Expectation Shocks and Business Cycles
ABSTRACT
I study a smorgasbord of different expectation shocks in two kinds of macroeconomic models. As a baseline, I use a simple aggregate demand and supply framework with adaptive expectations. I present impulse response results for exogenous, temporary expectation shocks lasting for one period only or four periods, expectation shock with output gap-centered Taylor rule as opposed to inflation targeting and permanent exogenous shocks (long-run shock) to expectations. Later, I extend my results using a New Keynesian model, allowing for a richer analysis. In this New Keynesian setting, I study the impact of anticipated and unanticipated preference shocks with backward- and forward-looking expectations. My results indicate the centrality of the expectation formation process in driving the shock reactions and propagation 1. Policymakers in Pakistan should design policies which manoeuvre market sentiments more effectively through press releases and frequent information sharing with the market to make business cycle fluctuations more docile.
MOTIVATION
There is a large and growing literature in macroeconomics which attributes business cycle fluctuations to expectations, especially considering the Great Recession, which did not seem to have been driven by highly unfavourable fundamentals. Many economists now recognise an enlarged role for beliefs in the narrative of business cycles (see, for example, Kozlowski, et al. 2019; Gennaioli and Shleifer, 2020). Classic studies such as those of Minsky (1977); Kindleberger (1978) and more recently, Reinhart and Rogoff (2009) argue that the failure of investors to assess risks accurately is a common thread of many of these episodes. Meanwhile, Rajan (2006) and Taleb (2007) stressed the dangers of low probability risks to financial stability due to subprime mortgages.
For instance, in October 2017, the University of Chicago surveyed a panel of leading economists in the United States and Europe on the importance of various factors contributing to the 2008 Global Financial Crisis. According to the panellists, the number one contributing factor was the “flawed financial sector” in terms of regulation and supervision. Meanwhile, the number two factor among the twelve considered ranking just below the first in estimated importance was an underestimation of risks from financial engineering. The experts seem to agree that the fragility of a highly leveraged financial system exposed to significant housing risk was not fully appreciated in the period leading to the crisis. Many economists increasingly recognise that the Lehman bankruptcy and the fire sales during 2008 revealed that investors and policymakers learned that the financial system was more fragile and interdependent than they previously thought Gennaioli and Shleifer (2020).
If the output over-expansion is fueled by excessive credit growth, as suggested by recent historical evidence Schularick and Taylor (2012); Mian, et al. (2017) (footnote 2), then the eventual recognition of tail risks and overheating in financial markets paves the way for a Minsky Moment Minsky (1977). For instance, Bordalo, et al. (2018) build a micro-founded and behavioural model of expectations called diagnostic expectations and credit cycles in which beliefs overreact to incoming news because of the representative heuristic. This phenomenon creates excessive optimism when credit spreads are low during booms. In contrast, it exaggerates subsequent reversal when good news inflow slows down, leading to endogenous cycles in the absence of change in fundamentals, endogenously engendering a recession.
Much of this work indicates that there are errors in expectations throughout the the business cycle, leading to the trend of data collection by various global central banks, such as the Federal Reserve in the USA, and even the State Bank of Pakistan (SBP), on expectations through survey data. Increasingly, such data is considered a valid and beneficial source of information for economic research. We have learned that expectations in financial markets tend to be extrapolative rather than rational, and this essential feature needs to be integrated into economic analysis.
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