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This paper measures latent fundamental exchange rates with independent component-based rates constructed from a cross-section of exchange rates and then uses their deviations from exchange rates to forecast. Empirical results indicate that the independent component-based model and its Taylor rule and purchasing power parity augmented models are superior to the random walk in predicting exchange rates. These results are robust to several scenarios and are likely to be observed if the U.S. sources and the recursive scheme are applied. Our results reveal that information regarding the third moment of exchange rate changes is helpful to explain exchange rate movements. [ABSTRACT FROM AUTHOR]

Time-varying specifications for the conditional variance of earnings of U.S. households are estimated with micro data over the period 1968-92. The cross-sectional mean of the estimated time-varying uncertainty of individual households has a significant impact on aggregate consumption growth. As such, aggregate precautionary savings may be more important than what is suggested by the results of estimating standard regression equations for aggregate consumption growth that incorporate only lagged income growth and the real interest rate. The estimation of a buffer stock consumption model with time-varying earnings uncertainty suggests that the precautionary savings motive is cyclical and has become less important in the 1980s. [ABSTRACT FROM AUTHOR]

This article reexamines the duration-based explanation of the value premium using novel estimates of the firms' equity and cash flow durations based on analyst forecasts. We show that the value premium can be explained by cross-sectional differences in the shares' equity durations, but not by their cash flow durations. Different from the duration-based explanation of the value premium that explains the value premium with cross-sectional differences in the firm's cash flow timing, we find that short-horizon stocks have lower (expected) returns than long-horizon stocks. This result is consistent with an upward-sloping equity yield curve. [ABSTRACT FROM AUTHOR]

Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional time series (PCSTS) since cross-sectional units (e.g., individual survey respondents) appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods-from pooled ordinary least squares to PCSTS to time series-force researchers to choose one level of analysis. The PCSTS tool kit does not provide an appropriate solution, and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed by the use of multilevel modeling to estimate both aggregate- and individual-level parameters simultaneously. We use Monte Carlo experiments and three applied examples to explore the advantages of our framework. [ABSTRACT FROM AUTHOR]

We explore empirically the theoretical prediction that public information acts as a focal point in the context of the U.S. monetary policy. We aim at establishing whether the publication of Federal Open Market Committee (FOMC) inflation forecasts affects the cross-sectional dispersion of private inflation expectations. Our main finding is that publishing FOMC inflation forecasts has a negative effect on the cross-sectional dispersion of private current-year inflation forecasts. This effect is found to be robust to another survey data set and to various macroeconomic controls. Moreover, we find that the dispersion of private inflation forecasts is not affected by the dispersion of views among FOMC members. [ABSTRACT FROM AUTHOR]

Standard present-value models suggest that exchange rates are driven by expected future fundamentals, implying that exchange rates contain information about future fundamentals. We test this key empirical prediction of present-value models in a sample of 35 currency pairs ranging from 1900 to 2009. Employing a variety of tests, we find that exchange rates have strong and significant predictive power for nominal fundamentals (inflation, money balances, nominal GDP), whereas predictability of real fundamentals and risk premia is much weaker and largely confined to the post-Bretton Woods era. Overall, we uncover ample evidence that future macrofundamentals drive current exchange rates. [ABSTRACT FROM AUTHOR]

I examine the timeliness of write-downs taken by U.S. financial institutions during the financial crisis of 2007-2008. The timeliness of write-downs is measured by benchmarking the quarterly accounting write-down schedule with the devaluation schedule implied by exposure-specific credit indices such as the ABX. The results show that the accounting write-downs are less timely than the devaluations implied by credit indices. In a cross-sectional analysis of the determinants of the timeliness of write-downs, I document that corporate governance quality, regulatory investigations, and litigation pressure are positively related to the timeliness of write-downs, whereas the write-downs by firms with higher financial leverage, tighter regulatory constraints, and more complex exposures are less timely. I control for numerous exposure-specific characteristics and document that less risky exposures and exposures that were affected later during the financial crisis were written down later. Regarding the consequences of timeliness, I find that the exposure to risky assets is reflected faster in stock returns for firms with timelier write-downs. [ABSTRACT FROM AUTHOR]

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation with three related developments. First, we build a multiple imputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms. [ABSTRACT FROM AUTHOR]