Frederick L. Joutz

Estimation of Short-Run and Long-Run Elasticities of Energy Demand From Panel Data Using Shrinkage Estimators

This paper discusses the problem of obtaining short-run and long-run elasticities of energy demand for each of forty-nine states in the United States using data for twenty-one years. Estimation using the time-series data by each state gave several wrong signs for the coefficients. Estimation using pooled data was not valid because the hypothesis of homogeneity of the coefficients was rejected. Shrinkage estimators gave more reasonable results. The paper presents, in a unified framework, the classical, empirical Bayes, and Bayes approaches for deriving these estimators. Coauthors are Robert P. Trost, Hongyi Li, and Frederick Joutz.

Short and long-run elasticities in US residential electricity demand: a co-integration approach

Abstract Co-integration techniques show promise in the analysis of short- and long-run effects of economic variables on energy use. We use these techniques to develop an error correction model of annual US residential electricity demand. We construct equipment stock indices and estimate the model for 1949–1993. Our analysis suggests a structural shift in consumption during the 1960s. We discuss reasons for this shift, report the short- and long-run elasticities, provide forecasts for 1994–1995, and compare the models forecasts with other published forecasts.

An evaluation of the predictions of the Federal Reserve

Abstract To successfully implement monetary policy, the Federal Reserve System (FED) must make forecasts about the future state of the economy. This paper examines some of the characteristics of these forecasts. The analysis presents the usual error measures and tests for rationality. The paper compares these predictions with those generated by ARIMA models and the ASA/NBER surveys. In addition, we analyze (1) the relationship between accuracy and the length of the forecast horizon, (2) whether accuracy has improved over time, and (3) the accuracy of the forecasts in the vicinity of turning points. We conclude that the FED predictions tended to yield the same type of errors that private forecasters have displayed: in some periods either real GNP or inflation had systematic errors; turning point errors occurred prior to recessions; the forecasts were unbiased, but showed evidence of inefficiency. However, the FED forecasts were not significantly different from the predictions of the ARIMA models or ASA/NBER surveys.

Relating the Knowledge Production Function to Total Factor Productivity: An Endogenous Growth Puzzle

The knowledge production function is central to RD the second captures a long-run positive relationship between TFP and the knowledge stock. The results indicate the presence of strong intertemporal knowledge spillovers and that the long-run impact of the knowledge stock on TFP is small. This evidence is interpreted in light of existing theoretical and empirical evidence on endogenous growth.

Factors affecting the yields on noninvestment grade bond indices: a cointegration analysis

Abstract This study examines the long- and short-run dynamics of the yields on noninvestment grade indices. Utilizing cointegration techniques, the traditional yield spread model is found to be inadequate. A revised model finds a long-run relationship between noninvestment grade yields, Treasury securities, and default rates. Error correction models are formulated to model the short-run dynamics of different segments of the market. These models include a long-run equilibrium (between yields, default rates, and Treasuries), mutual fund flows, minor bond ratings, debt subordination measures, a stock index, and a January effect. Segmentation in the noninvestment grade market is also demonstrated.

Can the Fed Predict the State of the Economy

Recent research has documented that the Federal Reserve produces systematic errors in forecasting inflation, real GDP growth, and the unemployment rate, even though these forecasts are unbiased. We show that these systematic errors reveal that the Fed is “surprised” by real and inflationary cycles. Using a modified Mincer-Zarnowitz regression, we show that the Fed knows the state of the economy for the current quarter, but cannot predict it one quarter ahead.

Economic growth, energy prices and technological innovation

Energy economists are repeatedly asked to analyze the short run and long run macroeconomic effects of energy price shocks. We examine the effect of energy prices in a dynamic model of economic growth as a function of capital, labor, energy prices, and technological innovation. Our approach extends earlier studies in two ways. First, we depart from the standard approach of modeling strictly in either levels or differences by using an error correction model. The advantage of this approach is that both short run and long run information is used in the model. Second, we construct a measure of technological innovation with patent filings. Typically, deterministic trends have been used to capture technological growth. The increase in energy efficiency since 1974 has been due to energy conservation and improvements in energy intensive capital stock and production processes. We try to simultaneously use energy prices and technological innovation in a model of macroeconomic growth. If energy price symmetry is imposed, we conclude that the short run energy price effects are approximately half the long run energy price effects. When the restriction is relaxed short run price symmetry does not hold. In particular, short run price increases are associated with economic downturns while short run price declines have no significant impact on economic activity. Also, the results show that technological innovation, measured through the stock of patents, contributes both directly to economic growth and indirectly through improvements in the economys capital stock. Section II briefly reviews empirical and theoretical research on energy and economic activity. The use of patents as measures of technological innovation and their relationship to macroeconomic growth are discussed in section III. In section IV we present a model of macroeconomic

Methods for identifying emerging General Purpose Technologies: a case study of nanotechnologies

Nanotechnology is an emerging field of science with the potential to generate new and enhance existing products and transform the production process. US patent data is used to track the emergence of nanotechnologies since 1978. The nanotechnologies that have undergone the most development are identified using patent citation data and co-citation patterns of patents are examined to define clusters of related nanotechnologies. The potential for economic impact of the emerging nanotechnologies is assessed using a generality index.

Exploring the link between oil prices and tanker rates

Given the secular and sharp rise in oil prices over the past decade, this study analyses the impact that the spike in oil prices has on tanker rates. We investigate a dynamic model explaining spot tanker rates. The magnitude of the impact of oil prices on the shipping industry, in terms of the level and volatility of spot (voyage) under bull and bear market conditions. The West African–US Gulf Tanker Rates, West Texas Intermediate spot and 3-month futures contract, and US Weekly Petroleum Inventories are analysed using cointegration and Granger causality analysis, from 1997 through 2007, in order to examine the lead–lag relationship between oil prices and tanker freight rates. Our findings show a relationship between spot and future crude oil prices, crude oil inventories and tanker rates. The significant increase of freight rates, and the simultaneous increase in oil prices, during the recent years, provides an intriguing economic environment to identify relationships between shipping market rates and oil prices. These relationships have significant implications for the markets. At the practical level, the better understanding of the relationship between freight rates and crude oil prices can improve operational management and budget planning decisions.

Biomass fuel supply: A methodology for determining marginal costs

Abstract Energy from biomass fuels is used in the electric utility, lumber and wood products, and paper and pulp industries. The objective of this paper is to discuss a method of constructing marginal cost curves for biomass fuel where there are multiple sources differing in availability. Most research has focused on the feasibility of a single source of fuel to supply a plant. A least-cost approach would consider wood fuel supplies from several sources. The methodology for determining the marginal cost of biomass fuel supply for a wood-fired electrical generating plant is described and an example is provided. Marginal cost curves allow decision-makers to ‘observe’ the effective cost to supply power plants. The methodology is useful because it frees feasibility studies from artificial one-source assumptions.