W. Hardle

Testing Linearity in AR Errors-in-variables Model with Application to Stochastic Volatility (2000)

D. Feldmann, W. Hardle, C. Hafner, M. Hoffmann, O. Lepski, A. Tsybakov

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given data set, a statistical test is required. In...

Symmetrized Nearest Neighbor Regression Estimates. (1998)

Carroll, R. J., Hardle, W.

The authors consider univariate nonparametric regression. Two standard nonparametric regression function estimates are kernel estimates and nearest neighbor estimates. Mack noted that both methods...

Semiparametric Single Index Versus Fixed Link Function Modelling (1997)

W. Hardle, V. Spokoiny, S. Sperlich

Discrete choice models are frequently used in statistical and econometric practice. Standard models such as logit models are based on exact knowledge of the form of the link and linear index...

On Adaptive Estimation in Partial Linear Models (1997)

G. Golubev, W. Hardle

The problem of estimation of the finite dimensional parameter in a partial linear model is considered. We derive upper and lower bounds for the second minimax order risk and show that the second...

Nonparametric Time Series Model Selection (1996)

W. Hardle, L. Yang, Wirtschaftswissenschaftliche Fakultat

Nonparametric procedures are an interesting alternative to classical time series analysis. The nonparametric technique follows the principle of `letting the data speak for themselves,' and provides...

On Adaptive Estimation in Partial Linear Models (1970)

G. Golubev, W. Hardle

The problem of estimation of the finite dimensional parameter in a partial linear model is considered. We derive upper and lower bounds for the second minimax order risk and show that the second...

Nonparametric Time Series Model Selection (1970)

W. Hardle, L. Yang, Wirtschaftswissenschaftliche Fakultat

Nonparametric procedures are an interesting alternative to classical time series analysis. The nonparametric technique follows the principle of `letting the data speak for themselves,' and provides...

Nonparametric Vector Autoregression (1970)

W. Hardle, Spandauer Strasse, A. Tsybakov, L. Yang, Wirtschaftswissenschaftliche Fakultat

We consider a vector conditional heteroskedastic autoregressive nonlinear (CHARN) model in which both the conditional mean and the conditional variance (volatility) matrix are unknown functions of...

Optimal Median Smoothing (1970)

W. Hardle, Wirtschaftswissenschaftliche Fakultat, W. Steiger

Median smoothing of a series of data values is considered. Naive programming of such an algorithm would result in large amount of computation, especially when the series of data values is long. By...

Semiparametric Single Index Versus Fixed Link Function Modelling (1970)

W. Hardle, V. Spokoiny, S. Sperlich

Discrete choice models are frequently used in statistical and econometric practice. Standard models such as logit models are based on exact knowledge of the form of the link and linear index...

Testing Increasing Dispersion (1970)

W. Hardle

Increasing dispersion in regression analysis means that with positive changes of the explanatory variable the residual variance increases. Motivated by theoretical questions in stability of demand...

Flexible Stochastic Volatility Structures for High Frequency Financial Data (1970)

D. Feldmann, W. Hardle, C. Hafner, M. Hoffmann, O. Lepski, A. B. Tsybakov

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In...

Better Bootstrap Confidence Intervals for Regression Curve Estimation (1970)

W. Hardle, S. Huet

Bootstrap methods in curve estimation have been introduced for smoothing parameter selection and for construction of confidence intervals. Most of the papers on confidence intervals use explicit bias...

Additive Nonparametric Regression on Principal Components (1970)

W. Hardle, A. B. Tsybakov

Nonparametric regression smoothing in high dimensions faces the problem of data sparseness. Additive regression models alleviate this problem by fitting a sum of one-dimensional smooth functions. A...

Search of Significant Variables in Nonparametric Additive Regression (1970)

W. Hardle, Wirtschaftswissenschaftliche Fakultat, A. Korostelev

Nonparametric additive regression is studied under the assumption that only a part of nonparametric components is separated away from zero. Each of these non-zero components depends on its own...

Fast and Simple Scatterplot Smoothing (1970)

W. Hardle

An important element of both exploratory data analysis and many dimensionality reduction techniques is a scatterplot smoother. In both areas there is a strong need for fast and simple procedures. A...

Discussion (1970)

W. Hardle, J. S. Marron, L. Yang, Wirtschaftswissenschaftliche Fakultat

chaft. 1 Interpretability Many statisticians view simplicity and intuitive understanding of "what the smooth is doing to the data", as very important criteria in choosing a smoothing method. In this...

Applied Nonparametric Methods.

Hardle, W.

economic models ; econometrics

HERNEL REGRESSION SMOOTHING OF TIME SERIES.

HARDLE, W., VIEU, P.

evaluation ; prices ; stochastic processes ; forecasts

ROBUST LOCALLY ADAPTIVE NONPARAMETRIC REGRESSION.

HARDLE, W., TSYBAKOV, A.

evaluation ; econometric models ; forecasts

REMARKS ON SLICED INVERSE REGRESSION.

HARDLE, W., TSYBAKOV, A.B.

evaluation ; forecasts ; econometrics

Optimal Median Smoothing.

Hardle, W., Steiger, W.

econometrics ; economic models ; computation

Applied Nonparametric Methods.

Hardle, W.

econometrics ; economic models

Testing a Regression Model when we Have Smooth Alternatives in Mind

Hardle, W., Kneip, A.

Goodness-of-fit tests based on residual sum of squares are a standard procedure in fitiing regression models. Often we have a smooth alternative in mind, a qualitative feature that the X2-test does...

Bootstarp Methods in Nonparametric Regression.

Hardle, W., Mammen, E.

regression analysis ; economic models ; econometrics

Testing Increasing Dispersion.

Hardle, W., Park, B.U.

demand ; expenditures ; household ; econometrics