Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y|X; T) = G{XT Ø + » + m1(T1) + … + md(Td)}. Here G is a known (link) function, Ø is an unknown parameter, and m1, …, md are unknown functions. In particular, we consider additive binary response models...

Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y

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...

Semiparametric generalized additive models are a powerful tool in quantitative econometrics. With response Y , covariates X, T the model is E(Y | X; T) = G { X T β + α + m1(T1) + . . . + md(Td) }. Here, G is a known link, â, á are unknown parameters, and m1, . . . , md are unknown (smooth)...

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...