Solving DSGE portfolio choice models with dispersed private information

Noisy rational expectations models, in which agents have dispersed private information and extract information from an endogenous asset price, are widely used in finance. However, these linear partial equilibrium models do not fit well in modern macroeconomics that is based on non-linear dynamic general equilibrium models. We develop a method for solving a DSGE model with portfolio choice and dispersed private information. We combine and extend existing local approximation methods applied to public information DSGE settings with methods for solving noisy rational expectations models in finance with dispersed private information.     

Learnability of an equilibrium with private information

This paper investigates the learnability of an equilibrium with private information. Agents of each type have their own private information about an exogenous variable and conduct adaptive learning with a heterogeneously misspecified perceived laws of motion (PLM) that includes only this variable. The paper shows that the existence of private information has a nonnegative impact on the learnability of the equilibrium; that is, the condition for learnability is unaffected or relaxed by heterogeneity and/or misspecification in PLMs caused by private information. In a New Keynesian model with private information about fundamental shocks, the learnability of the equilibrium is ensured by the Taylor principle of monetary policy. The paper also confirms that these results hold true not only in the presence of private information, but also in a variety of informational structures.     

The stability of macroeconomic systems with Bayesian learners

We study abstract macroeconomic systems in which expectations play an important role. Consistent with the recent literature on recursive learning and expectations, we replace the agents in the economy with econometricians. Unlike the recursive learning literature, however, the econometricians in the analysis here are Bayesian learners. We are interested in the extent to which expectational stability remains the key concept in the Bayesian environment. We isolate conditions under which versions of expectational stability conditions govern the stability of these systems just as in the standard case of recursive learning. We conclude that Bayesian learning schemes, while they are more sophisticated, do not alter the essential expectational stability findings in the literature.     

Learning in linear models with expectational leads

Models with expectational leads typically admit multiple rational expectations solutions. Based on the ordinary least-squares algorithm, this paper provides an adaptive learning scheme which allows a forecasting agent to select a particular solution on economic grounds. Conditions are given under which this scheme converges to rational expectations solutions globally for all initial conditions. We strengthen convergence results in relaxing standard assumptions and in providing conditions ensuring algorithm convergence which are easier to verify and to interpret than those previously known.     

Rational expectations equilibria and the ex-post core of an economy with asymmetric information

We study the relationship between the set of rational expectations equilibrium allocations and the ex-post core of exchange economies with asymmetric information.     

Insider trading,overconfidence, and private information flow

In this study, we investigate a dynamic model wherein an overconfident and a risk-neutral informed trader optimally exploit their long-lived private information regarding the value of an asset. We find that when the degree of overconfidence becomes larger, or the intensity of private information flow becomes larger relative to the initial private signal, the market becomes more stable. Additionally, we find that the greater the intensity of private information flow relative to the initial private signal, the more evident the patient transaction and the slower the information is incorporated in the price.     

Invertible and non-invertible information sets in linear rational expectations models

Rational expectations solutions are usually derived by assuming that all state variables relevant to forward-looking behaviour are directly observable, or that they are “…an invertible function of observables” (Mehra and Prescott, 1980). Using a framework that nests linearised DSGE models, we give a number of results useful for the analysis of linear rational expectations models with restricted information sets. We distinguish between instantaneous and asymptotic invertibility, and show that the latter may require significantly less information than the former. We also show that non-invertibility of the information set can have significant implications for the time series properties of economies.     

Robust efficiency in mixed economies with asymmetric information

In this paper, we study robustly efficient allocations in a pure exchange economy. Answering a question of Hervés-Beloso and Moreno-García (2008), we present an extension of their main result to an asymmetric information mixed economy whose commodity space is an ordered separable Banach space having an interior point in its positive cone.     

Blocking efficiency in an economy with asymmetric information

In this paper, we study a pure exchange atomless economy with asymmetric information and having an ordered Banach space with an interior point in its positive cone as the commodity space. An extension of the main theorem in Vind (1972) to the private core without free disposal is established. As a particular case of this result, a solution to a problem mentioned in Pesce (2010) is derived.     

Adaptive learning,endogenous uncertainty,and asymmetric dynamics

I present a simple model where forecasting confidence affects aggregate demand. It is shown that this model has similar stability properties, under statistical and evolutionary learning, as a model without a confidence affect. From this setup, I introduce “Expectational Business Cycles” where output fluctuates due to learning, heterogeneous forecasting models and random changes in the efficient forecasting model. Agents use one of two forecasting models to forecast future variables while heterogeneity is dictated via an evolutionary process. Increased uncertainty, due to a shock to the structure of the economy, may result in a sudden decrease in output. As agents learn the equilibrium, output slowly increases to its equilibrium value. Expectational business cycles tend to arrive faster, last longer and are more severe as agents possess less information.     

A remark on rational expectation equilibria with incomplete markets and real assets

In this note, I established the existence, for a generic set of endowments, of a fully revealing rational expectation equilibrium (REE) in an economy characterized by incomplete markets and real assets.     

Assessing the utility of private information in transportation planning studies: A case study of trip generation analysis

The extent to which private information should be used in planning for transportation infrastructure has not been fully resolved. To contribute to this debate, this paper discusses the argument for using private information by presenting a transportation planning case study in which the sample size required to detect a significant difference in two types of residential trip generation rates was reduced. One rate is based on ground counts and the other is based on household surveys. The number of vehicle trips generated by suburban dwelling units over a 24-h period is a critical element in the transportation planning process.When no-private information was used, the rates derived using the two methods were not statistically different. However, the use of private information might lead to different rates. Private information, described herein as individual transportation decisions, is defined by four characteristics: individually identifying (not anonymous); linked to another data source (not aggregated); available to a limited audience (not unlimited); and available for only a specific, socially legitimate use.The potential discrepancy between rates using ground counts and household survey data provides an opportunity to investigate the utility of private information. Simulations with an existing data set were performed to determine the extent to which additional private information could have revealed a discrepancy. The study concluded that the required sample size decreases substantially depending on the amount of private information available. The use of limited private information reduces the required sample size by over 30%. With more private information, the required sample size is reduced by over 90%. Thus, the use of robust statistical tests that is infeasible with only public information is possible with the availability of private data.The significance of this study is the approach presented to evaluate the utility of private information by documenting the tangible benefits, such as reduced sample sizes and reduced data collection costs, that will accrue if it is used. The political cost of acquiring the information can then be addressed, as can extending the approach to other situations where the use of private information is being considered.     

Estimation in a growth study with irregular measurement times

Between 1982 and 1988 a growth study was carried out at the Division of Pediatric Oncology of the University Hospital of Groningen. A special feature of the project was that sample sizes are small and that ages at entry may be very different. In addition the intended design was not fully complied with. This paper highlights some aspects of the statistical analysis which is based on (1) reference scores, (2) statistical procedures allowing for an irregular pattern of measurement times caused by missing data and shifted measurement times.     

Learning cycles in Bertrand competition with differentiated commodities and competing learning rules

This paper stresses the importance of heterogeneity in learning. We consider a Bertrand oligopoly with firms using either least squares learning or gradient learning for determining the price. We demonstrate that convergence properties of the rules are strongly affected by heterogeneity. In particular, gradient learning may become unstable as the number of gradient learners increases. Endogenous choice between the learning rules may induce cyclical switching. Stable gradient learning gives higher average profit than least squares learning, making firms switch to gradient learning. This can destabilize gradient learning which, because of decreasing profits, makes firms switch back to least squares learning.     

Arbitrage and price revelation with asymmetric information and incomplete markets

This paper deals with the issue of arbitrage with differential information and incomplete financial markets, with a focus on information that no-arbitrage asset prices can reveal. Time and uncertainty are represented by two periods and a finite set S of states of nature, one of which will prevail at the second period. Agents may operate limited financial transfers across periods and states via finitely many nominal assets. Each agent i has a private information about which state will prevail at the second period; this information is represented by a subset S_i of S. Agents receive no wrong information in the sense that the “true state” belongs to the “pooled information” set ∩_iS_i, hence assumed to be non-empty.Our analysis is two-fold. We first extend the classical symmetric information analysis to the asymmetric setting, via a concept of no-arbitrage price. Second, we study how such no-arbitrage prices convey information to agents in a decentralized way. The main difference between the symmetric and the asymmetric settings stems from the fact that a classical no-arbitrage asset price (common to every agent) always exists in the first case, but no longer in the asymmetric one, thus allowing arbitrage opportunities. This is the main reason why agents may need to refine their information up to an information structure which precludes arbitrage.     

Cohort and target age effects on subjective survival probabilities: Implications for models of the retirement phase

Subjective survival scaling factors are often estimated from one observation of life expectancy and treated as constant to any target age. Using new survey data on subjective survival probabilities, we estimate a model incorporating cohort- and target age-varying beliefs in scaling factors. Both cohort age and target age matter: respondents are pessimistic about overall life expectancy but optimistic about survival at advanced ages, and older respondents are more optimistic than younger. We propose a new theoretical model incorporating cohort- and target age-varying beliefs and illustrate their effects on the perceived value of annuities and on retirement phase consumption plans.     

Evolving macroeconomic perceptions and the term structure of interest rates

We explore the role of evolving beliefs regarding the structure of the macroeconomy in improving our understanding of the term structure of interest rates within the context of a simple macro-finance model. Using quarterly vintages of real-time data and survey forecasts for the United States over the past 40 years, we show that a recursively estimated VAR on real GDP growth, inflation and the nominal short-term interest rate generates predictions that are more consistent with survey forecasts than a benchmark fixed-coefficient counterpart. We then estimate a simple term structure model under the assumption that investor risk attitude is driven by near-term expectations of the three state variables. When we allow for evolving beliefs about the macroeconomy, the resulting term structure model provides a better fit to the cross section of yields than the benchmark model, especially at longer maturities, and exhibits better performance in out-of-sample predictions of future yield movements.     

Irreversible investment and capacity choice with Bayesian learning

This paper incorporates information incompleteness into the standard capacity choice model. The firm dynamically updates its belief about the expected growth rate based on the realization of demand shocks. First, the firm’s expansion decision becomes more conservative in the dynamic updating case. Moreover, a myopic firm always overestimates the marginal value of capital but underestimates firm value. When we decompose the firm value into the value of assets in place and growth opportunities, dynamic learning significantly increases the value of growth opportunities rather than that of assets in place. Finally, the implications for the user cost of capital is also examined.     

Contamination in linear regression models and its influence on estimators

The consequences of the omission of possibly contaminated observations in a linear regression model for the performance of the ordinary least squares ( LS- ) estimator are discussed. We compare the ordinary L Sestimator with the corresponding 'never pooled' LS -estimator with respect to the matrix-valued mean squared error. Necessary and sufficient conditions are derived for the superiority of an estimator to another one and tests are proposed to check these conditions. Finally the resulting preliminary-test-estimators are investigated.