E. Philip Howrey

Evaluating Alternative Monetary Policy Rules

This paper examines monetary policy from an optimal control perspective. Three loss functions are minimized for each of three models, and the results are compared. The three loss functions target nominal growth, real growth, and inflation, respectively. The three models are a small structural model, a VAR model, and a large structural model. A numerical procedure is presented that can handle a variety of loss functions and models.

Estimating the distributional impact of time-of-day pricing of electricity

Abstract We consider the general problem of recovering estimates of welfare measures such as willingness to pay, price indices, etc. from demand data with particular emphasis on the problem of unobserved taste variation across households. We model taste differences across households in a variance components framework and derive an iterative estimator for systems of demand equations with variance components which is relatively easy to compute. Finally we present an example of the methods we propose which involves time of day pricing of electricity. We are able to calculate the fraction of the population which would prefer such pricing policies to flat rate pricing.

Exchange rate forecasts with the Michigan quarterly econometric model of the US economy

Abstract Gandolfo et al. [ Journal of Banking and Finance 14 (1990) 965–992] have shown that their continuous time model of the Italian economy produces better ex post out-of-sample forecasts of the lira

A true time series and its indicators : an alternative approach

exchange rate than either existing structural or random-walk models. When the Michigan Quarterly Econometric Model is used, it is found that ex post out-of-sample forecasts of the trade-weighted value of the US dollar produced by the model are also superior to forecasts of a random-walk model. However, ex ante forecasts in which all the exogenous as well as the endogenous variables are forecast are less accurate than those produced by the random walk. The price of imported goods in foreign currency, an exogenous variable in both the Michigan and Italian econometric models, is the key variable in the Michigan model which explains the divergence of the ex ante and ex post forecasting results.

Using mixed frequency data to improve macroeconomic forecasts of inventory investment

Abstract de Leeuw and McKelvey proposed a method for using two imperfect indicators to predict the values of an unobserved series. In this article we suggest an alternative method that uses the multiple indicator approach to model the true time series and its indicators. We examine the identification status of the model, estimate the parameters by maximum likelihood, and use the Kalman filter to obtain predictions of the unobserved series. The method is applied to the U.S. gross private saving rate using flow of funds and national income and product accounts data as indicators.

An analysis of RSQE forecasts: 1971–1992

This paper briefly reviews and extends the evidence on the importance of inventory investment in business cycles. A method for combining high-frequency observations with forecasts of a conventional quarterly econometric model is then proposed. The method is applied to the Michigan Quarterly Econometric Model of the U.S. Economy to see if improved forecasts of inventory investment can be obtained. The use of a small set of monthly indicators is found to yield improved forecasts of real GNP but are of little help in forecasting inventory investment. A more comprehensive set of monthly indicators including inventory and sales may be needed to obtain improved estimates of quarterly inventory investment.

The Use of Preliminary Data in Economic Time-Series Prediction

The purpose of this paper is to evaluate the accuracy of ex ante econometric model forecasts of four key macroeconomic variables: real GNP growth, the rate of price inflation measured by the GNP deflator, the civilian unemployment rate, and the Treasury Bill rate. Annual forecasts produced by the Research Seminar in Quantitative Economics (RSQE) based on the Michigan Quarterly Econometric Model of the U.S. Economy are compared with quasi ex ante forecasts from a four-variable vector autoregressive (VAR) model. Statistical tests of the equality of forecast error variances as well as univariate and multivariate forecast encompassing-type tests are conducted. The forecast error variance comparisons indicate that for three of the four variables the RSQE forecasts are more accurate than the VAR forecasts and for one of the variables (real GNP growth) only slightly less accurate. The forecast encompassing-type tests indicate that the RSQE forecasts contain information not contained in the VAR forecasts and, conversely, that VAR forecasts contain information not included in the RSQE forecasts. The scope for improving RSQE forecasts by combining them with VAR forecasts is rather limited, however.

The Predictive Power of the Index of Consumer Sentiment

It is well known that ex ante economic forecasts rely on preliminary data which will subsequently be revised as more complete information becomes available. However, in the specification and estimation of economic forecasting models, the distinction between preliminary and revised data is typically ignored. This practice leads to the construction of models that are not specifically designed to use the provisional observations that are available at the time the forecasts are prepared. This traditional approach to economic forecasting results in needlessly large prediction errors which can be reduced by using a more appropriate forecast procedure.