Capítulo 2. Antecedentes teóricos
2.2 Reactores de mezcla multifásica
2.2.3 Tecnologías convencionales de hidrogenación
The intensity of choice parameter controls the degree with which agents switch from one rule to the other when the performance of the forecasting rules change. In general we find that, as this parameter increases, volatility and inertia tend to increase. This is illustrated in figure 25. The upper panel shows the volatility of output and inflation as a function of the intensity of choice parameter. We observe a clear positive relation.
The lower panel shows how the autocorrelation coefficients increase when intensity of choice is increased.
We conclude that as agents react more forcefully to changes in performance of their forecasting rules, the volatility of output and inflation and their inertia increases.
Figure 24: Standard deviation and autocorrelation of output gap and inflation
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0.005
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0.005
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
Note: the standard deviations and autocorrelation coefficients are the averages obtained from simulating the model 1000 times, each time over 1000 periods.
8. Conclusion
DSGE-models provide a coherent framework of macroeconomic analysis. This coherence is brought about by restricting acceptable behaviour of agents to dynamic utility maximization and rational expectations. These features explain the intellectual appeal of these models and their recent success in academic circles and among policymakers.
The problem of the DSGE-models (and more generally of macroeconomic models based on rational expectations) is that they assume extraordinary cognitive capabilities of individual agents. Recent developments in other disciplines including psychology and brain science document that individual agents struggle with limited cognitive abilities, restricting their capacity to understand the world. As a result, individual agents use small bits of information and simple rules to guide their behaviour.
We have used these new insights to extend the DSGE-model framework to an environment in which agents use simple rules to forecast output and inflation. In order to provide discipline in the use of these rules we have introduced a mechanism that allows for the selection of those rules that are more profitable than others.
The ensuing “heuristic model” produces a number of results that distinguishes it from the rational expectations models. First, the heuristic model is capable of generating endogenous cycles based on waves of optimism and pessimism. This dynamics is akin to what Keynes called animal spirits. Second, in contrast to the rational expectations DSGE-models the inertia in output and prices is generated internally in the model, instead of being “imported”. Third, due to its non-linearity, the heuristic model produces a degree of uncertainty about the transmission of monetary policy shocks that is different from the uncertainty obtained in DSGE-models. In the latter linear models, uncertainty about the effects of monetary policy shocks arises only because of the lack of precision in the estimation of the structural parameters of the model. In the heuristic model there is an additional dimension to uncertainty. This is that the same policy shock can have different effects depending on the state of the economy, including the degree of optimism and pessimism agents have about the future. True, DSGE-models can potentially produce similar results. However, these have routinely been excluded by linearizing an otherwise non-linear model.
The success of DSGE-model has much to do with the story it tells about how the macroeconomy functions. This is a story in which rationality of superbly informed and identical agents reigns. Shocks from the outside occur continuously forcing these agents to re-optimize all the time. Unfortunately and inexplicably, the outside world imposes restrictions on this behaviour creating distortions and departures from optimality. It also generates cycles in output and inflation. This in turn creates a stabilizing responsibility for the central bank.
We have questioned this story by presenting an alternative one. This is a story in which agents do not understand the model well, and use a trial and error strategy to discover its underlying logic. Such a model generates cycles endogenously. Thus in contrast with the DSGE-world where the shocks come from outside, in the heuristic world some shocks are generated within the model.
There is another dimension in the difference between the two models. In his famous AER-article Hayek(1945) stressed that individuals have only very small parts of the available information in their brains. No individual can ever hope to understand and to process the full complexity of the world in which he lives. That’s why markets are so important. They are vehicles that efficiently aggregate information that is spread around in society. Our model is in the logic of this Hayekian view. This is the logic that produces cycles when markets aggregate the different and incomplete pieces of information individuals use when forecasting the future.
The research presented in this paper should be considered to be preliminary. In order to be convincing as an alternative modeling strategy, a rigorous empirical evaluation of the model will be necessary, whereby the predictions of the model are confronted with the data. In addition, the menu of heuristics which is extremely small in this paper will have to be broadened so that the selection of the “fittest” rules can occur using a wider pool of possible rules.
Appendix A: parameter values of the calibrated model
Heuristic model
pstar = 0; % the central bank's inflation target
a1 = 0.5; %coefficient of expected output in output equation a2 = -0.2; %a is the interest elasticity of output demand
b1 = 0.5; %b1 is coefficient of expected inflation in inflation equation b2 = 0.05; %b2 is coefficient of output in inflation equation
c1 = 1.5; %c1 is coefficient of inflation in Taylor equation c2 = 0.5; %c2 is coefficient of output in Taylor equation c3 = 0.5; %interest smoothing parameter in Taylor equation g = 0.01; %output forecasts optimists
gamma = 10000; %switching parameter gamma in Brock Hommes sigma1 = 0.005; %standard deviation shocks output
sigma2 = 0.005; %standard deviation shocks inflation sigma3 = 0.005; %standard deviation shocks Taylor
rho=0.5; %rho measures the speed of declining weights omega in mean squares errors
Rational model
pstar = 0; % the central bank's inflation target
a1 = 0.5; %coefficient of expected output in output equation a2 = -0.2; %a is the interest elasticity of output demand
b1 = 0.5; %b1 is coefficient of expected inflation in inflation equation b2 = 0.05; %b2 is coefficient of output in inflation equation
c1 = 1.5; %c1 is coefficient of inflation in Taylor equation c2 = 0.5; %c2 is coefficient of output in Taylor equation c3 = 0.5; %interest smoothing parameter in Taylor equation sigma1 = 0.005; %standard deviation shocks output
sigma2 = 0.005; %standard deviation shocks inflation sigma3 = 0.005; %standard deviation shocks Taylor
Appendix B:
w e ight output optimists
0 50 100 150 200 250 300
Appendix C: Mean impulse responses to negative supply shock
100 110 120 130 140 150 160 170 180 190 200
-8 -6 -4 -2 0 2 4x 10-3
Time
Level
me an impulse re sponse output to ne ga tive supply shock
100 110 120 130 140 150 160 170 180 190 200
-4 -2 0 2 4 6 8x 10-3
Time
Level
mean impulse response inflation to negative supply shock
100 110 120 130 140 150 160 170 180 190 200
-0.01 -0.005 0 0.005 0.01 0.015 0.02
Time
Level
mea n impulse re sponse inte re st ra te to nega tive supply shock
Appendix D: Mean impulse responses to positive demand shock
100 110 120 130 140 150 160 170 180 190 200
-2 -1 0 1 2 3 4 5x 10-3
Time
Level
me a n impulse re sponse output to positive de ma nd shock
100 110 120 130 140 150 160 170 180 190 200
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5x 10-3
Time
Level
mean impulse response inflation to positive demand shock
100 110 120 130 140 150 160 170 180 190 200
-5 -4 -3 -2 -1 0 1 2 3 4 5x 10-3
Time
Level
me an impulse re sponse inte re st ra te to positive de ma nd shock
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