Specifying the Forecast Generating Process for Exchange Rate Survey Forecasts

This paper contributes to the literature on the modeling of survey forecasts using learning variables. We use individual industry data on yen-dollar exchange rate predictions at the two week, three month, and six month horizons supplied by the Japan Center for International Finance. Compared to earlier studies, our focus is not on testing a single type of learning model, whether univariate or mixed, but on searching over many types of learning models to determine if any are congruent. In addition to including the standard expectational variables (adaptive, extrapolative, and regressive), we also include a set of interactive variables which allow for lagged dependence of one industry’s forecast on the others. Our search produces a remarkably small number of congruent specifications-even when we allow for 1) a flexible lag specification, 2) endogenous break points and 3) an expansion of the initial list of regressors to include lagged dependent variables and use a General-to-Specific modeling strategy. We conclude that, regardless of forecasters’ ability to produce rational forecasts, they are not only “different,” but different in ways that cannot be adequately represented by learning models.Learning Models, Exchange Rate, Survey Forecasts

The Rationality and Heterogeneity of Survey Forecasts of the Yen-Dollar Exchange Rate: A Reexamination

This paper examines the rationality and diversity of industry-level forecasts of the yen-dollar exchange rate collected by the Japan Center for International Finance. In several ways we update and extend the seminal work by Ito (1990). We compare three specifications for testing rationality: the ”conventional” bivariate regression, the univariate regression of a forecast error on a constant and other information set variables, and an error correction model (ECM). We find that the bivariate specification, while producing consistent estimates, suffers from two defects: first, the conventional restrictions are suffcient but not necessary for unbiasedness; second, the test has low power. However, before we can apply the univariate specification, we must conduct pretests for the stationarity of the forecast error. We find a unit root in the six-month horizon forecast error for all groups, thereby rejecting unbiasedness and weak effciency at the pretest stage. For the other two horizons, we find much evidence in favor of unbiasedness but not weak effciency. Our ECM rejects unbiasedness for all forecasters at all horizons. We conjecture that these results, too, occur because the restrictions test suffciency, not necessity. In our systems estimation and micro- homogeneity testing, we use an innovative GMM technique (Bonham and Cohen (2001)) that allows for forecaster cross-correlation due to the existence of common shocks and/or herd e ects. Tests of micro-homogeneity uniformly reject the hypothesis that forecasters across the four industries exhibit similar rationality characteristics.Rational Expectations, Heterogeneity, Exchange Rate, Survey Forecast

Militant Morality: Civil Disobedience and Bioethics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90558/1/3561983.pd

To Aggregate, Pool, or Neither: Testing the Rational Expectations Hypothesis Using Survey Data

It is well known that even if all forecasters are rational, estimated coefficients in unbiasedness regressions using consensus forecasts are inconsistent because forecasters have private information. However, if all forecasters face a common realization, pooled estimators are also inconsistent. In contrast, we show that when predictions and realizations are integrated and cointegrated, micro-homogeneity ensures that consensus and pooled estimators are consistent. Therefore, contrary to claims in the literature, in the absence of micro-homogeneity, pooling is not a solution to the aggregation problem. We reject micro-homogeneity for a number of forecasts from the Survey of Professional Forecasters. Therefore, for these variables unbiasedness can only be tested at the individual level.Rational Expectations, Micro-homogeneity, Heterogeneity Bias, Aggregation Bias, Survey Forecasts

Testing the Rational Expectations Hypothesis using Survey Data

Because of the importance of inflation expectations, Lloyd B. Thomas Jr. (Fall 1999, p. 125-44) reexamines "the evidence on the nature and performance of various measures of expected inflation, with special attention given to the issue of rationality" (p. 126). Thomas tests the unbiasedness hypothesis using the Livingston and Michigan survey forecasts for the 1960 to 1997 time period and is unable to reject the null hypothesis of unbiasedness. Unfortunately, two types of problems due to aggregation plague such tests: private information bias and micro-heterogeneity bias. Therefore, for these survey forecasts, consensus regressions should generally not be used to test rationality; rationality can only be tested at the individual level.

Preference by Race in University Admissions and the Quest for Diversity

At issue is race preference, not affirmative action

The Michigan Civil Rights Initiative and the Civil Rights Act of 1964

The underlying principle of the Michigan Civil Rights Initiative (MCRI), adopted by state wide vote on 7 November 2006, is identical to that of the Civil Rights Act of 1964. Section 601 of the Civil Rights Act provides: “No person in the United States shall, on the ground of race, color, or national origin, be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving Federal financial assistance.” The recent passage of the MCRI results now in the inclusion [in Article 1, Section 26 of the Michigan constitution] of section (2), which provides: “The state shall not discriminate against, or grant preferential treatment to any individual or group on the basis of race, sex, color, ethnicity or national origin in the operation of public employment, public education or public contracting.