Advances in Econometrics

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.

Catherine Doz

Anna Petronevich

Dynamic Factor Models

Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach

法国经济商业周期转折点的测定：一种MS-DFM方法

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. We apply our test to test for a key identifying assumption in the literature on nonparametric, nonseparable models by studying the returns to schooling.

Liangjun Su

Halbert L. White

Essays in Honor of Jerry Hausman

Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression

依赖过程的条件独立性规范检验与局部多项式分位数回归

The objective of this research is to examine, validate, and recommend techniques for handling the problem of missingness in observational data. We use a rich observational data set, the NielsenHomeScan data set, which allows us to effectively combine elements from simulated data sets: large numbers of observations, large number of data sets and variables, allowing elements of ‘‘design’’ that typically come with simulated data, and its observational nature. We created random 20% and 50% uniform missingness in our data sets and employed several widely used methods of single imputation, such as mean, regression, and stochastic regression imputations, and multiple imputation methods to fill in the data gaps. We compared these methods by measuring the error of predicting the missing Partial funding for this research came from a cooperative agreement with the Economic Research Service, USDA. Missing Data Methods: Cross-sectional Methods and Applications Advances in Econometrics, Volume 27A, 313–337 Copyright r 2011 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-9053/doi:10.1108/S0731-9053(2011)000027A015 313 D ow nl oa de d by T ex as A & M U ni ve rs ity A t 0 9: 15 0 2 Ju ly 2 01 9 (P T ) values and the parameter estimates from the subsequent regression analysis using the imputed values. We also compared coverage or the percentages of intervals that covered the true parameter in both cases. Based on our results, themethod of single regression or conditional mean imputation provided the best predictions of the missing price values with 28.34 and 28.59 mean absolute percent errors in 20% and 50%missingness settings, respectively. The imputation from conditional distribution method had the best rate of coverage. The parameter estimates based on data sets imputed by conditional mean method were consistently unbiased and had the smallest standard deviations. Themultiple imputationmethods had the best coverage of both the parameter estimates and predictions of the dependent variable.

G. Kyureghian

O. Capps

Missing Data Methods: Cross-sectional Methods and Applications

缺失数据方法：横截面方法与应用

Sam Mirmirani

Hsi Cheng Li

Applications of Artificial Intelligence in Finance and Economics

A COMPARISON OF VAR AND NEURAL NETWORKS WITH GENETIC ALGORITHM IN FORECASTING PRICE OF OIL

油价预测中VAR与神经网络结合遗传算法的比较

出版信息	出版商: Emerald Group Publishing Ltd.，出版周期: ，期刊类型: book-series，开源期刊: 否
基本数据	创刊年份: 2024，原创研究文献占比: ，自引率:， Gold OA占比:

Advances in Econometrics

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Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach

Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression

Missing Data Methods: Cross-sectional Methods and Applications

A COMPARISON OF VAR AND NEURAL NETWORKS WITH GENETIC ALGORITHM IN FORECASTING PRICE OF OIL

Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach

Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression

Missing Data Methods: Cross-sectional Methods and Applications

A COMPARISON OF VAR AND NEURAL NETWORKS WITH GENETIC ALGORITHM IN FORECASTING PRICE OF OIL

ISSN	0731-9053
h-index	29