Expectation formation of older married couples and the rational expectations hypothesis

This paper tests the Rational Expectations (RE) hypothesis regarding retirement expectations of older married American couples, controlling for sample selection and reporting biases. In prior research we found that individual retirement expectation formation was consistent with the Rational Expectation hypothesis, but in that work spousal considerations were not analyzed. In this research we take advantage of panel data on expectations to test the RE hypothesis among married individuals as well as joint expectations among couples. We find that regardless of whether we assume that married individuals form their own expectations taking spouse's information as exogenous, or the reports of the couple are the result of a joint expectation formation process, their expectations are consistent with the RE hypothesis. Our results support a wide variety of models in economics that assume rational behavior of married couples.     

Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia

This study estimates the technical efficiency measures of maize producing farm households in Ethiopia using stochastic frontier (SF) panel models that take different approaches to model firm heterogeneity. The efficiency measures are found to vary depending on how the estimation model treats both unobserved and observed firm heterogeneity. Estimates from the ‘true’ random effects (TRE) models that treat firm effects as heterogeneity are found to be identical to those from pooled SF models. Those results differ from the ones generated from the basic random effects (RE) models that treat firm effects as part of overall technical inefficiency. The more flexible generalised ‘true’ random effects (GTRE) model that splits the error term into firm effects, persistent inefficiency, transient inefficiency, and a random noise component indicates the presence of higher levels of persistent inefficiency than transient inefficiency. The basic truncated-normal RE model and heteroscedastic RE model yields similar efficiency estimates. The GTRE model predict persistent efficiency measures similar to those from the basic RE and flexible RE model with environmental variables incorporated in the variance function as well as in the deterministic production frontier. These results imply that the RE and GTRE panel models provide reliable efficiency estimates for our data compared to the TRE models. All the estimated SF models generate comparable production function parameters in terms of magnitude and sign. Overall, the results underscore the importance of scrutinising stochastic frontier models for their reliability of analytical results before drawing policy inferences.

     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Experimental evidence on inflation expectation formation

Using laboratory experiments within a New Keynesian sticky price framework, we study the process of inflation expectation formation. We focus on adaptive learning and rational expectations contrary to the previous literature that mostly studied simple heuristics. Using a test for rational expectations that allows heterogeneity of expectations we find that we cannot reject rationality for about 40% of subjects. More than 20% of subjects are also best described by adaptive learning models, where they behave like econometricians and update their model estimates every period. However, rather than using a single forecasting model, switching between models describes their behavior better. Switching is more likely to occur when experimental economy is in a recession.     

Identification of rational expectations models

This paper deals with the problem of the identification of simultaneous Rational Expectations (RE) models. In the case of RE models with current expectations of the endogenous variables, the necessary and sufficient conditions for the global identification are derived explicitly in terms of the structural parameters and the linear homogenous identifying restrictions. It is shown that in the absence of a priori restrictions on the processes generating the exogenous variables and the disturbances, RE models and general distributed lag models are ‘observationally equivalent’. In the case of RE models with future expectations of the endogenous variables, a general solution that highlights the ‘non-uniqueness’ problem and from which other solutions such as forward or backward solutions can be obtained, is derived. It is shown that untestable and often quite arbitrary restrictions are needed if RE models with future expectations are to be identifiable. Certain order conditions similar to those obtained for the identification of RE models with current expectations are also derived for this case.     

Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations

This study examines the role of households’ expectations in predicting the housing boom–bust cycles in the United States. It incorporates two nonlinear features of housing price dynamics: a threshold co-movement between households’ expectations and housing price growth and a structural break in their interrelation. It uses the monthly good-time-to-buy (GTTB) index as a proxy for households’ expectations about the U.S. housing market, and employs the structural break threshold vector autoregression (SBTVAR) to specify breakpoints in housing market dynamics during the recent decades. The findings indicate that shifts in interactions between households’ expectations and housing price growth are synchronous with the recent housing boom–bust cycles. The SBTVAR framework outperforms other models as it captures more of the housing market's unique dynamic characteristics. The GTTB index, which governs expectation regime-switching patterns, is able to signal the recent housing bust three periods in advance.     

Constructing Multi‐Country Rational Expectations Models

This article considers some of the technical issues involved in using the global vector autoregression (GVAR) approach to construct a multi‐country rational expectations (RE) model and illustrates them with a new Keynesian model for 33 countries estimated with quarterly data over the period 1980–2011. The issues considered are: the measurement of steady states; the determination of exchange rates and the specification of the short‐run country‐specific models; the identification and estimation of the model subject to the theoretical constraints required for a determinate rational expectations solution; the solution of a large RE model; the structure and estimation of the covariance matrix and the simulation of shocks. The model used as an illustration shows that global demand and supply shocks are the most important drivers of output, inflation and interest rates in the long run. By contrast, monetary or exchange rate shocks have only a short‐run impact in the evolution of the world economy. The article also shows the importance of international connections, directly as well as indirectly through spillover effects. Overall, ignoring global inter‐connections as country‐specific models do, could give rise to misleading conclusions.     

MONETARY POLICY IN A NON‐REPRESENTATIVE AGENT ECONOMY: A SURVEY

It is well known that central bank policies affect not only macroeconomic aggregates, but also their distribution across economic agents. Similarly, a number of papers demonstrated that heterogeneity of agents may matter for the transmission of monetary policy to macro variables. Despite this, the mainstream monetary economics literature has so far been dominated by dynamic stochastic general equilibrium models with representative agents. This paper aims to tilt this imbalance towards heterogeneous agents setups by surveying the main positive and normative findings of this line of the literature, and suggesting areas in which these models could be implemented. In particular, we review studies that analyse the heterogeneity of (i) households’ income, (ii) households’ preferences, (iii) consumers’ age, (iv) expectations and (v) firms’ productivity and financial position. We highlight the results on issues that, by construction, cannot be investigated in a representative agent framework and discuss important papers modifying the findings from the representative agent literature.     

Binary choice under social interactions: an empirical study with and without subjective data on expectations

This paper examines two methods of modeling binary choice with social interactions: models assuming homogeneous rational expectations and models using subjective data on expectations. Exploiting a unique survey conducted during the 1996 US presidential election that was designed to study voting behavior under social context, we find that in various model specifications using subjective expectations consistently improves models' goodness‐of‐fit; and that subjective expectations are not rational as formulated by Brock and Durlauf. Specifically, members' characteristics are individually important in forming expectations. We also include correlated effect in the rational expectation model. This extension provides a remedy to the selection issues that often arise in social interaction models. Copyright © 2008 John Wiley & Sons, Ltd.     

Issue Information

Much research studies US inflation history with a trend‐cycle model with unobserved components, where the trend may be viewed as the Fed's evolving inflation target or long‐horizon expected inflation. We provide a novel way to measure the slowly evolving trend and the cycle (or inflation gap), by combining inflation predictions from the Survey of Professional Forecasters (SPF) with realized inflation. The SPF forecasts may be treated either as rational expectations (RE) or updating according to a sticky information (SI) law of motion. We estimate RE and SI state‐space models with stochastic volatility on samples of consumer price index and gross national product/gross domestic product deflator inflation and the associated SPF inflation predictions using a particle Metropolis–Markov chain Monte Carlo sampler. The trend converges to 2% and its volatility declines over time—two tendencies largely complete by the late 1990s.     

Regional Inequalities in Consumption Patterns: A Multilevel Approach to the Case of Italy

The main aim of this paper is to evaluate the disparities in the Italian regions on the demand side. In more detail, an attempt will be made to find if the consumption behaviour of Italian households is different in the regions. With this in mind, Istat's 2000 Italian Family Budget data set was analysed. The data in question, which were collected through a two‐stage sample over Italy's 20 regions, contains information regarding the expenses of approximately 23,000 households. In this analysis, both households and regions are considered as units: households are nested in the regions so that the basic data structure is hierarchical. In order to take this hierarchical structure into account, a multilevel model was used, making it possible for parameters to vary randomly from region to region. The model in question also made it possible to consider heterogeneity across different groups (regions), such as stochastic variation. First, regional inequalities were tested using a simple model in which households constituted the first level of analysis and were grouped according to their region (the second level). As a second step, and in order to investigate the interaction between geographical context and income distribution, another model was used. This was cross‐classified by income and regions. The most relevant results showed that there is wide fragmentation of consumption behaviour and, at the same time, various differentiated types of behaviour in the regions under analysis. These territorial differentials become clear from income class and items of consumption.     

DO CONSUMER PRICE SUBSIDIES REALLY IMPROVE NUTRITION?

Many developing countries use food-price subsidies or controls to improve nutrition. However, subsidizing goods on which households spend a high proportion of their budget can create large wealth effects. Consumers may then substitute towards foods with higher non-nutritional attributes (e.g., taste), but lower nutritional content per unit of currency, weakening or perhaps even reversing the subsidy's intended impact. We analyze data from a randomized program of large price subsidies for poor households in two provinces of China and find no evidence that the subsidies improved nutrition. In fact, it may have had a negative impact for some households. (JEL I38; O12; Q18).     

Behavior of the firm under rate-of-return regulation with two capital inputs

Traditional electric utility companies face a trade-off between building generation facilities that utilize renewable energy (RE) and non-renewable energy (non-RE). The firm's input decision to build capacity for either source depends on several constraining factors, including input prices, policies that promote or discourage RE use, and the type of regulation faced by the firm. This paper models the utility company's decision between RE and non-RE capital inputs. From the model, we derive the result that rate-of-return (ROR) regulation decreases the investment in RE capital relative to the unregulated firm. These findings suggest restructuring electricity generation markets, which removes the ROR on generating assets, can increase the relative use of RE. A second result of the model shows that the renewable portfolio standard (RPS) increases the investment in capital that requires RE as a source of electricity, as expected. This paper contributes to the literature on the substitution between renewable and non-renewable resources, by examining the policies that affect the investment in the two types of technologies. The model can also be applied to other regulated utilities, such as water or natural gas companies, with outputs that are produced from different types of capital.     

Mean–variance econometric analysis of household portfolios

We investigate households' portfolio choice using a microeconometric approach derived from mean–variance optimization. We assume that households have heterogeneous expectations on the distribution of excess returns and that they cannot take short positions in risky assets. Assuming two such assets, we derive an explicit solution of the model characterized by four possible portfolio regimes, which are analyzed using two structural probit and tobit specifications with three latent state variables. Both specifications are estimated by weighted maximum likelihood on a cross‐section of US households drawn from the 2004 SCF. The tobit specification is simulated in order to evaluate the regressors' effects on regime probabilities and asset demands. We also assess to what extent the predicted state variables are consistent with the self‐reported expected returns and risk aversion elicited from the SCF questionnaire. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting performance of multivariate time series models with full and reduced rank: an empirical examination

Since Quenouille's influential work on multiple time series, much progress has been made towards the goal of parameter reduction and model fit. Relatively less attention has been paid to the systematic evaluation of out-of-sample forecast performance of multivariate time series models. In this paper, we update the hog data set studied by Quenouille (and other researchers who followed him). We re-estimate his model with extended observations (1867–1966), and generate recursive one- to four-steps-ahead forecasts for the period of 1967 through 2000. These forecasts are compared to forecasts from an unrestricted vector autoregression, a reduced rank regression model, an index model and a cointegration-based error correction model. The error correction model that takes into account both nonstationarity of the data and rank reduction performs best at all four forecasting horizons. However, differences among competing models are statistically insignificant in most cases. No model consistently encompasses the others at all four horizons.     

Inflation expectations in India: Learning from household tendency surveys

We use a large household survey that is being conducted by the Reserve Bank of India since 2005 to estimate the dynamics of aggregate inflation expectations over a volatile inflation regime. A simple average of the quantitative responses produces biased estimates of the official inflation data. We therefore estimate expectations by quantifying the reported directional responses. We perform quantification by using the hierarchical ordered probit model, in addition to the balance statistic. We find that the quantified expectations from qualitative forecasts track the actual inflation rate better than the averages of the quantitative forecasts, highlighting the filtering role of qualitative tendency surveys. We also report estimates of the disagreement among households. The proposed approach is particularly suitable in emerging economies, where inflation tends to be high and volatile.     

Fixed and random effects models: making an informed choice

This paper assesses the options available to researchers analysing multilevel (including longitudinal) data, with the aim of supporting good methodological decision-making. Given the confusion in the literature about the key properties of fixed and random effects (FE and RE) models, we present these models’ capabilities and limitations. We also discuss the within-between RE model, sometimes misleadingly labelled a ‘hybrid’ model, showing that it is the most general of the three, with all the strengths of the other two. As such, and because it allows for important extensions—notably random slopes—we argue it should be used (as a starting point at least) in all multilevel analyses. We develop the argument through simulations, evaluating how these models cope with some likely mis-specifications. These simulations reveal that (1) failing to include random slopes can generate anti-conservative standard errors, and (2) assuming random intercepts are Normally distributed, when they are not, introduces only modest biases. These results strengthen the case for the use of, and need for, these models.

     

Learning, monetary policy rules, and macroeconomic stability

Several papers have documented a regime switch in US monetary policy from ‘passive’ and destabilizing in the pre-1979 period to ‘active’ and stabilizing afterwards. These studies typically work with DSGE models with rational expectations.This paper relaxes the assumption of rational expectations and allows for learning instead. Economic agents form expectations from simple models and update the parameters through constant-gain learning. In this setting, the paper aims to test whether monetary policy may have been a source of macroeconomic instability in the 1970s by inducing unstable learning dynamics.The model is estimated by Bayesian methods. The constant-gain coefficient is jointly estimated with the structural and policy parameters in the system.The results show that monetary policy was respecting the Taylor principle also in the pre-1979 period and, therefore, did not trigger macroeconomic instability.     

Estimating production technology for policy analysis: trading off precision and heterogeneity

This paper develops an approach to select models that can make the best use of limited micro-level data sets to estimate production function parameters. Since production is often the core of the agricultural and environment policy analyses, we evaluate the models using criteria that reflect the objectives of policy analysis. We argue that policy production models should optimize the precision of policy response predictions, but also incorporate sufficient heterogeneity to allow policy makers to consider the distributional consequences of policies. Hence we develop a series of quantitative metrics of both precision and heterogeneity to compare model performance. Our approach consists of two steps. We first combine the method of generalized maximum entropy and data envelopment analysis and simultaneously estimate the production frontier and technical inefficiency parameters. With a set of household level data, we estimate production models at three different levels. The province-level model restricts the production technology parameters to be the same for all households. The county-level models allow production technology parameters to vary by county but restrict them to be equal across communities within the same county. The community-level models allow production technology parameters to vary by community. In the second step, we use the disaggregated information gain, percentage absolute prediction error and the Theil??s U statistic to evaluate these models.