scholarly journals Can Agents with Causal Misperceptions be Systematically Fooled?

2019 ◽  
Vol 18 (2) ◽  
pp. 583-617 ◽  
Author(s):  
Ran Spiegler
Keyword(s):  
Monetary Policy ◽  
Bayesian Networks ◽  
Directed Acyclic Graph ◽  
Phillips Curve ◽  
Causal Model ◽  
Expected Value ◽  
Prediction Errors ◽  
Acyclic Graph ◽  
Long Run ◽  
Individual Variables

Abstract An agent forms estimates (or forecasts) of individual variables conditional on some observed signal. His estimates are based on fitting a subjective causal model—formalized as a directed acyclic graph, following the “Bayesian networks” literature—to objective long-run data. I show that the agent’s average estimates coincide with the variables’ true expected value (for any underlying objective distribution) if and only if the agent’s graph is perfect—that is, it directly links every pair of variables that it perceives as causes of some third variable. This result identifies neglect of direct correlation between perceived causes as the kind of causal misperception that can generate systematic prediction errors. I demonstrate the relevance of this result for economic applications: speculative trade, manipulation of a firm’s reputation, and a stylized “monetary policy” example in which the inflation-output relation obeys an expectational Phillips Curve.

scholarly journals Short-Run and Long-Run Effects of Milton Friedman's Presidential Address

2018 ◽  
Vol 32 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Robert E. Hall ◽  
Thomas J. Sargent
Keyword(s):  
Monetary Policy ◽  
Phillips Curve ◽  
Presidential Address ◽  
Real Economy ◽  
The Real ◽  
Long Run ◽  
Invariance Hypothesis ◽  
Short Run ◽  
The Right ◽  
Main Message

The centerpiece of Milton Friedman's (1968) presidential address to the American Economic Association, delivered in Washington, DC, on December 29, 1967, was the striking proposition that monetary policy has no longer-run effects on the real economy. Friedman focused on two real measures, the unemployment rate and the real interest rate, but the message was broader—in the longer run, monetary policy controls only the price level. We call this the monetary-policy invariance hypothesis. By 1968, macroeconomics had adopted the basic Phillips curve as the favored model of correlations between inflation and unemployment, and Friedman used the Phillips curve in the exposition of the invariance hypothesis. Friedman's presidential address was commonly interpreted as a recommendation to add a previously omitted variable, the rate of inflation anticipated by the public, to the right-hand side of what then became an augmented Phillips curve. We believe that Friedman's main message, the invariance hypothesis about long-term outcomes, has prevailed over the last half-century based on the broad sweep of evidence from many economies over many years. Subsequent research has not been kind to the Phillips curve, but we will argue that Friedman's exposition of the invariance hypothesis in terms of a 1960s-style Phillips curve is incidental to his main message.

scholarly journals An optimal control problem of monetary policy

2021 ◽  
Vol 26 (11) ◽  
pp. 5769
Author(s):  
Andrea Bacchiocchi ◽  
Germana Giombini
Keyword(s):  
Optimal Control ◽  
Monetary Policy ◽  
Interest Rate ◽  
Optimal Control Problem ◽  
Continuous Time ◽  
Phillips Curve ◽  
Optimal Monetary Policy ◽  
Trade Off ◽  
Long Run ◽  
The Interest Rate

<p style='text-indent:20px;'>This paper analyses an optimal monetary policy under a non-linear Phillips curve and linear GDP dynamics. A central bank controls the inflation and the GDP trends through the adjustment of the interest rate to prevent shocks and deviations from the long-run optimal targets. The optimal control path for the monetary instrument, the interest rate, is the result of a dynamic minimization problem in a continuous-time fashion. The model allows considering various economic dynamics ranging from hyperinflation to disinflation, sustained growth and recession. The outcomes provide useful monetary policy insights and reveal the dilemma between objectives faced by the monetary authority in trade-off scenarios.</p>

scholarly journals Bayesian Networks and Boundedly Rational Expectations *

2016 ◽  
Vol 131 (3) ◽  
pp. 1243-1290 ◽  
Author(s):  
Ran Spiegler
Keyword(s):  
Rational Expectations ◽  
Directed Acyclic Graph ◽  
Causal Model ◽  
Causal Attribution ◽  
Demand For Education ◽  
Several Variables ◽  
Long Run ◽  
Correlation Structures ◽  
Personal Equilibrium ◽  
Subjective Belief

AbstractI present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). The DM is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p , resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian network formula. As a result of this belief distortion, the DM’s evaluation of actions can vary with their long-run frequencies. Accordingly, I define a ”personal equilibrium” notion of individual behavior. The framework enables simple graphical representations of causal-attribution errors (such as coarseness or reverse causation), and provides tools for checking rationality properties of the DM’s behavior. I demonstrate the framework’s scope of applications with examples covering diverse areas, from demand for education to public policy.

Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve

2008 ◽  
Vol 98 (5) ◽  
pp. 2101-2126 ◽  
Author(s):  
Timothy Cogley ◽  
Argia M Sbordone
Keyword(s):  
Monetary Policy ◽  
Ad Hoc ◽  
Phillips Curve ◽  
New Keynesian ◽  
Time Varying ◽  
New Keynesian Phillips Curve ◽  
Inflation Persistence ◽  
Long Run ◽  
Trend Inflation ◽  
Forward Looking

Purely forward-looking versions of the New Keynesian Phillips curve (NKPC) generate too little inflation persistence. Some authors add ad hoc backward-looking terms to address this shortcoming. We hypothesize that inflation persistence results mainly from variation in the long-run trend component of inflation, which we attribute to shifts in monetary policy. We derive a version of the NKPC that incorporates a time-varying inflation trend and examine whether it explains the dynamics of inflation. When drift in trend inflation is taken into account, a purely forward-looking version of the model fits the data well, and there is no need for backward-looking components. (JEL E12, E31, E52)

PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES

2006 ◽  
Vol 17 (03) ◽  
pp. 447-455 ◽  
Author(s):  
PABLO FELGAER ◽  
PAOLA BRITOS ◽  
RAMÓN GARCÍA-MARTÍNEZ
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Hybrid Method ◽  
Learning Process ◽  
Directed Acyclic Graph ◽  
Learning Method ◽  
Automatic Learning ◽  
Acyclic Graph ◽  
Health Domain ◽  
Learning Techniques

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

FUZZY BAYESIAN NETWORKS — A GENERAL FORMALISM FOR REPRESENTATION, INFERENCE AND LEARNING WITH HYBRID BAYESIAN NETWORKS

2000 ◽  
Vol 14 (07) ◽  
pp. 941-962 ◽  
Author(s):  
HEPING PAN ◽  
LIN LIU
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Directed Acyclic Graph ◽  
Continuous Variable ◽  
Discrete Variables ◽  
Discrete Variable ◽  
Hybrid Bayesian Networks ◽  
Acyclic Graph ◽  
Continuous And Discrete Variables ◽  
Fuzzy Bayesian Networks

This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.

A Causative Approach to Demographic and Socioeconomic Factors Affecting Parental Ratings of Child Oral Health

2020 ◽  
Vol 6 (1) ◽  
pp. 68-76
Author(s):  
M.A. Foley ◽  
A.J. Spencer ◽  
R. Lalloo ◽  
L.G. Do
Keyword(s):  
Oral Health ◽  
Directed Acyclic Graph ◽  
Causal Model ◽  
Effect Sizes ◽  
Sensitivity Analyses ◽  
Regression Analyses ◽  
Acyclic Graph ◽  
Education Levels ◽  
Parental Ratings ◽  
Child Oral Health

Introduction: Many studies have investigated associations between demographic, socioeconomic status (SES), behavioral, and clinical factors and parental ratings of child oral health. Caries experience, pain, missing teeth, malocclusions, and conditions and treatments likely to negatively affect the child or family in the future have been consistently associated with poorer parental ratings. In contrast, effect sizes for associations between demographic and SES indicators (race/ethnicity, country of birth, family structure, household income, employment status, and parental education levels) and parental ratings vary greatly. Objectives: The primary objectives of this study were to estimate effect sizes for associations between demographic and SES variables and parental ratings of child oral health and then to consider possible causal implications. Methods: This article uses a nationally representative data set from 24,664 Australian children aged 5 to 14 y, regression analyses guided by a directed acyclic graph causal model, and sensitivity analyses to investigate effects of demographic and SES factors on parental ratings of oral health. Results: One in 8 children had oral health rated as fair or poor by a parent. Indigenous children, older boys, young children with a migrant parent, children from single-parent families, low-income households and families where no parent worked full-time, and children whose parents had lower education levels were much more likely to receive a fair or poor parental oral health rating in crude and adjusted models. Conclusion: This cross-sectional study helps to clarify inconsistent findings from previous research and shows many demographic and SES variables to be strong determinants of parental ratings of child oral health, consistent with the effects of these variables on other health outcomes. Sensitivity analyses and consideration of the potential for chance and bias to have affected these findings suggest that many of these associations may be causal. Knowledge Transfer Statement: Based on regression analyses driven by a directed acyclic graph causal model, this research shows a strong impact of demographic and socioeconomic determinants on parental ratings of child oral health, consistent with associations between these variables and other oral and general health outcomes. Many of these associations may be causal. We demonstrate the value of causal models and causal thinking when analyzing complex multilevel observational data.

scholarly journals Labor Productivity and Phillips Curve in China: Contemporary Investigation

2019 ◽  
Vol 15 (1) ◽  
pp. 37-48
Author(s):  
Nahid Kalbasi Anaraki
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Phillips Curve ◽  
Short Term ◽  
Trade Off ◽  
The United Kingdom ◽  
Long Run ◽  
Advanced Economies ◽  
Using Data ◽  
The Relationship

The Phillips curve on the trade-off between inflation and unemployment has been debated among economists for more than decades. Several studies have found that Phillips curve is dead in advanced economies and does not exist. Among others, Friedman (1968) stated that Phillips curve does not exist in the long-run because the relationship between inflation and unemployment is a temporary and short-term. On the contrary, Fuhrer (1995) found that Phillips curve is still alive in the United Kingdom; and Malinov and Sommers (1997) found that Phillips curve is still alive and stable in several OECD countries. This paper attempts to investigate whether a long-run Philips Curve exists in China. Using data for the period of 1987-2016 the estimated results of this study indicate that the Phillips curve, which existed during the late 1980s through 2000 in China has been gradually transformed to an almost vertical curve since 2000s, with a correlation of 0.8, indicating the importance of other policy variables including monetary policy and exchange rate regimes.

scholarly journals A Model of Competing Narratives

2020 ◽  
Vol 110 (12) ◽  
pp. 3786-3816
Author(s):  
Kfir Eliaz ◽  
Ran Spiegler
Keyword(s):  
Probability Distribution ◽  
Bayesian Networks ◽  
Causal Model ◽  
Random Variables ◽  
Equilibrium Analysis ◽  
Political Polarization ◽  
Long Run ◽  
Selection Of

We formalize the argument that political disagreements can be traced to a “clash of narratives.” Drawing on the “Bayesian Networks” literature, we represent a narrative by a causal model that maps actions into consequences, weaving a selection of other random variables into the story. Narratives generate beliefs by interpreting long-run correlations between these variables. An equilibrium is defined as a probability distribution over narrative-policy pairs that maximize a representative agent's anticipatory utility, capturing the idea that people are drawn to hopeful narratives. Our equilibrium analysis sheds light on the structure of prevailing narratives, the variables they involve, the policies they sustain, and their contribution to political polarization. (JEL D72, D83, D85, F52)

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