Full-information Rational Expectations Research Articles

(Mis)Alignment between observed and expected monetary policy: The case of Brazil

The economic agents’ perception of the monetary policy might not coincide with the central bank conduct. We investigate this issue by using both actual and expected data identified by professional forecasters for Brazil, an emerging economy that has long adopted the inflation-targeting regime. We estimate observed, expected, and forward-looking interest rate rules, apply Full-Information Rational Expectations (FIRE) tests, assess restrictions implied by models of information rigidity, and perform several robustness checks. The estimated policy rules respond to inflation deviations and output gap according to theoretical grounds. Professional forecasters, however, do not believe in aggressiveness to fight inflation in longer forecasting horizons, while FIRE tests unveil information rigidity in the short run. There is a misalignment between the Central Bank practice and the private agents’ perception of the monetary policy in the long but not in the short run, suggesting unanchored long-term inflation expectations.

Predictable Forecast Errors in Full-Information Rational Expectations Models with Regime Shifts

Predictable Forecast Errors in Full-Information Rational Expectations Models with Regime Shifts

Coherence without rationality at the zero lower bound

Standard rational expectations models with an occasionally binding zero lower bound constraint either admit no solutions (incoherence) or multiple solutions (incompleteness). This paper shows that deviations from full-information rational expectations mitigate concerns about incoherence and incompleteness. Models with no rational expectations equilibria admit self-confirming equilibria involving the use of simple mis-specified forecasting models. Completeness and coherence are restored if expectations are adaptive or if agents are less forward-looking due to some information or behavioral friction. In the case of incompleteness, the E-stability criterion selects an equilibrium.

Identifying the source of information rigidities in the expectations formation process

Coibion and Gorodnichenko (2015) provide a useful framework to test the null hypothesis of full-information rational expectations against two popular classes of information rigidities, sticky information (SI) and noisy information (NI). However, the observational equivalence of SI and NI in average forecast errors gives no power in the test for one against the other. We identify the source of information rigidities by estimating the equations for the average forecast errors and variance of forecasts. The results show the importance of both SI and NI, but favor a type of NI in which agents can quickly learn the underlying state.

The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations

We estimate a simple reduced-form model of expectation formation with three distinct deviations from full-information rational expectations that combines underreaction with overreaction to new information. In particular, forecasts are sticky, extrapolate the most recent news about the current period, and depend on the lagged consensus forecast about the period being forecast. We find that all three biases are present in the Survey of Professional Forecasters as well as in the Livingston Survey, and that their magnitudes depend on the forecasting horizon. We also stress the point that using the past consensus forecast to form expectations is a reasonable thing to do if a forecaster is not able to come up with full-information rational expectations all by herself. Finally, we show that forecasters that have more sticky expectations generally tend to rely less on the lagged consensus forecast but extrapolate news about the current period more.

Does inattentiveness matter for DSGE modeling? An empirical investigation

The purpose of this paper is to investigate the empirical performance of the standard New Keynesian dynamic stochastic general equilibrium (DSGE) model in its usual form with full-information rational expectations and compare it with versions assuming inattentiveness- namely sticky information and imperfect information data revision. Using a Bayesian estimation approach on US quarterly data (both real-time and survey) from 1969 to 2015, we find that the model with sticky information fits best and is the only one that can generate the delayed responses observed in the data. The imperfect information data revision model is improved fits better when survey data is used in place of real-time data, suggesting that it contains extra information. • The paper considers the full-information rational expectations with two other forms of inattentive expectations, namely sticky information and imperfect information data revision. • The paper extends empirical evidence using both real-time and survey data to verify which model with the inattentive feature best replicates actual outcomes. • This is the first empirical analysis to compare models with and without expectations inattentiveness. • The New Keynesian DSGE model with an inattentive feature is a better fit with the real-time data using Bayesian estimation. • The imperfect information data revision model performs better when we substitute the real-time data with the survey data.

Identifying the Source of Information Rigidities in the Expectations Formation Process

Coibion and Gorodnichenko (2015) provide a useful framework to test the null hypothesis of full-information rational expectations against two popular classes of information rigidities, sticky information (SI) and noisy information (NI). However, the observational equivalence of SI and NI in average forecast errors gives no power in the test for one against the other. We identify the source of information rigidities by estimating the equations for the average forecast errors and variance of forecasts. The results show the importance of both SI and NI, but favor a type of NI in which agents quickly learn the underlying state.

Predictable Forecast Errors in Full-Information Rational Expectations Models with Regime Shifts

Predictable Forecast Errors in Full-Information Rational Expectations Models with Regime Shifts

International business cycles: Information matters

We propose a mechanism that explains standard stylized facts in both international macroeconomics and international finance. To do so, we develop a New Keynesian DSGE model with financial frictions à la Bernanke et al. (1999), in which we depart from the full-information rational expectations (FIRE) assumption. The key ingredient is home information bias (HIB) in expectations. While the FIRE model predicts high consumption co-movements, no departure from uncovered interest parity (UIP) and procyclical trade balance, assuming HIB makes the model consistent with the data by producing low consumption correlation, solving the quantity puzzle, generating endogenous departures from the UIP and matching a countercyclical trade balance. The mechanism is empirically validated and shown to be robust to the extent of frictions in the economy.

Identifying the source of information rigidities in the expectations formation process

Coibion and Gorodnichenko (2015) provide a useful framework to test the null hypothesis of full-information rational expectations against two popular classes of information rigidities, sticky information (SI) and noisy information (NI). However, the observational equivalence of SI and NI in average forecast errors gives no power in the test for one against the other. We identify the source of information rigidities by estimating the equations for the average forecast errors and variance of forecasts. The results show the importance of both SI and NI, but favor a type of NI in which agents quickly learn the underlying state.

Do Firms’ Inflation Expectations Matter in Their Employment and Wages? A Causal Evidence

This paper investigates how the difference between firms' inflation expectations, measured by the loan amounts they demand, and actual inflation affects their employment. In addition, it examines the relationship between firms' inflation expectations and wages in an individualistic bargaining model. Theoretically, the model shows that a firm's actual labor demand meets its expected labor demand if the firm has a rational expectation regarding inflation. On the other hand, the firm's actual labor demand deviates from its expected labor demand if the firm cannot forecast inflation correctly. Empirically, using firm-level data from a province in Iran between 2004 and 2011, the model's predictions have been tested. The empirical results confirm that there is a specific loan amount for which firms' expected employments meet their actual ones. In contrast, there is a gap between firms' expected employments and their actual ones for any other loan amount. Furthermore, the result indicates that there is a positive and significant relationship between the loan amounts demanded by firms and wages. These results can be explained by firms' inflation expectations that show they are not consistent with full-information rational expectations models, though they are not far away from them.

Subjective Shadow Rate Beliefs at the Zero Lower Bound

We estimate shadow rates (Black (1995); Wu and Xia (2016)) using forward rates on US Treasuries and forecasts of short term interest rates from the Blue Chip Financial Survey. We estimate a suite of alternative models with different numbers of factors, with and without forecast data, and assuming forecasts are full information rational expectations or that forecasters have distorted beliefs. Regardless of how expectations formation is modeled, our estimates that incorporate forecasts imply: (1) deeper easing by the Federal Reserve during the ZLB period relative to previous estimates, (2) longer real-time forecasts of the duration of the ZLB period, and (3) a structural break in the effect of monetary policy on the macro-economy before and after the Great Recession. Our estimates also imply that the perceived path of policy was much tighter than the Fed’s official projected path, suggesting the FOMC’s statements were not perceived as fully credible during the Great Recession.

Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts

Using a novel dataset that contains qualitative firm survey data on sales forecasts as well as balance-sheet data on realized sales, we document that only major forecast errors are predictable and display autocorrelation. This result is a particular violation of the Full Information Rational Expectations hypothesis that requires explanation. In contrast, minor forecast errors are neither predictable nor autocorrelated. To arrive at this result, we develop a novel methodology to quantify qualitative survey data on firm forecasts. It is generally applicable when quantitative information, e.g. from balance sheets, is available on the realization of the forecasted variable. Finally, we provide a model of rational inattention that explains our empirical results. Firms optimally limit their degree of attention to information when operating in market environments where information processing is more costly. This results in major forecast errors that are predictable and autocorrelated.

Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts

Using a novel dataset that contains qualitative firm survey data on sales forecasts as well as balance-sheet data on realized sales, we document that only major forecast errors are predictable and display autocorrelation. This result is a particular violation of the Full Information Rational Expectations hypothesis that requires explanation. In contrast, minor forecast errors are neither predictable nor autocorrelated. To arrive at this result, we develop a novel methodology to quantify qualitative survey data on firm forecasts. It is generally applicable when quantitative information, e.g. from balance sheets, is available on the realization of the forecasted variable. Finally, we provide a model of rational inattention that explains our empirical results. Firms optimally limit their degree of attention to information when operating in market environments where information processing is more costly. This results in major forecast errors that are predictable and autocorrelated.

On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations

Establishing the external validity of experimental inflation forecasts is essential if laboratory experiments are to be used as decision-making tools for monetary policy. Our contribution is to document whether different measures of inflation expectations, based on various categories of agents (participants in experiments, households, industry forecasters, professional forecasters, financial market participants and central bankers), share common patterns. We do so by analyzing the forecasting performance of these different categories of data, their deviations from full information rational expectations, and the variables that enter the determination of these expectations. Overall, the different categories of forecasts exhibit common features: forecast errors are comparably large and autocorrelated, and forecast errors and forecast revisions are predictable from past information, suggesting the presence of some form of bounded rationality or information imperfections. Finally, lagged inflation positively affects the determination of inflation expectations. While experimental forecasts are relatively comparable to survey and financial market data, more heterogeneity is observed compared to central bank forecasts.

Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach

This paper studies a dynamic stochastic general equilibrium model with inattentive agents to explain the dynamic patterns of inflation forecast errors. Empirical findings suggest that inflation forecasts implied by the model are quite similar to survey expectations. This model dominates a full-information rational expectations model in its ability to account for why actual inflation is systematically associated with inflation expectations and survey forecast errors, and for generating excessively persistent forecast errors. Historical decomposition analysis reveals that supply shock and measurement errors in inflation are found to be dominant forces driving variations in inflation forecast errors, while secular shifts in inflation are generated mainly by supply and inflation target shocks.

Imperfect Information in Macroeconomics

This article presents some recent theoretical and empirical contributions to the macroeconomic literature that challenge the perfect information hypo-thesis. By taking into account the information frictions encountered by economic agents, it is possible to explain some of the empirical regularities that are difficult to rationalise in the standard framework of full information rational expectations. As an example, we discuss how the sign, size and persistence of the estimated effects of monetary and fiscal policies can change when the informational frictions experienced by economic agents are taken into account.

The Formation of Expectations, Inflation, and the Phillips Curve

This paper argues for a careful (re)consideration of the expectations formation process and a more systematic inclusion of real-time expectations through survey data in macroeconomic analyses. While the rational expectations revolution has allowed for great leaps in macroeconomic modeling, the surveyed empirical microevidence appears increasingly at odds with the full-information rational expectation assumption. We explore models of expectation formation that can potentially explain why and how survey data deviate from full-information rational expectations. Using the New Keynesian Phillips curve as an extensive case study, we demonstrate how incorporating survey data on inflation expectations can address a number of otherwise puzzling shortcomings that arise under the assumption of full-information rational expectations. (JEL D04, E24, E27, E31, E37)

Overreaction in Macroeconomic Expectations

We study the rationality of individual and consensus forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), who examine predictability of forecast errors from forecast revisions. We find that individual forecasters typically overreact to news, while consensus forecasts under-react relative to full-information rational expectations. We reconcile these findings within a diagnostic expectations version of a dispersed information learning model. Structural estimation indicates that departures from Bayesian updating in the form of diagnostic overreaction capture important variation in forecast biases across different series, yielding a belief distortion parameter similar to estimates obtained in other settings. (JEL C53, D83, D84, E13, E17, E27, E47)

On the External Validity of Experimental Inflation Forecasts: A Comparison with Five Categories of Field Expectations

Establishing the external validity of experimental inflation forecasts is essential if laboratory experiments are to be used as decision-making tools for monetary policy. Our contribution is to document whether different measures of inflation expectations, based on various categories of agents (participants in experiments, households, industry forecasters, professional forecasters, financial market participants and central bankers), share common patterns. We do so by analysing the forecasting performance of these different categories of data, their deviations from full information rational expectations, and the variables that enter the determination of these expectations. Overall, the different categories of forecasts exhibit common features: forecast errors are comparably large and autocorrelated, and forecast errors and forecast revisions are predictable from past information, suggesting the presence of some form of bounded rationality or information imperfections. Finally, lagged inflation positively affects the determination of inflation expectations. While experimental forecasts are relatively comparable to survey and financial market data, more heterogeneity is observed compared to central bank forecasts.