Ebook A Forecast Of The U.S. Business Cycle Using Three Different Approaches
Economists’ views of the business cycle change over time and approaches regarding the fluctuation of the business cycle have evolved. There is evidence that within the past ten to fifteen years there has been an evolution in the field of macroeconomic policy, or as Taylor (2000) states it, “a new normative macroeconomics.” During the period of the Great Depression, the Keynesian belief was prominent (Mankiw 1993). Towards the 1970s, the Nixon administration caused economists to have considerable interest in the “political business cycle theory” (Fair 1988), and with today’s recent study by Davies (2006), the economic shock and volatilities approach is on the rise.
The Keynesian approach asserts the importance of aggregate demand as the ultimate driving force in the economy. Mankiw (1993) writes that the Keynesian belief shows an emphasis on how shifts in aggregate demand cause economic fluctuations. The sum of consumer (C), investment (I), government (G) and net exports spending (X – M) are, according to the Keynesian approach, the catalysts that stimulate the fluctuations of the business cycle. Economists who employ a Keynesian approach also focus on consumer spending and saving as principal predictors of turning points (Johnson 2005). Mankiw (1989) states this in broader terms when he says, “Many of the macroeconomic disturbances that receive much attention among Keynesian macroeconomists will also have important effects in real (predictive) business cycle models.” According to Keynesian belief, macroeconomic disturbances are consumer, investment, and government spending along with net exports.
The political business cycle theory is an alternative approach to understand why business cycles change. Taylor (2000) states that there is a simple political need to be observed of doing something “positive” during a recession, which may include to “increase spending or to cut taxes to stimulate the economy in the short run, even though such stimulus would be inflationary in the long run.” It is in the politicians’ best interest to influence policies in order to stimulate the economy during times of election to win favor of the voters.
For example, the Nixon Administration deliberately aimed for a recession during the early part of its administration so that the economy would be on the upswing going into the subsequent election cycle (Nordhaus, 1975). Fair (1988) demonstrated a correlation between conditions in the economic environment and how the economic environment affects a particular party’s vote shares. Using the growth rate of real per capita GNP (somewhere between six months to a year before the election), Fair concluded that the incumbent party would receive a 1.02 percentage point increase in the vote share for every one-percentage point increase in the growth rate. Fair also concluded that the incumbent party would receive a 0.34 percentage point decrease in the vote share for every one-percentage point increase in the inflation rate. Fair’s model successfully predicted fifteen out of sixteen presidential elections through the years 1916 – 1978. Fair proves that the politicians in office manipulate monetary and fiscal policy in order to increase their vote shares during times of election.
Zuk and Woodbury (1986) investigated “whether U.S. defense spending has been systematically increased (or decreased) during national election years for the presumed purpose of influencing the economy in general and the electorate in particular.” Zuk and Woodbury, however, found that military spending is most likely not used as a policy instrument for winning elections. They did state, however, that their findings are based on a short time-series, therefore “the number of variables that can be meaningfully analyzed is fairly small,” and they left room for further investigation on the subject.
Recent research has constructed new inflation and real GDP forecast data from the ASA-NBER Survey of Professional Forecasters (Davies 2006). The traditional approach on shocks showed that in the short run, nominal shocks matter more, while in the long run, real shocks matter (Roberts 1993). Engle and Ng (1993) created a news impact curve based on the traditional approach showing negative shocks having more of an impact than positive shocks on the overall environment. The traditional approach on shocks was to regard any change in a variable as a shock. If inflation was three percent for a particular quarter and four percent in the following quarter, economists said there was a one percent shock. If, however, this one percent change in inflation was anticipated, there is no shock according to the new view. If inflation was anticipated to rise from three to six percent, but only rose three to four percent, then, according to the new view, there is a negative two percent shock in the environment. Similarly, volatility has traditionally been measured as the variance of a variable.
The volatility of inflation shocks has been measured as the variance of inflation over time. The approach is flawed because it assumes that “shock” equals a change in the variable which when using the new approach is incorrect. The Davies framework addresses this issue by using forecasts to measure shocks, or in other words, it assumes that “shock” equals changes in the forecast.
The intent of this analysis is to measure the extent to which new measures of economic shocks and volatilities can predict turning points in the business cycle, and to compare the performance of a model built on the new measures to models constructed based on the Keynesian and political approaches. In this paper, three different models forecast the expansion of business cycles: a Keynesian approach, a political approach, and a shock/volatility approach. The models represent the “evolution” of beliefs over time. The first model in my analysis, based on Keynesian theory, attempts to determine the extent to which spending and labor costs (the wages and salaries of all civilian workers) can predict real GDP changes. I also include the variables in the formula for generating aggregate demand: consumption, investment, government, and net export spending. I will include in my political model variables that represent monetary policy (CPI) and fiscal policy (tax rates) as a result of analyzing Taylor’s (2000) research. I will also include the variables of military spending as a percentage of real GDP and the percentage of Congressional seats held by Democrats and Republicans.
In comparison to these established models, I construct a model using new measures of shocks and volatility. This model is constructed based on the proposition that economic shocks affect business cycles, which means that they represent a potential change in peoples’ expectations of the future. As people alter their behaviors based on their new expectations, they can induce turning points in the business cycle. I employ Davies’ (2006) framework for decomposing the forecasts into implied shock and volatility data (see Davies) to predict the business cycle.
Contents
Introduction
Model
Results
Conclusion
References
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