Endogenous business cycle propagation and the persistence problem: The role of labor-market frictions

Contrasting sharply with a recent trend in DSGE modeling, we propose a business cycle model where frictions and shocks are chosen with parsimony. The model emphasizes a few labor-market frictions and shocks to monetary policy and technology. The model, estimated from U.S. quarterly postwar data, accounts well for important differences in the serial correlation of the growth rates of aggregate quantities, the size of aggregate fluctuations and key comovements, including the correlation between hours and labor productivity. Despite its simplicity, the model offers an answer to the persistence problem (Chari et al., 2000) that does not rely on multiple frictions and adjustment lags or ad hoc backward-looking components. We conclude modern DSGE models need not embed large batteries of frictions and shocks to account for the salient features of postwar business cycles.     

Fiscal news and macroeconomic volatility

This paper analyzes the contribution of anticipated capital and labor tax shocks to business cycle volatility in an estimated New Keynesian business cycle model. While fiscal policy accounts for about 15% of output variance at business cycle frequencies, this mostly derives from anticipated government spending shocks. Tax shocks, both anticipated and unanticipated, contribute little to the fluctuations of real variables. However, anticipated capital tax shocks do explain a sizable part of inflation fluctuations, accounting for up to 12% of its variance. In line with earlier studies, news shocks in total account for about 50% of output variance. Further decomposing this news effect, we find permanent total factor productivity news shocks to be most important. When looking at the federal level instead of total government, the importance of anticipated tax and spending shocks significantly increases, suggesting that fiscal policy at the subnational level typically counteracts the effects of federal fiscal policy shocks.     

The (un)importance of unemployment fluctuations for the welfare cost of business cycles

This paper studies the cost of business cycles within a real business cycle model with search and matching frictions in the labor market. We endogenously link both the cyclical fluctuations and the mean level of unemployment to the aggregate business cycle risk. The key result of the paper is that business cycles are costly: fluctuations over the cycle induce a higher average unemployment rate since employment is nonlinear in the job-finding rate and the past unemployment rate. We show this analytically for a special case of the model. We then calibrate the model to U.S. data. For the calibrated model, too, business cycles cause higher average unemployment; the welfare cost of business cycles can easily be an order of magnitude larger than Lucas's (1987) estimate. The cost of business cycles is the higher the lower the value of nonemployment is, or, equivalently, the lower is the disutility of work. The ensuing cost of business cycles rises further when workers' skills depreciate during unemployment.     

Search in the product market and the real business cycle

Empirical evidence suggests that most firms operate in imperfectly competitive markets. We develop a search-matching model between wholesalers and retailers. Firms face search costs and form long-term relationships. Price bargain results in both wholesaler and retailer mark ups, which depend on firms' relative bargaining power. We simulate the general equilibrium model and explore the role of product market search frictions for business cycles. We conclude from the simulation exercise that incorporating product market search structure and shocks improve the standard real business cycle model to reproduce US business cycle fluctuations.     

EQUILIBRIUM BUSINESS CYCLES: THEORY AND EVIDENCE

ABSTRACT. This paper surveys equilibrium business cycles (EBC) theory, which has dominated the business cycle literature since the mid 1970s. It focuses primarily on the real business cycle (RBC) literature the origin of which is traced to the monetary equilibrium business cycle (MBC) model developed by Lucas (1975). RBC and MBC models are themselves related to a wider class of linear stochastic business cycle models which, following Frisch (1933), view the cycle as the result of the propagation, by the economic system, of a series of random shocks. The MBC approach highlighted the importance of monetary shocks but its failure to adequately explain observed fluctuations provided the impetus to the development of the RBC approach, which emphasises the importance of real shocks. This paper also appraises the empirical support for the RBC approach and finds it less than compelling. Given the failure of Keynesian, and equilibrium linear stochastic business cycle models to fully explain economic fluctuations, the Frischian approach to business cycle modelling is called into question. Developments to existing models, which may help to clarify our understanding of business cycle behaviour, are discussed with a view to setting out a research agenda for the 1990s and beyond.     

Keynesian impulses versus Solow residuals: identifying sources of business cycle fluctuations

We employ a neoclassical business‐cycle model to study two sources of business‐cycle fluctuations: marginal efficiency of investment shocks, and total factor productivity shocks. The parameters of the model are estimated using a Bayesian procedure that accommodates prior uncertainty about their magnitudes; from these estimates, posterior distributions of the two shocks are obtained. The postwar US experience suggests that both shocks are important in understanding fluctuations, but that total factor productivity shocks are primarily responsible for beginning and ending recessions. Copyright © 2000 John Wiley & Sons, Ltd.     

Costly external finance and labor market dynamics

We study the role of agency frictions and costly external finance in cyclical labor market dynamics, with a focus on how credit-market frictions may amplify aggregate TFP shocks. The main result is that aggregate TFP shocks lead to large fluctuations of labor market quantities if the model is calibrated to the empirically observed countercyclicality of the finance premium. A financial accelerator mechanism thus amplifies labor market fluctuations by rendering rigidity in real wage dynamics. In contrast, if the finance premium is procyclical, which the model can be parameterized to accommodate, amplification is absent, and labor-market fluctuations display the Shimer (2005) puzzle.     

Assessing the Empirical Relevance of Labour Frictions to Business Cycle Fluctuations

This paper describes a dynamic stochastic general equilibrium model augmented with labour frictions, namely: indivisible labour, predetermined employment and adjustment costs. This improves the fit to the data as shown by a higher log marginal likelihood and closer match to key business cycle statistics. The labour frictions introduced are relevant for model dynamics and economic policy: the effect of total factor productivity shocks on most macroeconomic variables is substantially mitigated; fiscal policy leads to a greater crowding out of private sector activity and monetary policy has a lower impact on output. Labour frictions also provide a better match to impulse response functions from vector autoregressive models.     

中国经济周期特征与太阳黑子均衡

本文在Benhabib、Farmer（1994）、Farmar、Guo（1994,1995）的不确定性均衡模型框架上,构建了一个考虑了信念偏差所代表的不确定因素和政府支出冲击的太阳黑子RBC模型来解释宏观经济的周期波动;并利用中国改革开放以来的经济数据,对该模型进行了实证检验。我们发现,该模型能够解释80%以上的中国经济波动特征;可以合理预测各宏观经济变量与产出之间的协动关系,合理预测各变量与劳动生产率之间的相关关系;对中国经济的解释力远远高于标准RBC模型,说明这一模型比较适合解释中国经济波动。另外,还发现信念冲击与政府支出冲击均是中国宏观经济波动的重要冲击来源,分别可以解释约30%、60%的经济波动,表明二者均是中国经济波动不可忽略的重要影响因素。我们的发现从不确定均衡和财政政策变动角度对宏观经济波动(比如,消费、就业、投资)的一些现象提供了合理解释。     

By force of demand: Explaining cyclical fluctuations of international trade and government spending

This paper explores the role of demand shocks, as an alternative to productivity shocks, in driving both domestic and international business cycles within the international real business cycle (IRBC) framework. In addition to those well-documented domestic business cycle fluctuations (e.g., the volatility and cyclicality of output, consumption, investment, labor hours, and labor productivity) and international business cycle properties (e.g., the countercyclical net export and the comovement puzzle), this paper focuses on two additional stylized facts in the industrialized countries: the procyclical trade openness (the GDP fraction of trade volume) and the countercyclical government size (the GDP fraction of government spending). Using a parsimonious dynamic stochastic general equilibrium model, we show that the model׳s predictions under productivity shocks are not consistent with these facts. Instead, a demand-shock-driven model replicates the above facts while matching other domestic and international business cycle properties. An estimated version of the model confirms the quantitatively important impacts of demand shocks.     

Introducing financial frictions and unemployment into a small open economy model

Which are the main frictions and the driving forces of business cycle dynamics in an open economy? To answer this question we extend the standard new Keynesian model in three dimensions: we incorporate financing frictions for capital, employment frictions for labor and extend the model into a small open economy setting. We estimate the model on Swedish data. Our main results are that (i) a financial shock is pivotal for explaining fluctuations in investment and GDP. (ii) The marginal efficiency of investment shock has negligible importance. (iii) The labor supply shock is unimportant in explaining GDP and no high frequency wage markup shock is needed.     

Financial intermediation and real estate prices impact on business cycles: A Bayesian analysis

How do financial intermediation and real estate prices impinge on the business cycle? I develop a two-sector stochastic general equilibrium model with financial intermediation and real estate collateral to assess the impact of financial conditions and land prices on aggregate fluctuations. I estimate the model with Bayesian methods using a novel data set that includes U.S. macro and financial variables during the period 1975–2010. The results from the estimated model show that financial conditions have a sizable effect on the variability of investment spending, while productivity shocks are the main source of consumption fluctuations. Specifically, on the macro side, (1) financial shocks explain about three quarters of investment spending variability and one third of the variance in hours worked. On the financial side, (2) financial shocks explain most of the variability in land prices, credit spread, and aggregate net worth of the financial sector. The model also accounts for observed unconditional moments of macro and financial variables. Our quantitative results are suggestive of the impact of diverse sources of financial instability, and as such relevant for macro prudential policy analysis.     

The implications of financial frictions and imperfect knowledge in the estimated DSGE model of the U.S. economy

In this paper, I study how alternative assumptions about expectation formation can modify the implications of financial frictions for the real economy. I incorporate a financial accelerator mechanism into a version of the Smets and Wouters (2007) DSGE framework and explore the properties of the model assuming, on the one hand, complete rationality of expectations and, alternatively, several learning algorithms that differ in terms of the information set used by agents to produce the forecasts. I show that the implications of the financial accelerator for the business cycle may vary depending on the approach to modeling the expectations. The results suggest that the learning scheme based on small forecasting functions is able to amplify the effects of financial frictions relative to the model with Rational Expectations. Specifically, I show that the dynamics of real variables under learning is driven to a significant extent by the time variation of agents’ beliefs about financial sector variables. During periods when agents perceive asset prices as being relatively more persistent, financial shocks lead to more pronounced macroeconomic outcomes. The amplification effect rises as financial frictions become more severe. At the same time, a learning specification in which agents use more information to generate predictions produces very different asset price and investment dynamics. In such a framework, learning cannot significantly alter the real effects of financial frictions implied by the Rational Expectations model.     

The role of credit supply shocks in pacific alliance countries: A TVP-VAR-SV approach

This paper analyzes the effect of loan supply shocks on the real economic activity of Pacific Alliance countries using a Time-Varying Parameter VAR with Stochastic Volatility (TVP-VAR-SV) model which is identified by sign restrictions. Two main results arise from the analysis. First, loan supply shocks have an important impact on real economic activity in all Pacific Alliance countries: about 1% in Colombia, Mexico, and Peru, and about 0.5% in Chile. Thus, its contribution to business cycle fluctuations is similar to that of aggregate supply shocks and aggregate demand shocks in both stability and slowdown periods. Second, the power of loan supply shocks to affect economic activity do not remain constant over the time and its evolution across periods is heterogeneous among all Pacific Alliance countries. The sensitivity analysis indicates that the results of the model are robust to different priors specifications and to multiple sets of sign restrictions.     

Nonlinear effects of government spending shocks in the USA: Evidence from state‐level data

This paper uses state‐level data to estimate the effect of government spending shocks during expansions and recessions. By employing a mixed‐frequency framework, we are able to include a long span of annual state‐level government spending data in our nonlinear quarterly panel VAR model. We find evidence that for the average state the fiscal multiplier is larger during recessions. However, there is substantial heterogeneity across the cross‐section. The degree of nonlinearity in the effect of spending shocks is larger in states that are subject to a higher degree of financial frictions. In contrast, states with a prevalence of manufacturing, mining and agricultural industries tend to have multipliers that are more similar across business cycle phases.     

Solving the multi-country real business cycle model using a Smolyak-collocation method

We describe a sparse-grid collocation method to compute recursive solutions of dynamic economies with a sizable number of state variables. We show how powerful this method can be in applications by computing the non-linear recursive solution of an international real business cycle model with a substantial number of countries, complete insurance markets and frictions that impede frictionless international capital flows. In this economy, the aggregate state vector includes the distribution of world capital across different countries as well as the exogenous country-specific technology shocks. We use the algorithm to efficiently solve models with up to 10 countries (i.e., up to 20 continuous-valued state variables).     

Econometric analysis of structural systems with permanent and transitory shocks

This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah [1989. The dynamic effects of aggregate demand and supply disturbances. The American Economic Review 79, 655–673], and shows that structural equations with known permanent shocks cannot contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a re-examination of the identification schemes used by Wickens and Motto [2001. Estimating shocks and impulse response functions. Journal of Applied Econometrics 16, 371–387], Shapiro and Watson [1988. Sources of business cycle fluctuations. NBER Macroeconomics Annual 3, 111–148], King et al. [1991. Stochastic trends and economic fluctuations. American Economic Review 81, 819–840], Gali [1992. How well does the ISLM model fit postwar US data? Quarterly Journal of Economics 107, 709–735; 1999. Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271] and Fisher [2006. The dynamic effects of neutral and investment-specific technology shocks. Journal of Political Economy 114, 413–451].     

Sentiment and the U.S. business cycle

Psychological factors are commonly believed to play a role on cyclical economic fluctuations, but they are typically omitted from state-of-the-art macroeconomic models.This paper introduces “sentiment” in a medium-scale DSGE model of the U.S. economy and tests the empirical contribution of sentiment shocks to business cycle fluctuations.The assumption of rational expectations is relaxed. The paper exploits, instead, observed data on expectations in the estimation. The observed expectations are assumed to be formed from a near-rational learning model. Agents are endowed with a perceived law of motion that resembles the model solution under rational expectations, but they lack knowledge about the solution’s reduced-form coefficients. They attempt to learn those coefficients over time using available time series at each point in the sample and updating their beliefs through constant-gain learning. In each period, however, they may form expectations that fall above or below those implied by the learning model. These deviations capture excesses of optimism and pessimism, which can be quite persistent and which are defined as sentiment in the model. Different sentiment shocks are identified in the empirical analysis: waves of undue optimism and pessimism may refer to expected future consumption, future investment, or future inflationary pressures.The results show that exogenous variations in sentiment are responsible for a sizable (above forty percent) portion of historical U.S. business cycle fluctuations. Sentiment shocks related to investment decisions, which evoke Keynes’ animal spirits, play the largest role. When the model is estimated imposing the rational expectations hypothesis, instead, the role of structural investment-specific and neutral technology shocks significantly expands to capture the omitted contribution of sentiment.     

Residential investment and house prices in a multi-sector monetary business cycle model

This paper develops a three-sector quantitative dynamic stochastic general equilibrium model to account for some of the salient business cycle properties concerning residential investment and house prices. We depart from the traditional Real Business Cycle setup by incorporating monetary frictions and credit market activities into the model economy. The model generates the high volatility of residential investment and hours worked in the house investment goods producing sector, as well as the procyclicality of house prices. The lead-lag pattern of house investment also roughly conforms with the data. We find that monetary policy and nominal interest rates play a special role in the determination of house prices. Money shocks generate remarkably volatile residential investment and house prices.     

PERMANENT AND TRANSITORY SHOCKS,AND THE UK BUSINESS CYCLE

In this paper the business cycle properties of UK data are investigated using a VAR technique. A Real Business Cycle (RBC) model is formulated. The model includes both permanent and transitory shocks to technology. The business cycle properties of the data and the model are investigated by deriving the expected changes over various forecast horizons from a VAR model. It is found, contrary to evidence in Rotemberg and Woodford (1996), that the model can account for many features of the data and that temporary shocks are pertinent in order to explain the business cycle moments. The main difference between theory and data is present in hours worked. © 1997 by John Wiley & Sons, Ltd.