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This paper exploits information from the term structure of survey expectations to identify news shocks in a DSGE model with rational expectations. We estimate a structural business-cycle model with price and wage stickiness. We allow for both unanticipated and anticipated components (“news”) in each structural disturbance: neutral and investment-specific technology shocks, government spending shocks, risk premium, price and wage markup shocks, and monetary policy shocks. We show that the estimation of a standard DSGE model with realized data obfuscates the identification of news shocks and yields weakly or non-identified parameters pertaining to such shocks. The identification of news shocks greatly improves when we re-estimate the model using data on observed expectations regarding future output, consumption, investment, government spending, inflation, and interest rates - at horizons ranging from one-period to five-periods ahead. The news series thus obtained largely differ from their counterparts that are estimated using only data on realized variables. Moreover, the results suggest that the identified news shocks explain a sizable portion of aggregate fluctuations. News about investment-specific technology and risk premium shocks play the largest role, followed by news about labor supply (wage markup) and monetary policy.

What are the effects of beliefs, sentiment, and uncertainty, over the business cycle? To answer this question, we develop a behavioral New Keynesian macroeconomic model, in which we relax the assumption of rational expectations. Agents are, instead, boundedly rational: they have a finite-planning horizon, and they learn about the economy over time. Moreover, we allow agents to have a potentially asymmetric loss function in forecasting, which creates a direct channel for expected variances to affect the economy. In forming expectations, agents may be subject to shifts in optimism and pessimism (sentiment) and their beliefs may be influenced by their perceptions about future uncertainty. We estimate the behavioral model using Bayesian methods and exploit a large number of subjective expectation series (both point and density forecasts) at different horizons from the Survey of Professional Forecasters. We find that sentiment shocks are the key source of business cycle fluctuations. Shifts in perceived uncertainty can also affect real activity and inflation through a confidence channel, as they play an important role in belief formation. Overall, the results shed light on the importance of behavioral forces over the business cycles, and on the contribution and interaction of first-moment - sentiment - shocks versus second-moment - perceived uncertainty - shocks.

This paper studies the interaction between the housing sector and the macroeconomic environment in Korea. Besides serving as a typical small open economy, Korea provides an interesting laboratory case, since the country was subject to various housing-cycle phases over the sample, and its policymakers implemented active policies to stabilize the housing market. We present a microfounded open-economy model extended to incorporate a housing sector. The model is estimated using Bayesian methods and a large set of observables. We highlight the role of assumptions about house-price expectations on economic dynamics. In the benchmark case of rational expectations, the spillovers from housing to macro variables are modest. House prices are largely driven by sector-specific demand and supply shocks. Business cycle fluctuations are mostly driven by open-economy shocks. When the assumption of rational expectations is relaxed in favor of extrapolative expectations, however, the impact of the housing sector increases. Macroeconomic spillovers from the housing market become more important, with housing shocks accounting for up to twenty percent of fluctuations in the broader economy.

This paper tests the ability of New Keynesian models to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding expectations. We exploit survey expectations data and adopt a dynamic stochastic general equilibrium (DSGE)-VAR approach to assess the extent and sources of model misspecification. The results point to serious misspecification in the expectations-formation side of the DSGE model. The rational expectations hypothesis is primarily responsible for the model's failure to capture the co-movements between observed macroeconomic expectations and realizations. Alternative models of expectations formation help partially reconcile the New Keynesian model with the data.

This paper estimates a New Keynesian model extended to include heterogeneous expectations: consumers and firms form either rational or boundedly-rational expectations. The inclusion of heterogeneous expectations alters the determinacy properties of the model, with the details of expectations potentially becoming more influential than the Taylor principle for equilibrium stability. The model is estimated with Bayesian techniques, using rolling windows and allowing the parameters to fall both in the determinacy and indeterminacy regions. The estimates reveal large shares of agents who depart from rational expectations. Heterogeneous expectations are decisively preferred by the data everywhere in the sample. Finally, the paper revisits the narrative that sees postwar US macroeconomic data as consistent with indeterminacy in the pre-1979 sample, with a switch to determinacy starting in the early 1980s, and it shows that it is overall robust to inclusion of heterogeneous expectations.

Psychological factors, market sentiments, and shifts in beliefs are believed by many to play a nontrivial role in inducing and amplifying economic fluctuations. Yet, these forces are rarely considered in macroeconomic models. This paper provides an attempt to evaluate the empirical role of expectational shocks on business cycle fluctuations. The paper relaxes the conventional assumption of rational expectations to exploit observed data on survey and market expectations in the estimation of a benchmark New Keynesian model. The observed expectations are modeled as formed from a near-rational expectation formation mechanism, which assumes that economic agents use a linear perceived law of motion for economic variables that has the same structural form as the model solution under rational expectations and that they need to learn model coefficients over time. In addition to the typical structural demand, supply, and policy disturbances, the model incorporates expectation shocks, which affect the formation of expectations by the private sector. Both the best-fitting learning process and the expectations shocks are identified from the expectations data and from the interaction between expectations and realized data. The expectations shocks capture waves of optimism and pessimism that lead agents to form forecasts that deviate from those implied by their learning model and by the state of the economy. The empirical results uncover a crucial role for these novel expectations shocks as a major driving force of the U.S. business cycle. Expectation shocks regarding future real activity are the main source of economic fluctuations, since they can account for roughly half of business cycle fluctuations.

© 2014 Elsevier B.V. This paper studies the implications of globalization for the dynamics of macroeconomic variables over the business cycle for a small open trade-dependent economy, such as South Korea.We study the impact of globalization through the lens of a structural model. Globalization is modeled as a time-varying degree of openness in the economy. We estimate the model allowing for non-fully rational expectations, learning by economic agents, and incomplete international financial markets.The empirical results show that globalization led to important changes in the macroeconomic environment. Domestic variables have become much more sensitive toward global measures over the 1991-2012 sample. In particular, domestic output and inflation are significantly affected by global output. Fluctuations in Korean output, inflation, and interest rates, which were driven for the most part by domestic shocks in the early 1990s, are, by the end of the sample, due in large part (roughly 70%) to global shocks (and shocks that are open-economy in nature).

This paper tests the ability of popular New Keynesian models, which are traditionally used to study monetary policy and business cycles, to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding expectations. In the empirical analysis, we exploit direct data on expectations from surveys. To explain the joint evolution of realized variables and expectations, we adopt a DSGE-VAR approach, which allows us to estimate all models in the continuum between the extremes of an unrestricted VAR, on one side, and a DSGE model in which the cross-equation restrictions are dogmatically imposed, on the other side. Moreover, the DSGE-VAR approach allows us to assess the extent, as well as the main sources, of misspecification in the model. The paper's results illustrate the failure of New Keynesian models under the rational expectations hypothesis to account for the dynamic interactions between observed macroeconomic expectations and macroeconomic realizations. Confirming previous studies, DSGE restrictions prove valuable when the New Keynesian model is exempted from matching observed expectations. But when the model is required to match data on expectations, it can do so only by moving away, and hence substantially rejecting, DSGE restrictions. Finally, we investigate alternative models of expectations formation, including examples of extrapolative and heterogeneous expectations, and show that they can go some way toward reconciling the New Keynesian model with the data. Intermediate DSGE-VAR models, which avail themselves of DSGE prior restrictions, return to fit the data better than the unrestricted VAR. Hence, the results overall point to misspecification in the expectations formation side of the DSGE model, more than in the structural microfounded equations.

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations. This paper departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of rational expectations, rational expectations extended to allow for `news' about future shocks, near-rational expectations and learning, and observed subjective expectations from surveys. The results show that the econometric evaluation of the model is extremely sensitive to how expectations are modeled. The posterior distributions for the structural parameters significantly shift when the assumption of rational expectations is modified. Estimates of the structural disturbances under different expectation processes are often dissimilar. The modeling of expectations has important effects on the ability of the model to fit macroeconomic time series. The model achieves its worse fit under rational expectations. The introduction of news improves fit. The best-fitting specifications, however, are those that assume learning. Expectations also have large effects on forecasting. Survey expectations, news, and learning all work to improve the model's one-step-ahead forecasting accuracy. Rational expectations, however, dominate over longer horizons, such as one-year ahead or beyond.

A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods. A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations. I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations (RE). This chapter departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of RE, RE extended to allow for “news” about future shocks, near-RE and learning, and observed subjective expectations from surveys. The results show that the econometric evaluation of the model is extremely sensitive to how expectations are modeled. The posterior distributions for the structural parameters significantly shift when the assumption of RE is modified. Estimates of the structural disturbances under different expectation processes are often dissimilar. The modeling of expectations has important effects on the ability of the model to fit macroeconomic time series. The model achieves its worse fit under RE. The introduction of news improves fit. The best-fitting specifications, however, are those that assume learning. Expectations also have large effects on forecasting. Survey expectations, news, and learning all work to improve the model's one-step-ahead forecasting accuracy. RE, however, dominate over longer horizons, such as one-year ahead or beyond.

This paper surveys the treatment of expectations in estimated Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models. A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods. A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations. I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

This paper estimates a structural general equilibrium model to investigate the changing relationship between the oil price and macroeconomic variables. The oil price, through the role of oil in production and consumption, affects aggregate demand and supply in the model. The assumption of rational expectations is relaxed in favor of learning. Oil prices, therefore, affect the economy through an additional channel, i.e. through their effect on the formation of agents' beliefs. The estimated learning dynamics indicates that economic agents' perceptions about the effects of oil prices on the economy have changed over time: oil prices were perceived to have large effects on output and inflation in the 1970s, but only milder effects after the mid-1980s. Since expectations play a large role in the determination of output and inflation, the effects of oil price increases on expectations can magnify the response of macroeconomic variables to oil price shocks. In the estimated model, in fact, the implied responses of output and inflation to oil price shocks were much more pronounced in the 1970s than in 2008. Therefore, through the time variation in the impact of oil prices on beliefs, the paper can successfully explain the observed weakening of the effects of oil price shocks on real activity and inflation.

This paper estimates a DSGE model with learning to re-examine the evidence on time variation in post-war U.S. monetary policy. Several papers document a regime switch, by showing that policy changed from `passive' and destabilizing in the pre-1979 period to `active' and stabilizing in the following decades. These papers typically work with DSGE models with rational expectations. This paper relaxes the assumption of rational expectations and it allows for learning instead. Economic agents form expectations from simple models and update the parameters through constant-gain learning. I estimate the model by Bayesian methods. The constant gain coefficient is jointly estimated with the structural and policy parameters of the system. I find that the feedback coefficient to inflation was well above 1 also in the 1960s and 1970s and therefore policy was not leading to macroeconomic instability. The results reconcile the evidence from DSGE models with what obtained by time-varying VAR studies, which typically find only modest changes in policy coefficients over the post-war sample.

This paper presents an estimated model with learning and provides evidence that learning can improve the fit of popular monetary DSGE models and endogenously generate realistic levels of persistence. The paper starts with an agnostic view, developing a model that nests learning and some of the structural sources of persistence, such as habit formation in consumption and inflation indexation, that are typically needed in monetary models with rational expectations to match the persistence of macroeconomic variables. I estimate the model by likelihood-based Bayesian methods, which allow the estimation of the learning gain coefficient jointly with the `deep' parameters of the economy. The empirical results show that when learning replaces rational expectations, the estimated degrees of habits and indexation drop near zero. This finding suggests that persistence arises in the model economy mainly from expectations and learning. The posterior model probabilities show that the specification with learning fits significantly better than does the specification with rational expectations. Finally, if learning rather than mechanical sources of persistence provides a more appropriate representation of the economy, the implied optimal policy will be different. The policymaker will also incur substantial costs from misspecifying private expectations formation.

What generates persistence in inflation? Is inflation persistence structural? This paper investigates learning as a potential source of persistence in inflation. The paper focuses on the price-setting problem of firms and presents a model that nests structural sources of persistence (indexation) and learning. Indexation is typically necessary under rational expectations to match the inertia in the data and to improve the fit of estimated New Keynesian Phillips curves. The empirical results show that when learning replaces the assumption of fully rational expectations, structural sources of persistence in inflation, such as indexation, become unsupported by the data. The results suggest learning behavior as the main source of persistence in inflation. This finding has implications for the optimal monetary policy. The paper also shows how one's results can heavily depend on the assumed learning speed. The estimated persistence and the model fit, in fact, vary across the whole range of constant gain values. The paper derives the best-fitting constant gains in the sample and shows that the learning speed has substantially changed over time.