Using research designs patterned after randomized experiments, many recent economic studies examine outcome measures for treatment groups and comparison groups that are not randomly assigned. By using variation in explanatory variables generated by changes in state laws, government draft mechanisms, or other means, these studies obtain variation that is readily examined and is plausibly exogenous. This paper describes the advantages of these studies and suggests how they can be improved. It also provides aids in judging the validity of inferences they draw. Design complications such as multiple treatment and comparison groups and multiple pre- or post-intervention observations are advocated.

Comparing Predictive Accuracy

We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample tests are proposed, evaluated, and illustrated.

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The objective of this paper is to provide a basic framework for researchers interested in reporting the results of their PLS analyses. Since the dominant paradigm in reporting Structural Equation Modeling results is covariance based, this paper begins by providing a discussion of key differences and rationale that researchers can use to support their use of PLS. This is followed by two examples from the discipline of Information Systems. The first consists of constructs with reflective indicators (mode A). This is followed up with a model that includes a construct with formative indicators (mode B).

How to Write Up and Report PLS Analyses

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Testing for nonlinearity in time series: the method of surrogate data

We describe a statistical approach for identifying nonlinearity in time series; in particular, we want to avoid claims of chaos when simpler models (such as linearly correlated noise) can explain the data. The method requires a careful statement of the null hypothesis which characterizes a candidate linear process, the generation of an ensemble of surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. We present algorithms for generating surrogate data under various null hypotheses, and we show the results of numerical experiments on artificial data using correlation dimension, Lyapunov exponent, and forecasting error as discriminating statistics. Finally, we consider a number of experimental time series -- including sunspots, electroencephalogram (EEG) signals, and fluid convection -- and evaluate the statistical significance of the evidence for nonlinear structure in each case. 56 refs., 8 figs.

/pdf/testing-for-nonlinearity-in-time-series-the-method-of-3dx4o87iwx.pdf

Testing for nonlinearity in time series: The method of surrogate data

Since the likelihood function corresponding to finite mixtures of normal distributions is unbounded, maximum likelihood estimation may break down in practice. The article introduces the “moment generating function estimator” defined as the estimator which minimizes the sum of squares of differences between the theoretical and sample moment generating functions. The consistency and asymptotic normality of the estimator are proved and its finite sample behavior is compared to that of the standard method of moments estimator by Monte Carlo experiments. The estimator is applied to the Hamermesh model of wage bargain determination.

Estimating Mixtures of Normal Distributions and Switching Regressions

Economists have long known that time scale matters, in that the structure of decisions as to the relevant time horizon, degree of time aggregation, strength of relationship, and even the relevant variables differ by time scale. Unfortunately, until recently it was difficult to decompose economic time series into orthogonal time-scale components except for the short and long run, in which the former is dominated by noise. This paper uses wavelets to produce an orthogonal decomposition of some economic variables by time scale over six different time scales. The relationship of interest is the permanent income hypothesis. We confirm that time-scale decomposition is very important for analyzing economic relationships and that a number of anomalies previously noted in the literature are explained by these means. The analysis indicates the importance of recognizing variations in phase between variables when investigating the economic relationships.

The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income

Economists have long known that timescale matters in that the structure of
 decisions as to the relevant time horizon, degree of time aggregation,
 strength of relationship, and even the relevant variables differ by timescale. Unfortunately, until recently it was difficult to decompose economic
 time series into orthogonal timescale components except for the short or
 long run in which the former is dominated by noise. Wavelets are used
 to produce an orthogonal decomposition of some economic variables by timescale over six different timescales. The relationship of interest is that
 between money and income, i.e., velocity. We confirm that timescale
 decomposition is very important for analyzing economic relationships. The
 analysis indicates the importance of recognizing variations in phase between
 variables when investigating the relationships between them and throws
 considerable light on the conflicting results that have been obtained in the
 literature using Granger causality tests.

Decomposition of economic relationships by timescale using wavelets

In this paper I review what insights we have gained about economic and financial relationships from the use of wavelets and speculate on what further insights we may gain in the future. Wavelets are treated as a â¬Slensâ¬? that enables the researcher to explore relationships that previously were unobservable.

Wavelets in Economics and Finance: Past and Future

In this paper I review what insights we have gained about economic and financial relationships from the use of wavelets and speculate on what further insights we may gain in the future. Wavelets are treated as a 'lens' that enables the researcher to explore relationships that previously were unobservable.

/pdf/wavelets-in-economics-and-finance-past-and-future-191pk41ply.pdf

James B. Ramsey

Papers

Estimating Mixtures of Normal Distributions and Switching Regressions

The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income

Decomposition of economic relationships by timescale using wavelets

Wavelets in Economics and Finance: Past and Future

Wavelets in Economics and Finance: Past and Future