Modeling the expectations of inflation in the OLG model with genetic programming (original) (raw)
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Previous work has used neural networks to predict the rate of inflation in Taiwan using four measures of 'money' (simple sum and three Divisia measures). In this work a new approach is dev eloped that uses an evolutionary strategy as a predictive tool. This approach is simple to implement yet produces results that compare favourably with the neural network predictions.
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In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.
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Drawing on a considerable empirical literature that reveals persistent and endogenously time-varying heterogeneity in inflation expectations, this paper embeds two inflation forecasting strategiesone based on costly ex ante full rationality or perfect foresight, and the second based on costless ex ante bounded rationality or extrapolative trend-followingin a dynamic macroeconomic model. Drawing also on the significant empirical evidence that inflation forecast errors may have to exceed some threshold before agents abandon their previously selected inflation forecasting strategy, we describe agents as switching between inflation forecasting strategies according to evolutionarily satisficing learning dynamics. We find that convergence to a long-run equilibrium consistent with growth, unemployment and inflation at their natural levels may be achieved even when heterogeneity in inflation expectations (with predominance of the extrapolative trend-following foresight strategy) is an attractor of an evolutionarily satisficing learning dynamic perturbed by mutant agents. Therefore, in keeping with robust empirical evidence, heterogeneity in strategies to form inflation expectations (with prevalence of boundedly rational expectations) can be a stable long-run equilibrium.
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Previous work has used neural networks to predict the rate of inflation in Taiwan using four measures of 'money' (simple sum and three divisia measures). In this work we develop a new approach that uses an evolutionary strategy as a predictive tool. This approach is simple to implement yet produces results that are favourable with the neural network predictions. Computational results are given.
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XREAP Working Papers, 2018-04, 2018
The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents’ expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents' expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents' to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland.
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We study a dynamic equilibrium model in which agents have adaptive expectations and monetary authorities pursue an inflation target. We show how alternative monetary stabilization policies become more effective when fiscal constraints on deficits are implemented, although they are not binding at the equilibrium target. In particular, we show that the inflation target equilibrium can be locally, or even globally, stable for a large class of adaptive learning schemes. We also compare alternative stabilization policies in terms of their stability properties. Commonly postulated conditional Taylor-type rules tend to be dominated by other rules, such as an unconditional Friedman type.
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Recently, genetic programming has been proposed to model agents' adaptive behavior in a complex transition process where uncertainty cannot be formalized within the usual probabilistic framework. However, this approach has not been widely accepted by economists. One of the main reasons is the lack of the theoretical foundation of using genetic programming to model transition dynamics. Therefore, the purpose of this paper is two-fold. First, motivated by the recent applications of algorithmic information theory in economics, we would like to show the relevance of genetic programming to transition dynamics given this background. Second, we would like to supply two concrete applications to transition dynamics. The first application, which is designed for the pedagogic purpose, shows that genetic programming can simulate the non-smooth transition, which is difficult to be captured by conventional toolkits, such as differential equations and difference equations. In the second application, genetic programming is applied to simulate the adaptive behavior of speculators. This simulation shows that genetic programming can generate artificial time series with the statistical properties frequently observed in real financial time series.
Evolutionary computation for macroeconomic forecasting
Computational Economics, 2019
The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.
An Experimental Study on Expectations and Learning in Overlapping Generations Models
Studies in Nonlinear Dynamics & Econometrics, 2000
A plethora of models of learning has been developed and studied in macro-economic models in recent years. In this paper we will try to discriminate between these learning models by running laboratory experiments with incentivized human subjects. Participants predict inflation rates for 50 successive periods in a standard overlapping generations model and are rewarded on the basis of their forecasting accuracy. The information set for each participant contains the past inflation rates and the participant's own past predictions which, in turn, determine the actual inflation rate. We consider two treatments, with a low and a high level of monetary growth, respectively. We find that the level of convergence to the monetary steady state is significantly lower and volatility of inflation rates higher in the second treatment. Constant gain learning algorithms, such as adaptive expectations with a low adjustment parameter, seem to provide a better description of the experimental data than decreasing gain algorithms, such as (ordinary) least squares learning. Moreover, many participants switch between prediction strategies during the experiment on the basis of poor performance of their initial prediction strategy.