Journal ArticleDOI
Testing Parametric Conditional Distributions of Dynamic Models
TLDR
In this article, a nonparametric test for parametric conditional distributions of dynamic models is proposed, coupled with Khmaladze's martingale transformation, which is asymptotically distribution-free and has nontrivial power against root-n local alternatives.Abstract:
This paper proposes a nonparametric test for parametric conditional distributions of dynamic models. The test is of the Kolmogorov type coupled with Khmaladze's martingale transformation. It is asymptotically distribution-free and has nontrivial power against root-n local alternatives. The method is applicable for various dynamic models, including autoregressive and moving average models, generalized autoregressive conditional heteroskedasticity (GARCH), integrated GARCH, and general nonlinear time series regressions. The method is also applicable for cross-sectional models. Finally, we apply the procedure to testing conditional normality and the conditional t-distribution in a GARCH model for the NYSE equal-weighted returns.read more
Citations
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Journal ArticleDOI
Instrumental quantile regression inference for structural and treatment effect models
TL;DR: The authors introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods for estimation and inference, which can be used to evaluate the impact of endogenous variables or treatments on the entire distribution of outcomes.
Journal ArticleDOI
Comparing Density Forecasts via Weighted Likelihood Ratio Tests
TL;DR: In this paper, the authors propose a test for comparing the out-of-sample accuracy of competing density forecasts of a variable, which is valid under general conditions: the data can be heterogeneous and the forecasts can be based on (nested or non-nested) parametric models or produced by semiparametric, nonparametric, or Bayesian estimation techniques.
Journal ArticleDOI
Tests for Skewness, Kurtosis, and Normality for Time Series Data
Jushan Bai,Serena Ng +1 more
TL;DR: Combini et al. as discussed by the authors presented the sampling distributions for the coefficient of skewness, kurtosis, and a joint test of normality for time series observations for serially correlated data.
Journal ArticleDOI
Combining density forecasts
TL;DR: In this paper, the authors proposed a data-driven approach to the direct combination of density forecasts by taking a weighted linear combination of the competing density forecasts. But the combination weights are chosen to minimize the distance between the forecasted and true but unknown density, as measured by the Kullback-Leibler information criterion.
Journal ArticleDOI
Forecasting economic and financial time-series with non-linear models
TL;DR: This paper discusses the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series, and discusses a number of areas which have received considerable attention in the recent literature, but where many questions remain.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Techniques for Testing the Constancy of Regression Relationships Over Time
TL;DR: In this paper, the stability over time of regression relationships is investigated using recursive residuals, defined to be uncorrelated with zero means and constant variance, and tests based on the cusum and cusume of squares of recursive residual coefficients are developed.
Book
Statistical Models Based on Counting Processes
TL;DR: Statistical Models Based on Counting Processes (SBP) as discussed by the authors is a monograph for mathematical statisticians and biostatisticians, although almost all methods are given in sufficient detail to be used in practice by other mathematically oriented researchers studying event histories.