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This paper uses adaptive learning to understand the heterogeneity of individual-level expectations. We exploit individual Survey of Professional Forecasters data on output and inflation forecasts. We endow all forecasters with the same information set that they would have as economic agents in a benchmark New Keynesian model. Forecasters are, however, allowed to differ in the constant gain values that they use to update their beliefs and in their sentiments. The latter are defined as the degrees of excess optimism or pessimism about the economy that cannot be justified by the learning model. Our results highlight the heterogeneity in the gain coefficients adopted by forecasters. The median values of the gain coefficients occasionally jump to higher values in the 1970-80s, and stabilize in the 1990s and 2000s. Individual sentiment is also persistent and heterogeneous. Differences in sentiment, however, do not simply cancel out in the aggregate: the majority of forecasters exhibit excess optimism, or excess pessimism, at the same time.

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.

The COVID-19 pandemic underscored the importance of countries’ interconnections in understanding and reacting to the spread of the virus.This paper uses a global model with a sample of 41 countries to study the interdependencies between COVID-19 health shocks, populations’ risk perceptions about the disease, and their social distancing responses; it also provides some early evidence about potential economic effects.Social networks are a central component in understanding the international transmission.We exploit a dataset on existing social connections across country borders, made available by Facebook, and show that social networks help explain not only the spread of the disease, but also cross-country spillovers in risk perceptions and in social behavior. Social distancing responses across countries are measured based on aggregated mobility tracking indicators, obtained from Google Mobility Reports.We estimate a Global VAR (GVAR) model, which allows for endogeneity of each health, social, and economic, domestic variable, and for a dependence of domestic variables on country-specific foreign aggregates, which depend in turn on the matrix of social connections.Our empirical results highlight the importance of cross-country interdependencies and social networks. Risk perceptions and social responses are affected by experiences abroad, with Italy and the U.S. playing large roles in our sample. The social distancing responses to domestic health shocks are heterogeneous across countries, but they share some similarities: they adjust only gradually and with delay, hence displaying adaptive behavior.Early indicators are suggestive of unemployment consequences that vary widely across countries, depending on their labor market characteristics. Unemployment is particularly responsive to health shocks in the U.S. and Spain, while the fluctuations are attenuated almost everywhere else.

This paper estimates a Behavioral New Keynesian model to revisit the evidence that passive US monetary policy in the pre-1979 sample led to indeterminate equilibria and sunspot-driven fluctuations, while active policy after 1982, by satisfying the Taylor principle, was instrumental in restoring macroeconomic stability. The model assumes “cognitive discounting”, i.e., consumers and firms pay less attention to variables further into the future. We estimate the model allowing for both determinacy and indeterminacy. The empirical results show that determinacy is preferred both before and after 1979. Even if monetary policy is found to react only mildly to inflation pre-Volcker, the substantial degrees of bounded rationality that we estimate prevent the economy from falling into indeterminacy.

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.

This paper presents a DSGE model in which agents׳ learning about the economy can endogenously generate time-varying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors. The model is estimated using likelihood-based Bayesian methods. The endogenous gain is jointly estimated with the structural parameters of the system. The estimation results show that private agents appear to have often switched to constant-gain learning, with a high constant gain, during most of the 1970s and until the early 1980s, while reverting to a decreasing gain later on. As a result, the model can generate a pattern of volatility, which is increasing in the 1970s and falling in the second half of the sample, with a decline that can roughly match the magnitude of the so-called “Great Moderation” in the 1984–2007 period. The paper also documents how a failure to incorporate learning into the estimation may lead econometricians to spuriously find time-varying volatility in the exogenous shocks, even when these have constant variance by construction.

There is substantial agreement in the monetary policy literature over the effects of exogenous monetary policy shocks. The shocks that are investigated, however, almost exclusively represent unanticipated changes in policy, which surprise the private sector and which are typically found to have a delayed and sluggish effect on output. In this paper, we estimate a New Keynesian model that incorporates news about future policies to try to disentangle the anticipated and unanticipated components of policy shocks. The paper shows that the conventional estimates confound two distinct effects on output: an effect due to unanticipated or ìsurpriseî shocks, which is smaller and more short-lived than the response usually obtained in the literature, and a large, delayed, and persistent effect due to anticipated policy shocks or "news." News shocks play a larger role in influencing the business cycle than unanticipated policy shocks, although the overall fraction of economic fluctuations that can be attributed to monetary policy remains limited.

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.

Background: The debate on US healthcare reform has largely focused on the introduction of a public health plan option. While supporters stress various beneficial effects that would arise from increased competition in the health insurance market, opponents often contend that a public plan would drive insurers out of the market and potentially lead to the 'collapse' of the private health insurance industry. Objectives: To contribute to the US healthcare reform debate by inferring, from financial market data, the effect that the public option is likely to have on the private health insurance market. Methods: The study utilized daily data on the price of a security that was traded in a prediction market from June 2009 and whose pay-off was tied to the event that a federal government-run healthcare plan - the 'public option' - would be approved by 31 December 2009 (100 daily observations). These data were combined with data on stock returns of health insurance companies (1500 observations from 100 trading days and 15 companies) to evaluate the expected effect of the public option on private health insurers. The impact on hospital companies (1000 observations) was also estimated. Results: The results suggested that daily stock returns of health insurance companies significantly responded to the changing probability regarding the public option. A 10% increase in the probability that the public option would pass, on average, reduced the stock returns of health insurance companies by 1.28% (p < 0.001). Hospital company stock returns were also affected (0.9% reduction; p < 0.001). Conclusions: The results reveal the market expectation of a negative effect of the public option on the value of health insurance companies. The magnitude of the effect suggests a downward adjustment in the expected profits of health insurers of around 13%, but it does not support more calamitous scenarios.

This article aims to study the determinants of macroeconomic inertia in the euro area. To this end, it estimates a simple monetary DSGE model with private-sector learning, but which also includes more structural sources of inertia, such as habit formation in consumption and inflation indexation. Economic agents are assumed to form near-rational expectations and to learn the model parameters over time. Likelihood-based Bayesian methods are used to estimate the agents' beliefs jointly within the system and to provide evidence on the fit of alternative learning rules. The results show that European macroeconomic inertia has only moderately changed over the sample. The evidence is consistent with a small gain coefficient and low degrees of habits and indexation, although some uncertainty remains after the estimation.

The paper by An and Schorfheide reviews an important body of literature that takes the empirical implications of Dynamic Stochastic General Equilibrium (DSGE) models seriously. Often, in the past, ...

© 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).

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.

The Intentional Electromagnetic Interference (IEMI) threat to critical infrastructure containing electronic systems presents a formidable compendium of problems. In this paper the IDS (Ingegneria Dei Sistemi1) modeling approach to the IEMI problem is described. The aim is to present a working philosophy to effectively address the IEMI issues. The approach is based on the following key tenets: a) a topological approach, b) a scaled working policy, c) multi-method tools (i.e. combining simple, asymptotic, full-wave, statistical and hybrid methods), d) “high-fidelity” modeling of multi-scale structures, and e) special models for solving particularly complex problems (e.g. field penetration into oversized shelters and field-to-cable coupling in electrically large but reverberating enclosures). Specific results relevant to high-fidelity modeling of real-life multi-scale structures, obtained through a special implementation of a “Multi-Level Fast Multipole Method Multi Resolution” (MLFMA-MR) code will be presented. A significant part of the content of this contribution is related to the activities performed in the frame of the European Union FP7 program “STRUCTURE” (http://www.structures-project.eu/), relevant to the theme “Critical Infrastructure (CI) Protection against Intentional Electromagnetic threats (IEMI)”. “STRUCTURES” program which will be also summarized in the following, together with a review of IEMI.

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 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.

Recent research has suggested that globalization may have transformed the U.S. Phillips curve by making inflation a function of global, rather than domestic, economic activity. This paper tests this view by estimating a structural model for the U.S., which incorporates a role of global output on the domestic demand and supply relations and on the formation of expectations. Expectations are modeled as near-rational and economic agents are allowed to learn about the economy's coefficients over time. The estimation reveals small and negative coefficients for the sensitivity of inflation to global output; moreover, the fit of the model improves when global output is excluded from the Phillips curve. Therefore, the evidence does not support altering the traditional closed-economy Phillips curve to include global output. The data suggest, instead, that global output may play an indirect role through the determination of domestic output. But the overall impact of global economic conditions on U.S. inflation remains negligible.

Recent papers have argued that one implication of globalization is that domestic inflation rates may have now become more a function of ``global", rather than domestic, economic conditions, as postulated by closed-economy Phillips curves. This paper aims to assess the empirical importance of global output in determining domestic inflation rates by estimating a structural model for a sample of G-7 economies. The model can capture the potential effects of global output fluctuations on both the aggregate supply and the aggregate demand relations in the economy and it is estimated using full-information Bayesian methods. The empirical results reveal a significant effect of global output on aggregate demand in most countries. Through this channel, global economic conditions can indirectly affect inflation. The results, instead, do not seem to provide evidence in favor of altering domestic Phillips curves to include global slack as an additional driving variable for inflation.

This paper employs a structural model to estimate whether global output gap has become an important determinant of U.S. inflation dynamics. The results provide support for the relevance of global slack as a determinant of U.S. inflation after 1985. The role of domestic output gap, instead, seems to have diminished over time.

"I know there's the myth of the autonomous Fed ..." Nixon barked a quick laugh. --Richard Nixon, talking to Arthur Burns on October 23, 1969, just after Burns's nomination to the Fed had been announced. (1) "I'd like to see another lowering of interest rates. I think there's room to do that. I can understand people worrying about inflation. But I don't think that's the big problem now." --George H. W. Bush, interview with New York Times, June 24, 1992. I. INTRODUCTION Economic conditions before elections affect election outcomes. (2) Rational politicians who recognize this regularity may, therefore, be tempted to try to influence the economy in the quarters preceding an election date to maximize their chances of being reelected. The literature on political business cycles (PBC) has developed models that rationalize economic fluctuations induced by political cycles. Nordhaus (1975) presented a model of "opportunistic" political cycles: the party in power stimulates the economy before elections to improve its reelection probability. Others have observed that different parties may have different preferences over inflation and output or unemployment outcomes and, therefore, we should observe "partisan" political cycles. Hibbs (1977) was the first to introduce the partisan cycle model, in which left-wing parties were assumed to have at least one of the following: a higher output target, a higher inflation target, or a higher relative weight on minimizing output rather than inflation deviations from the targets, compared with right-wing parties. Several papers test for the existence of opportunistic or partisan cycles in the United States. Alesina, Cohen, and Roubini (1992) and Alesina, Roubini, and Cohen (1997) find only weak evidence of an opportunistic political cycle looking at M1 growth rates. Grier (1989) and Beck (1987), instead, find support for the effect of political variables on M1 growth rates for the 1960-1980 period, but not on the mean level of the federal funds rate. As discussed in Drazen (2000a, 2000b), there is basically no evidence, instead, that political cycles matter for macroeconomic outcomes by looking at data on unemployment and output growth (Grier 2008 is a recent exception), and only weak evidence for inflation. Empirical tests of the partisan cycle model (Alesina, Roubini, and Cohen 1997; Faust and Irons 1999) find partisan differences in output growth rates, but no support for partisan cycles in inflation and monetary policy. Tests of PBC typically assume monetary policy as the main tool that is exploited by politicians to manipulate the economy. These studies usually focus on comparing the level of inflation, output, money growth rates, or interest rates across political cycles, or they add a political dummy variable to the relevant regression and test its significance. This paper offers a different approach. The paper aims to empirically test various political business cycle theories adopting an optimizing New Keynesian model with a monetary and fiscal policy mix as the main setting (the inclusion of both fiscal and monetary policy is motivated by Drazen 2000b). (3) The New Keynesian model is a baseline setting for the analysis of monetary policy, but it has not been widely used to study PBC. An advantage of this paper with respect to most of the previous PBC literature lies in the use of a general equilibrium framework: this is, in fact, necessary when testing the importance of politically motivated policy choices to effectively control for differences in the macroeconomic conditions, the set of shocks, or the private sector expectations that prevail when the policy decision is taken. The monetary and fiscal policy rule parameters, as well as the parameters that reflect the frequency of price adjustment by firms and the steady-state level of inflation, are allowed to depend on the (observed) political regime. …

This paper aims to infer the evolving Fed's inflation target by estimating a monetary model under the assumptions of RE and learning. The results emphasize how different assumptions about expectations may have important effects on the inferred target movements.

Factor-augmented VARs (FAVARs) have combined standard VARs with factor analysis to exploit large data sets in the study of monetary policy. FAVARs enjoy a number of advantages over VARs: they allow a better identification of the monetary policy shock; they avoid the use of a single variable to proxy theoretical constructs; they allow researchers to compute impulse responses for hundreds of variables. Their shortcoming, however, is that the factors are not identified and lack an economic interpretation. This paper seeks to provide an interpretation to the factors. We propose a novel Structural Factor-Augmented VAR (SFAVAR) model, where the factors have a clear meaning: Real Activity factor, Inflation factor, Financial Market factor, Credit factor, Expectations factor, and so forth. The paper employs a Bayesian approach to jointly estimate the factors and the dynamic model. This framework is then used to study the effects of monetary policy on a wide range of macroeconomic variables

This paper estimates a monetary DSGE model with learning introduced from the primitive assumptions. The model nests infinite-horizon learning and features, such as habit formation in consumption and inflation indexation, that are essential for the model fit under rational expectations. I estimate the DSGE model by Bayesian methods, obtaining estimates of the main learning parameter, the constant gain, jointly with the deep parameters of the economy. The results show that relaxing the assumption of rational expectations in favor of learning may render mechanical sources of persistence superfluous. In particular, learning appears to be a crucial determinant of inflation inertia.

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.

Factor-augmented VARs (FAVARs) have combined standard VARs with factor analysis to exploit large data sets in the study of monetary policy. FAVARs enjoy a number of advantages over VARs: they allow a better identification of the monetary policy shock; they can avoid the use of a single variable to proxy theoretical constructs, such as the output gap; they allow researchers to compute impulse responses for hundreds of variables. Their shortcoming, however, is that the factors are not identified and, therefore, lack any economic interpretation. This paper seeks to provide an interpretation to the factors. We propose a novel Structural Factor-Augmented VAR (SFAVAR) model, where the factors have a clear meaning: 'Real Activity' factor, 'Price Pressures' factor, 'Financial Market' factor, 'Credit Conditions' factor, 'Expectations' factor, etc. The paper employs a Bayesian approach to extract the factors and jointly estimate the model. This framework is then suited to study the effects on a wide range of macroeconomic variables of monetary policy and non-policy shocks.

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.

In this paper a nonlinear deconvolving system, based on the use of the recently introduced flexible activation function whose control points are adaptively changed, is proposed.

Monetary policy has been usually analyzed in the context of small macroeconomic models where central banks are allowed to exploit a limited amount of information. Under these frameworks, researchers typically derive the optimality of aggressive monetary rules, contrasting with the observed policy conservatism and interest rate smoothing. This paper allows the central bank to exploit a wider information set, while taking into account the associated model uncertainty, by employing Bayesian Model Averaging with Markov Chain Model Composition (MC³). In this enriched environment, we derive the optimality of smoother and more cautious policy rates, together with clear gains in macroeconomic efficiency.

In a study of the space distribution of eight red blood cells markers in European and Asian populations, evidence for a longitudinal cline of GLO, ESD, PGP, AK, and 6-PGD allele frequencies was found. The respective Fst values appeared significantly greater than those computed at loci whose allele frequencies are not associated with longitude, namely ADA, GPT, and SOD. A systematic pressure appears to have acted upon the former systems, even though the effects of selection can partially overlap with the effects of migratory movements, which have already been demonstrated in the studied area. Conversely, random genetic drift, or random fluctuation of selection coefficients, cannot account for the observed gene frequency diversity.

This paper starts from the observation that parameter instability and model uncertainty are relevant problems for the analysis of monetary policy in small macroeconomic models. We propose to deal with these two problems by implementing a novel "thick recursive modelling" approach. At each point in time we estimate all models generated by the combinations of a base-set of k observable regressors for aggregate demand and supply. We compute optimal monetary policies for all possible models and then consider alternative ways of summarizing their distribution. Our main results show that thick recursive modelling delivers optimal policy rates that track the observed policy rates better than the optimal policy rates obtained under a constant parameter specification with no role for model uncertainty.