Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y|X; T) = G{... more Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y|X; T) = G{XT O + » + m1(T1) + … + md(Td)}. Here G is a known (link) function, O is an unknown parameter, and m1, …, md are unknown functions. In particular, we consider additive binary response models where the response Y is binary. In these models, given X and T, the response Y has a Bernoulli distribution with parameter G{XT O + » + m1(T1) + … + md(Td)}. The paper discusses estimation of O and m1, …, md. Procedures are proposed for testing linearity of the additive components m1, …, md. Furthermore, bootstrap uniform confidence intervals for the additive components are introduced. The practical performance of the proposed methods is discussed in simulations and in two economic applications.
Summary We introduce bootstrap tests for semiparametric generalized structured models. These can ... more Summary We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All estimators are obtained by smooth backfitting. Simulation studies show excellent performance of the test procedures.
In this paper, we apply machine learning to forecast the conditional variance of long-term stock ... more In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon.
This paper discusses the problem of testing misspecifications in semiparametric regression models... more This paper discusses the problem of testing misspecifications in semiparametric regression models for a large family of econometric models under rather general conditions. We focus on two main issues that typically arise in econometrics. First, many econometric models are estimated through maximum likelihood or pseudo-ML methods like, for example, limited dependent variable or gravity models. Second, often one might not want to fully specify the null hypothesis. Instead, one would rather impose some structure like separability or monotonicity. In order to address these points we introduce an adaptive omnibus test. Special emphasis is given to practical issues like adaptive bandwidth choice, general but simple requirements on the estimates, and finite sample performance, including the resampling approximations.
Computational Statistics & Data Analysis, 1996
In this paper we apply the theory of continued fraction expansions of Stieltjes transforms of pro... more In this paper we apply the theory of continued fraction expansions of Stieltjes transforms of probability measures to the Bayesian D-optimal design problem for nonlinear regression models. Some analytic results are obtained for models with two parameters. A numerical procedure is proposed which is based on this theory and maximizes the criterion function without distinguishing between support points and weights
Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y|X; T) = G{... more Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y|X; T) = G{XT O + » + m1(T1) + … + md(Td)}. Here G is a known (link) function, O is an unknown parameter, and m1, …, md are unknown functions. In particular, we consider additive binary response models where the response Y is binary. In these models, given X and T, the response Y has a Bernoulli distribution with parameter G{XT O + » + m1(T1) + … + md(Td)}. The paper discusses estimation of O and m1, …, md. Procedures are proposed for testing linearity of the additive components m1, …, md. Furthermore, bootstrap uniform confidence intervals for the additive components are introduced. The practical performance of the proposed methods is discussed in simulations and in two economic applications.
Summary We introduce bootstrap tests for semiparametric generalized structured models. These can ... more Summary We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All estimators are obtained by smooth backfitting. Simulation studies show excellent performance of the test procedures.
In this paper, we apply machine learning to forecast the conditional variance of long-term stock ... more In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon.
This paper discusses the problem of testing misspecifications in semiparametric regression models... more This paper discusses the problem of testing misspecifications in semiparametric regression models for a large family of econometric models under rather general conditions. We focus on two main issues that typically arise in econometrics. First, many econometric models are estimated through maximum likelihood or pseudo-ML methods like, for example, limited dependent variable or gravity models. Second, often one might not want to fully specify the null hypothesis. Instead, one would rather impose some structure like separability or monotonicity. In order to address these points we introduce an adaptive omnibus test. Special emphasis is given to practical issues like adaptive bandwidth choice, general but simple requirements on the estimates, and finite sample performance, including the resampling approximations.
Computational Statistics & Data Analysis, 1996
In this paper we apply the theory of continued fraction expansions of Stieltjes transforms of pro... more In this paper we apply the theory of continued fraction expansions of Stieltjes transforms of probability measures to the Bayesian D-optimal design problem for nonlinear regression models. Some analytic results are obtained for models with two parameters. A numerical procedure is proposed which is based on this theory and maximizes the criterion function without distinguishing between support points and weights
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