Tracking jobs in clean industries in New England

Tracking jobs in clean industries—often called “green jobs”—is difficult because, unlike the high-technology sector, the clean-industries sector lacks a standard definition of which industries the sector actually comprises. This article explores four definitions of the sector: two defined by measures developed by analysts at highly respected institutions, and two defined by measures we created based on widely used databases. We use these definitions to analyze the composition and concentration of jobs in clean industries in New England and each state in the region and compare these figures with the national average. In doing so we show how the findings vary with the definition of the clean-industries sector.Industries - New England ; Environmental protection - New England

Currency risk and imperfect knowledge: Cointegrated VAR analyses with survey data

Much progress has been made in understanding excess returns in the foreign exchange market through the use of survey data on traders\u27 exchange rate forecasts. On the whole, this literature, which is reviewed in chapter 1, has found that excess returns derive from both violations of the rational expectations hypothesis (non white-noise forecast errors) as well as a time-varying risk premium. What this literature has not done however is to determine whether any of the existing models of the risk premium can account for the time-varying risk premium found in survey data. The second and third chapters use the Cointegrated VAR model to test the Capital Asset Pricing Model (CAPM), the Consumption CAPM, and the Keynes-Imperfect Knowledge Economics (IKE) gap model, which relate the risk premium to the exchange rate\u27s variance, covariance with consumption, and deviation from Purchasing Power Parity respectively. The strongest support is found for the Keynes-IKE gap model. The analysis of this model is then extended in chapter 4 to the I(2) CVAR framework, which is a unique empirical approach designed to account for data which undergoes persistent changes over time without the need for data transformations which cause a loss of information. The I(2) model also allows for more rigorous testing of the theory and a better examination of the dynamics between the exchange rate, expectations, prices, and interest rates. The Keynes-IKE gap model still performs quite well. Further, persistent changes are found for the real exchange rate in several instances, which is problematic for standard REH theory but fully compatible with the IKE theory

How Market Sentiment Drives Forecasts of Stock Returns

We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns