Measuring Potential Output and Output Gap and Macroeconomic Policy: The Case of Kenya

Measuring the level of an economy.s potential output and output gap are essential in identifying a sustainable non-inflationary growth and assessing appropriate macroeconomic policies. The estimation of potential output helps to determine the pace of sustainable growth while output gap estimates provide a key benchmark against which to assess inflationary or disinflationary pressures suggesting when to tighten or ease monetary policies. These measures also help to provide a gauge in the determining the structural fiscal position of the government. This paper attempts to measure Kenya.s potential output and output gap using alternative statistical techniques and structural methods. Estimation of potential output and output gap using these techniques shows varied results. The estimated potential output growth using different methods gave a range of .2.9 to 2.4 percent for 2000 and a range of .0.8 to 4.6 for 2001. Although various methods produce varied results, they however provided a broad consensus on the over-all trend and performance of the Kenyan economy. This study found that firstly, potential output growth is declining over the recent time and secondly, the Kenyan economy is contracting in the recent years.

Macroeconomic Rationality and Lucas' Misperceptions Model: Further Evidence from Forty-One Countries

Several researchers have examined Lucas's misperceptions model as well as various propositions derived from it within a cross-section empirical framework. The cross-section approach imposes a single monetary policy regime for the entire period. Our paper innovates on existing tests of those rational expectations propositions by allowing the simultaneous effect of monetary and short run aggregate supply (oil price) shocks on output behavior and the employment of advanced panel econometric techniques. Our empirical findings, for a sample of 41 countries over 1949 to 1999, provide evidence in favor of the majority of rational expectations propositions.

Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.