This dissertation consists of two essays on forecasting real GDP growth and predicting recessions in the United States. In the first essay, we create a new indicator of economic activity based on a business cycle pattern, able to better forecast real output changes. The second essay utilizes the same indicator with the purpose of improving recession forecast. The accurate prediction of economic activity is valuable for the business community, policymakers, and the general public because better forecasts of GDP growth have the potential to improve economic conditions. In the first essay, we create a new indicator based on the correlation of residential and non-residential marginal product of capital (MPK) estimates and use it to improve forecasts of output growth. The correlation of residential and non-residential MPK is highly negative during recessions, while in expansions the same correlation is positive. For six out of seven expansions, the correlation of the two series becomes zero between one and three quarters before the subsequent recession. This cyclical behavior allows the use of a measure based on the correlation of the MPKs to create a better forecast of GDP growth. To this end, we compare the out-of-sample predictability of the model including the indicator against a benchmark model, and strongly reject the hypothesis of no out-of-sample predictability from the newly created indicator to GDP growth. We also provide evidence in favor of highly improved in-sample fit when the new indicator is included, and conclude that it Granger-causes GDP growth. The improvement in GDP growth forecasts is greater when an oil price measure is included in the models. The second paper employs a probit model for the US to describe the probability of an economic recession during the next five quarters, using the new indicator based on the correlation of residential and non-residential marginal product of capital. We find that in every one to three quarters prior to a recession, the correlation of the two series is not significantly different from zero, with the exception of the Great Recession. We show that models including the new measure improve both in-sample fit and out-of-sample performance when compared to nested baseline alternatives, giving accurate out-of-sample forecasts for the 1990-1991 recession. We also show that forecasts including the new indicator outperform those reported in the survey of professional forecasters, suggesting that other variables would not undo the contribution of the new indicator.
Economic Indicators Essay
Economic indicators are various layers of statistics that provide insight and information into how an economy is functioning. An economist might use economic indicators to paint a picture of current economic performance, or make future economic predictions. As a team, we will profile six economic indicators: Consumer Price Index, Capacity Utilization, Unemployment Rate, Producer Price Index, Interest Rate, and Inflation Rate. Historic charts for each indicator are included in our Power Point Presentation. As we move forward, we will use this information to help us better understand our selected business, the airline industry.
Consumer Price Index (CPI)
The Consumer Price Index is published by the Federal Department of Labor for the purpose of measuring the cost of living in the United States. The index measures the general level of prices in a fixed basket of goods that are typically purchased by consumers. It translates the price of goods such as food, beverages, housing, apparel, transportation, medical care, and entertainment from what they were in prior years to what the same goods or services would cost today. The Consumer Price Index allows anyone looking at it to determine how much money they need to spend today in order to live in the same lifestyle they lived in previous years. The yearly change in percentage in the value of the index is one way of measuring the annual inflation rate. When the Consumer Price Index rises, it is an indication of an inflationary environment and that consumers will be paying more for the same goods or services than they had paid in the past. Conversely, when the CPI falls consumers will be paying less for the same goods or services than they paid in the past. Although, the former rather than the later is more prevalent in the history of the CPI.
The performance of the airline industry can be measured in its overall ability to fill seats on flights, measured in available seat miles per month. The industry experienced steady growth through the 1990's until Sept. 2001. The airline industry increased the number of flights, and the size of planes used, particularly in higher traffic routes. In 1992 there were 45 billion available seat miles per month on domestic flights, which grew to 62 billion seat miles by 2001. After Sept. 11, 2001, the downturn in air traffic caused the available seat miles to drop to under 50 billion, a 20% decrease in a short time. By July of 2003, the available seat miles had increased back to 57 billion, and improved to 62 billion by July of 2004.
The other factor in this comparison is the number of unused seat miles, "the difference between available seat-miles and revenue passenger miles, are used as a measure of airline capacity utilization." (www.bts.gov/publications/transportation_indicators/October, 2002)
The number of unused seat miles has fluctuated between 15 and 20 billion per month during this...
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