Forecast combinations, also known as ensemble models, routinely require practitioners to select a model from a massive number of potential candidates. Ten explanatory variables can be grouped into 21078 forecast combinations, and the number of possibilities increases further to 21078+21078 if we...

This paper summarizes and assesses several of the most popular methods to seasonally adjust weekly data. The industry standard approach, known as X-13ARIMA-SEATS, is suitable only for monthly or quarterly data. Given the increased availability and promise of non-traditional data at higher...

Chapter 1. Preliminaries -- Chapter 2. Topology -- Chapter 3. Metric Spaces -- Chapter 4. Normed Spaces -- Chapter 5. Sequences and Series -- Chapter 6. Differential Calculus of functions of one variable -- Chapter 7. Functions of several variables -- Chapter 8. Integral Calculus -- Chapter 9....

Multiple imputation technique is becoming a popular method for analyzing data with missing values. Several methods have been proposed for creating multiple imputations and most of these methods assume that the data are missing at random (MAR). However, limited diagnostic tools are available to...

This article shows that, under some common assumptions, the pointwise synthetic control estimator would be unbiased if the outcome variable follows an autoregressive process. However, if such autoregressive process is nonstationary, then the variance of the pointwise estimator would be sensitive...

Many social experiments are run in multiple waves, or are replications of earlier social experiments. In principle, the sampling design can be modified in later stages or replications to allow for more efficient estimation of causal effects. We consider the design of a two-stage experiment for...

We provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures...

This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent commonfactor structure....

We propose a new no-change benchmark to evaluate forecasts of series that are temporally aggregated. The new benchmark is the last high-frequency observation and reflects the null hypothesis that the underlying series, rather than the aggregated series, is unpredictable. Under the random walk...