C12 - Hypothesis Testing: General
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Testing Linear Factor Pricing Models with Large Cross-Sections: A Distribution-Free Approach
We develop a finite-sample procedure to test the beta-pricing representation of linear factor pricing models that is applicable even if the number of test assets is greater than the length of the time series. Our distribution-free framework leaves open the possibility of unknown forms of non-normalities, heteroskedasticity, time-varying correlations, and even outliers in the asset returns. -
A Consistent Test for Multivariate Conditional Distributions
We propose a new test for a multivariate parametric conditional distribution of a vector of variables yt given a conditional vector xt. -
Testing for Financial Contagion with Applications to the Canadian Banking System
The author proposes a new test for financial contagion based on a non-parametric measure of the cross-market correlation. The test does not depend on the assumption that the data are drawn from a given probability distribution; therefore, it allows for maximal flexibility in fitting into the data. -
Short-Run and Long-Run Causality between Monetary Policy Variables and Stock Prices
The authors examine simultaneously the causal links connecting monetary policy variables, real activity, and stock returns. -
Forecasting Canadian Time Series with the New Keynesian Model
The authors document the out-of-sample forecasting accuracy of the New Keynesian model for Canada. -
Testing the Parametric Specification of the Diffusion Function in a Diffusion Process
A new consistent test is proposed for the parametric specification of the diffusion function in a diffusion process without any restrictions on the functional form of the drift function. -
Exact Tests of Equal Forecast Accuracy with an Application to the Term Structure of Interest Rates
The author proposes a class of exact tests of the null hypothesis of exchangeable forecast errors and, hence, of the hypothesis of no difference in the unconditional accuracy of two competing forecasts. -
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. -
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.
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