This study investigates whether energy prices and macroeconomic indicators are determinants of the European Union Allowance (EUA) prices and how their relationships changed in phase III of the European Union Emission Trading Schema (EU ETS). An innovative application of the partial least squares...

In the presence of fixed threshold effects, the least squares (LS) estimator of the threshold parameter poses challenges for statistical inference due to its non-standard limiting distribution, which also presents challenges for bootstrap methods. To address this issue, we propose a novel...

We consider fully modified least squares estimation for systems of cointegrating polynomial regressions, i. e., systems of regressions that include deterministic variables, integrated processes and their powers as regressors. The errors are allowed to be correlated across equations, over time...

We develop a methodology for estimating and testing the effect of anomalies in conditional asset pricing models when premia are time-varying. Our method, which builds on the two-pass methodology, is developed for ordinary and weighted least-squares estimation, considering both cases of correct...

The standard uncovered interest parity (UIP)—a logarithm version—is inappropriate for currencies with a high interest rate because the interest rate (i) does not satisfy the ln(1 + i) = i condition. Therefore, this study employs the precise form of UIP and evaluates its empirical validity...

In this paper, we propose the Fama-Macbeth (FM) regression with sparsity, which is named FM-LASSO. We impose a l2 penalty across time to make sure that the factors are sparse. The factors in our model are shrinked with consistency. If a factor is 0 once, then it is always 0. If a factor is not...

A correlation between regressors and disturbances presents challenging problems in linear regression. Issues like omitted variables, measurement error and simultaneity render ordinary least squares (OLS) biased and inconsistent. In the context of heteroscedastic linear regression, this note...

When a sample combines data from two or more groups, multivariate regression yields a matrix-weighted average of the group-specific coefficient vectors. However, it is possible that the weighted average of a specific coefficient falls outside the range of the group-specific coefficients, and it...