We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially or geometrically). In other...

A low frequency factor model regression uses returns computed at a lower frequency than data available. An example is using monthly rather than daily returns to estimate the Capital Asset Pricing Model (CAPM). I show that when using overlapping observations to estimate low frequency factor model...

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

Confidence intervals in econometric time series regressions suffer from notorious coverage problems. This is especially true when the dependence in the data is noticeable and sample sizes are small to moderate, as is often the case in empirical studies. This paper suggests using the studentized...

If an economic relationship is superimposed by a linear time trend, the regression without detrending is misspecified. The estimators of such a regression do not converge to the true parameter values. First, the asymptotic limit arising from such misspecified regressions is characterized....

This paper extends previous work in Escribano and Jorda (1997) and introduces new LM specification procedures to choose between Logistic and Exponential Smooth Transition Regression (STR) Models. These procedures are simpler, consistent and more powerful than those previously available in the...

We provide an overview of recent empirical research on patterns of cross-country growth. The new empirical regularities considered differ from earlier ones, e.g., the well-known Kaldor stylized facts. The new research no longer makes production function accounting a central part of the analysis....

We propose Midastar models by combining the Mixed Data Sampling (MIDAS) and the threshold autoregression (TAR). The Midastar model of the first kind is designed for a low frequency target variable and a high frequency threshold variable. The proposed model can detect threshold effects...