen as a variant of the hierarchical Bayes [HB] Pareto/NBD model. In addition, the proposed model allows us to relate segment membership as well as within segment customer heterogeneity to selected customer characteristics. Our model, therefore, also increases the explanatory power of BTYD models to a great extent. We are now able to evaluate the impact of customers’ characteristics on the membership probabilities of different segments. This allows, for example, one to a-priori predict which customers are likely to become frequent purchasers. The proposed model is compared against the benchmark Pareto/NBD model (Schmittlein, Morrison, and Colombo 1987) and its HB extension (Abe 2009) on simulated datasets as well as on a real dataset from a large grocery e-retailer in a Western European country. Our BTYD model indeed provides a useful customer segmentation that allows managers to draw conclusions on how customers’ purchase and defection behavior are associated with their shopping characteristics such as basket size and the delivery fee paid. |
| Keywords: | buy-till-you-defect models, segmentation, mixture of normals, Bayesian estimation, customer base analysis |
| Date: | 2014–04–24 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:51244&r=ecm |
| By: | Blöchl, Andreas |
| Abstract: | Penalized splines have become a popular tool to model the trend component in economic time series. The outcome of the spline predominantly depends on the choice of a penalization parameter that controls the smoothness of the trend. This paper derives the penalization of splines by frequency domain aspects and points out their link to rational square wave filters. As a novel contribution this paper focuses on the so called excess variability at the margins that describes the undesired increasing variability of the trend estimation to the ends of the series. It will be shown that the too high volatility at the margins can be reduced considerably by a time varying penalization, which yields more reliable estimations for the most recent periods. |
| Keywords: | excess variability; penalized splines; spectral analysis; time varying penalization; trends |
| Date: | 2014–04 |
| URL: | http://d.repec.org/n?u=RePEc:lmu:muenec:20687&r=ecm |
| By: | Siobhan Austen (School of Economics and Finance, Curtin University); Rachel Ong (School of Economics and Finance, Curtin University); Richard Seymour (Bankwest Curtin Economics Centre, Curtin Business School, Curtin University) |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:ozl:bcecwp:wp1311&r=ecm |