The primary objective of this note is to revisit the two envelope problem and propose a simple resolution. It is argued that the paradox arises from the ambiguity associated with the money content $x of the chosen envelope. ...

We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively ...

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an “in-sample” period – purportedly utilized only for model specification/estimation – and a “post-sample” period, purportedly utilized (only at the end of the analysis) for model validation/testing purposes. This partitioning is usually infeasible, however, with samples of modest length – e.g., T ≤ 150 – as is common in both quarterly data sets and/or in monthly data sets where institutional arrangements vary over time, simply because there is in such cases insufficient data available to credibly accomplish both purposes separately. A cross-sample validation (CSV) testing procedure is proposed below which both eliminates the aforementioned a priori partitioning and which also substantially ameliorates this power versus credibility predicament – preserving most of the power of in-sample testing (by utilizing all of the sample data in the test), while also retaining most of the credibility of post-sample testing (by always basing model forecasts on data not utilized in estimating that particular model’s coefficients). Simulations show that the price paid, in terms of power relative to the in-sample Granger-causality F test, is manageable. An illustrative application is given, to a re-analysis of the Engel andWest [1] study of the causal relationship between macroeconomic fundamentals and the exchange rate; several of their conclusions are changed by our analysis....

The primary objective of this paper is to revisit Simpson's paradox using a statistical misspecification perspective. It is argued that the reversal of statistical associations is sometimes spurious, stemming from invalid ...

The Neyman and Scott (1948) model is widely used to demonstrate a serious weakness of the Maximum Likelihood (ML) method: it can give rise to inconsistent estimators. The primary objective of this paper is to revisit this ...

R. A. Fisher founded modern statistical inference in 1922 and identified its fundamental problems to be: specification, estimation and distribution. Since then the problem of statistical model specification has received ...

A linear array can detect a plane wave signai at a wrong bearing if the signal wavelengths are shorter than twice the distance between the closest adjacent sensors. This penomenon, called spatial aliasing, is most pronounced ...

Most array signal processing systems use delay-and-sum beamforming to estimate source bearings. This paper demonstrates the close relationship between beamforming and frequency-wavenumber spectrum analysis. The latter ...

Present value studies of asset market efficiency are controversial because they compare asset prices to unobserved discounted streams of future rents. As an alternative, if housing markets are efficient, then the price of ...

Previous property tax capitalization studies assume that families itemize, that they save in taxable assets, and that real interest income is taxed. However, many families do not itemize, many families invest in tax-deferred ...