Adaptive Expectations Research Articles

Development of thermal comfort models for various climatic zones of North-East India

Abstract Thermal comfort study provides crucial information about thermal performance of naturally ventilated buildings. Humphreys and Auliciems comfort model uses indoor and outdoor temperatures to predict comfort temperatures. It is found that the comfort temperatures obtained by using these methods do not take into account the occupant behavioural adaptability to a particular climatic zone. This demands development of new set of comfort models based on local environmental parameters, socio-cultural setup and behavioural action. Analysis shows that four major variables like indoor and outdoor temperature, relative humidity and clothing pattern plays an important role in defining comfort and greatly influence the occupant's perception and acceptance on thermal comfort. In this study, comfort models are developed based on these variables. The computed neutral temperatures based on the models are compared with the comfort temperatures obtained through comfort survey. The models are developed using the measured data of January and July months and validated with the measured data of April and October months. This study also concludes that it is not possible to obtain a generalized thermal comfort model for all climatic zone because adaptation process, expectation and perception of people are region specific and governed by local socio-cultural requirement.

The formation of wage expectations in the effort and quit decisions of workers

This study develops models in which workers form expectations of average wages in choosing levels of effort and on-the-job search. It is assumed that information on lagged average wages is available at a low fixed cost, whilst acquiring other information requires an additional variable cost. Under reasonable conditions, workers' expectations are at least partly adaptive, and may be a mixture of rational and adaptive expectations. Microeconomic parameters determine the degree to which expectations are adaptive and the weights placed on various lags of wages. As a result of partly adaptive expectations, nominal demand shocks may have real effects.

Empirical agent-based land market: Integrating adaptive economic behavior in urban land-use models

This paper introduces an economic agent-based model of an urban housing market. The RHEA (Risks and Hedonics in Empirical Agent-based land market) model captures natural hazard risks and environmental amenities through hedonic analysis, facilitating empirical agent-based land market modeling. RHEA is well grounded in economic theory and uses rich spatial data and econometric analysis. It moves beyond the existing work by explicitly simulating the emergence of property prices and their spatial distribution under adaptive price expectations of heterogeneous agents, advancing toward empirical modeling of agent-based land markets. At the same time RHEA operates in a realistic GIS landscape where realtor and households agents form ask and bid prices using empirical hedonic price functions. The simulation results demonstrate that this combination of theoretically sound micro-foundations in agents’ behavior and empirical data is feasible. This opens opportunities to explore various methodological and policy-relevant research questions including exploration of abrupt non-marginal changes in markets and regime shifts in coupled socio-environmental systems.

Learning and Signals under Discretionary Monetary Policy

AbstractThis paper aims at assessing the relevance of communicating central bank's forecasts to the private sector under discretionary monetary policy. In a New Keynesian environment, the central bank and the private sector have different information sets. The private sector uses the central bank's expectations as a signal in order to update its priors, which are constituted by standard adaptive learning expectations. E‐stability is positively affected by a high correlation between actual inflation and the signal about inflation. Numerical simulations show that the presence of signals is beneficial both in the long and in the short run.

Transitory noise in reported earnings: Implications for forecasting and valuation

The paper considers a setting where the present value of expected dividends determines price and the information to forecast the future includes reported earnings. In the model, reported earnings have been garbled by transitory noise, which cannot be inferred. ‘True’, but now unobservable, earnings are permanent as in Ohlson (1992). The stage setting result shows that capitalized expected reported earnings for the next period equals price regardless of the noise. More subtle is the influence of current reported earnings on the forecast of future expected earnings and, relatedly, the valuation in terms of the history of data. Because of the noise term, Bayesian updating implies that the investor uses the entire earnings history to learn about permanent earnings and to forecast future expected reported earnings. Specifically, the main result shows that the next period’s expected earnings equal a weighted average of (i) current reported earnings and (ii) beginning-of-the-period expected earnings for the current period. This framework is often referred to as ‘adaptive expectations’ because there is gradual learning and updating. It depends critically on dividend policy irrelevance. The paper goes on to show that the weight on current earnings (term (i)) decreases as the noise increases. The model has testable implications for returns on earnings regressions and how one operationalizes value-relevance.

EXPECTATIVAS DE INFLACIÓN EN COSTA RICA

<p>The Banco Central de Costa Rica (BCCR) is currently transitioning to an Inflation<br />Targeting (IT) regime. In such context, it is relevant for the monetary authority to<br />characterize in the most complete way possible the inflation expectations of the<br />population, both in the particular elements of the forecasts and in the processes by<br />which agents take information into account for their individual predictions. This paper<br />documents the analysis of the inflation expectations data contained in the Monthly Survey<br />of Inflation and Exchange Rate Expectations. We test several hypotheses such as rational<br />and adaptive expectations. The results show significant biases in those expectations, while<br />there is also a lack of learning from past errors by the agents surveyed.</p>

Regional US House Price Formation: One Model Fits All?

Does a "one model fits all" approach apply to the econometric modeling of regional house price determination? To answer this question, we utilize a panel of 100 US Metropolitan Statistical Areas over the period 1980q1-2010q2. For each area we estimate a separate cointegrated VAR model, focusing on differences in the effect of subprime lending and lagged house price appreciation. Our results demonstrate substantial differences in the importance of subprime lending for house price determination across regional housing markets. Specifically, we find a greater impact of subprime lending in areas with a high degree of physical and regulatory restrictions on land supply. Likewise, lagged house price appreciation - interpreted as capturing an adaptive expectation channel - is found to be more important in areas where the supply of dwellings is more constrained, in areas located in a state with non-recourse lending and in more populous areas. Our results also suggest that disequilibrium constellations are restored more slowly in areas located in a state with non-recourse lending.

Stability of a Continuous Reaction-Diffusion Cournot-Kopel Duopoly Game Model

In order to take into account the territory in which the outputs are in the market and the time-depending firms' strategies, the discrete Cournot duopoly game (with adaptive expectations, modeled by Kopel) is generalized through a non autonomous reaction-diffusion binary system of PDEs, with self and cross diffusion terms. Linear and nonlinear asymptotic L 2-stability, via the Liapunov Direct Methot and a nonautonomous energy functional, are investigated.

The price-price Phillips curve in small open economies and monetary unions: theory and empirics

This paper extends the efficiency wages/partially adaptive expectations Phillips curve, otherwise known as the price-price Phillips curve, from a closed economy context to an open economy one with both commodity trade and capital mobility. We also consider the case of a monetary union (a country) with two member states (regions). The theoretical results are a priori ambiguous. However, in the first place, on resorting to plausible numerical simulations, economic openness increases the reactiveness of inflation to the unemployment rate. In regard to a monetary union, the national unemployment multiplier in the aggregate Phillips curve decreases with the weight of the member state in aggregate employment and increases with that in output. Secondly, we show in two empirical applications that our calibration can provide informative priors for models to be estimated thanks to the Kalman filter.

Necessary Condition for Rationality and Adaptive Expectations Schemes

There are situations in which the old-fashioned adaptive expectation process seems to provide a good description of agents’ behavior (Chow, 2011). Unfortunately, this expectation scheme may not satisfy the necessary rationality condition (unconditional mean-zero error). This paper shows how to fix the problem introducing an error correction term.

A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock

Linear model is a general forecasting model and moving average technical index (MATI) is one of useful forecasting methods to predict the future stock prices in stock markets. Therefore, individual investors, stock fund managers, and financial analysts attempt to predict price fluctuation in stock markets by either linear model or MATI. From literatures, three major drawbacks are found in many existing forecasting models. First, forecasting rules mined from some AI algorithms, such as neural networks, could be very difficult to understand. Second, statistic assumptions about variables are required for time series to generate forecasting models, which are not easily understandable by stock investors. Third, stock market investors usually make short-term decisions based on recent price fluctuations, i.e., the last one or two periods, but most time series models use only the last period of stock price. In order to overcome these drawbacks, this study proposes a hybrid forecasting model using linear model and MATI to predict stock price trends with the following four steps: (1) test the lag period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and calculate the last n-period moving average; (2) use subtractive clustering to partition technical indicator values into linguistic values based on data discretization method objectively; (3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset, and optimize the FIS parameters by adaptive network; and (4) refine the proposed model by adaptive expectation models. The proposed model is then verified by root mean squared error (RMSE), and a ten-year period of TAIEX is selected as experiment datasets. The results show that the proposed model is superior to the other forecasting models, namely Chen's model and Yu's model in terms of RMSE.

Testing for adaptive signatures of amino acid alphabet evolution using chemistry space

Multidisciplinary consensus indicates that half of the genetically amino acids are likely to have been available on the prebiotic earth, which implies certain adaptive expectations for the relationship between those amino acids and later additions to the genetic code. Chemistry space a concept that translates molecules to corresponding points in multidimensional space provides a framework for investigating these relationships. We therefore developed three tests to explore these implications using chemistry space to quantify otherwise qualitative questions. All three of our tests individually, as well as combined, provide quantitative evidence to support an adaptive expansion of the genetically encoded amino acid alphabet from 10 prebiotically plausible (“early”) amino acids to the full set of 20 amino acids found within the standard genetic code. We present three logically independent, novel tests of the adaptive growth of the amino acid alphabet from a smaller, functionally cohesive alphabet of only 10 amino acids to the 20 amino acids of the standard genetic code. While similar tests in the past have compared the genetically encoded amino acids to an external context of amino acids that were not incorporated into the genetic code our tests focus on the internal context of the 20 genetically encoded amino acids and find strong support. Of particular note one of these tests for the first time moves beyond consideration of amino acids as monomers and begins to explore polypeptides by considering the chemistry space of amino acid dimers.

Adaptive Expectations with Bias Correction

There are situations in which the old-fashioned adaptive expectation process seems to provide a good description of agents’ behavior. Unfortunately, this expectation scheme may not satisfy the necessary rationality condition (unconditional mean-zero error). This paper shows how to fix the problem introducing an bias correction term and analyzes its effect on the dynamics of economic variables.

Uyarlanmış ve rasyonel beklentiler modellerinin doğrusal olmayan eşik regresyon modelleri çerçevesinde incelenmesi

The concept of Expectations is one of the most important developments in the last 50 years of Economic Theory. On the other hand, the popularity of nonlinear modeling techniques used in econometric studies has been increasing. In this study, Adaptive and Rational Expectation models are analyzed within the framework of nonlinear Threshold Regression models, and parameter estimation biases are provided with Monte Carlo simulation techniques. According to the results of this analysis, while the Adaptive Expectations model offers biased parameter estimation values based on nonlinear models, the Rational Expectation model offers unbiased parameter estimation values.

Adaptive Expectations and Dynamic Models for Network Goods

Abstract: In this work, we consider a utility function that is influenced by the value of network externality function at the consumer’s expected market size/share. Using this utility function, a market share adjustment function is introduced and is utilized to develop dynamic models of existing rational and static expectation processes. In addition, the role and scope of dynamic models of market share adjustment process are extended to the well-known adaptive expectation and its extension processes. The properties of equilibrium states of dynamic models are investigated which include location, stability, oscillation and the initial states in systematic and unified way. The most significant byproduct of presented results is that the properties of equilibrium states depend on the type of consumer expectation of a network good and the parameters of dynamic market share adjustment processes.

“Adaptive Expectations” of Milton Friedman and Monetarists and Phillips Curve; And the Comparison of them with Other Macroeconomic Schools

AbstractThe focus of this article is the “adaptive expectations hypothesis” of Milton Friedman and his analysis of short-run and long-run Phillips Curve. In order to analyze these contributions of M. Friedman more clearly, discussions about the Phillips Curve and different views about this issue according to various macroeconomic schools and information about the evolution of the Phillips Curve Analysis are given. In this sense, apart from Milton Friedman and Monetarists, Phillips Curve was analyzed in a successive order compatible with the history of discussion within Keynes and Keynesian economics, New Keynesian Economics and New Classical School operating with “rational expectations hypothesis”.

A high-order fuzzy time series forecasting model for internet stock trading

Recently, many fuzzy time series models have already been used to solve nonlinear and complexity issues. However, first-order fuzzy time series models have proven to be insufficient for solving these problems. For this reason, many researchers proposed high-order fuzzy time series models and focused on three main issues: fuzzification, fuzzy logical relationships, and defuzzification. This paper presents a novel high-order fuzzy time series model which overcomes the drawback mentioned above. First, it uses entropy-based partitioning to more accurately define the linguistic intervals in the fuzzification procedure. Second, it applies an artificial neural network to compute the complicated fuzzy logical relationships. Third, it uses the adaptive expectation model to adjust the forecasting during the defuzzification procedure. To evaluate the proposed model, we used datasets from both the Taiwanese stock index from 2000 to 2003 and from the student enrollment records of the University of Alabama. The results of our study show that the proposed model is able to obtain an accurate forecast without encountering conventional fuzzy time series issues.

Parent–Child Discrepancies in Educational Expectations: Differential Effects of Actual Versus Perceived Discrepancies

This study explored how discrepancies between parents' and adolescents' educational expectations influenced adolescents' achievement using a nationally representative, longitudinal sample of 14,041 students (14 years old at baseline). Actual discrepancies (i.e., those between parents' and adolescents' actual educational expectations) and perceived discrepancies (i.e., those between adolescents' perceptions of their parents' educational expectations and adolescents' own) were examined. Achievement was higher when parents actually held higher expectations than adolescents held or when adolescents perceived that their parents' expectations were lower than their own. In contrast, achievement was lower when parents actually held lower expectations than adolescents held or when adolescents believed that their parents' expectations exceeded their own. Implications for identifying adolescents at risk and promoting adaptive parent-child educational expectations are discussed.

The divergence between core and headline inflation: Implications for consumers’ inflation expectations

We show that US consumer inflation expectations are formed using a variant of adaptive expectations proposed by Mankiw et al. (2004). In particular, expectations behave differently when food and energy prices rise sharply relative to other prices. Using the recently proposed test of Homm and Breitung (2012), we analyze the 1982–2010 period and identify seven periods where the headline price index of personal consumption expenditures (PCEs) move explosively relative to the core PCE. During explosive periods, consumers rely more on past inflation in forming inflation expectations as compared to non-explosive periods. The importance of considering explosive periods in forming inflation expectations is also observed when CPI measures are considered. Finally, we find that during volatile periods inflation expectations are formed similarly as in normal periods. Our results indicate that the explosive behavior of food and energy prices should be taken into consideration when designing policies that aim to anchor inflation expectations.

A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX

Technical analysis is one of the useful forecasting methods to predict the future stock prices. For professional stock analysts and fund managers, how to select necessary technical indicators to forecast stock trends is important. Traditionally, stock analysts have used linear time series models for stock forecasting. However, the results would be in doubt when the forecasting problems are nonlinear. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models only use the last period of stock prices in forecasting. In this paper, the proposed hybrid model utilizes an adaptive expectation genetic algorithm to optimize adaptive network-based fuzzy inference system (ANFIS) for predicting stock price trends, and four proposed procedures are included in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a cited paper (Cheng et al., 2010); (2) use subtractive clustering to partition technical indicator values into linguistic values based on an objective data discretization method; (3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset and optimize the FIS parameters by adaptive network; and (4) refine the proposed model using the adaptive expectation model, which optimizes parameter by genetic algorithm. The effectiveness of the proposed model is verified with performance evaluations and root mean squared error (RMSE), and a 6-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) is selected as the experimental datasets. The experimental results have shown that the proposed model is superior to the three listing forecasting models (Chen's model, Yu's model, and Cheng et al.'s model) in terms of RMSE.