C1 - Econometric and Statistical Methods and Methodology: General
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A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data
This paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false. -
Evaluating Linear and Non-Linear Time-Varying Forecast-Combination Methods
This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach that allows maximum flexibility of the weighting parameters. -
Testing for a Structural Break in the Volatility of Real GDP Growth in Canada
This study tests for a structural break in the volatility of real GDP growth in Canada following the methodology of McConnell and Quiros (1998). A break is found in the first quarter of 1991. -
Exact Non-Parametric Tests for a Random Walk with Unknown Drift under Conditional Heteroscedasticity
This paper proposes a class of linear signed rank statistics to test for a random walk with unknown drift in the presence of arbitrary forms of conditional heteroscedasticity.