The Impact of Training Intensity on Establishment Productivity

The empirical literature on productivity effects of continuing training is constantly increasing. However, the results on this subject differ widely. Explanations for this worrying diversity seem to lie in differences between countries, labor market institutions, and data generation on one hand, and in differences between the underlying estimation techniques on the other ( Bartel, 2000 ). This paper concentrates on the latter problem and shows how results vary with different estimation techniques.     

A proposed Procedure for Facilitating the Analysis of Lease-Purchase Decisions by Consumers

Advertising extolling the advantages of leasing an automobile is increasing, leading many consumers to ask whether leasing makes sense as an alternative to ownership. This paper sets forth a simple procedure which professional financial advisors and educators can use to help consumers make this decision. The procedure can be applied to any buy-lease decision. In what follows, key variables in the buy-lease decision are identified, and an example using actual data is presented to illustrate the procedure.     

Optimal Aggregation of Money Supply Forecasts: Accuracy,Profitability and Market Efficiency

We present a general procedure for aggregating expert forecasts which exploits regularities in the structure of information within the forecaster population. Specific information structures lead to aggregation methods which adjust for additive bias, differences in individual accuracy, and correlation among forecasts. As an application, we construct composite predictions of the weekly change in the money supply from forecasts made by twenty major securities dealers, for which high positive correlation is found to be a significant characteristic. Due to instability in the information structure, our methods cannot improve on the accuracy of a simple average in this case. However, they do capture information about the correlation among money supply forecasts which is not fully impounded in short-term interest rates. Forecasts from our models accurately predict the direction of price changes for Treasury bills and Treasury bill futures after a money supply announcement.     

Not All Related Party Transactions (RPTs) Are the Same: Ex Ante Versus Ex Post RPTs

Related party transactions (RPTs) are potential means for insiders to expropriate outside shareholders via self‐dealing. There are, however, possible benefits to these arrangements for outside shareholders. We find that the overall volume of disclosed RPTs is generally not significantly associated with shareholder wealth as measured by operating profitability or Tobin's Q. However, the results for total RPT volume obscure that ex ante RPTs, transactions that predate a counterparty becoming a related party, are innocuous at worst in terms of their association with operating profitability and significantly positively associated with Tobin's Q whereas ex post RPTs, transactions initiated after a counterparty becomes a related party, are significantly negatively associated with operating profitability. Ex post RPTs also result in significant share price declines when first disclosed and are associated with an increased likelihood that a firm will enter financial distress or deregister its securities. These results are consistent with ex post RPTs serving as means for insiders to expropriate outside shareholders.     

Robustness and U.S. Monetary Policy Experimentation

We study how a concern for robustness modifies a policymaker's incentive to experiment. A policymaker has a prior over two submodels of inflation‐unemployment dynamics. One submodel implies an exploitable trade‐off, the other does not. Bayes' law gives the policymaker an incentive to experiment. The policymaker fears that both submodels and his prior probability distribution over them are misspecified. We compute decision rules that are robust to misspecifications of each submodel and of the prior distribution over submodels. We compare robust rules to ones that Cogley, Colacito, and Sargent (2007) computed assuming that the models and the prior distribution are correctly specified. We explain how the policymaker's desires to protect against misspecifications of the submodels, on the one hand, and misspecifications of the prior over them, on the other, have different effects on the decision rule.