2advantageous. We are particularly concerned in this note by this latter type of cheating which involves initial resources. We point out that besides the intuitive explanation of
model. Many results (observed value, deterministic solution, computed mean among the replications, minimum and maximum of the stochastic solution, first relative differences of the observed, deterministic and mean stochastic values) are displayed and several empirical indicators of goodness of fit are computed: (1) the mean over the simulation period of the actual, deterministic and mean stochastic values; (2) the root mean square error (RMSE) of the deterministic and mean stochastic solutions; (3) the mean absolute percentage error (MAPE) of the deterministic and mean stochastic solutions; (4) Theil's inequality coefficient (U) of the deterministic and mean stochastic solutions; (5) the coefficients and standard errors of the regression (with intercept) of the observed values on the deterministic or mean stochastic solutions; (6) the coefficients and standard errors of the regression (without intercept) of the first relative differences of the observed values over those of the deterministic or mean stochastic solutions. The package is written in FORTRAN IV and ASSEMBLER 370 languages. It consists of approximately one thousand statements, in addition to the statements necessary to formalize the model. The required storage for the program is 60 kilobytes. A large work matrix is then required to hold intermediate and final results of the computation; its dimensions depend on the parameters specified in the input file (number of equations, simulation period, number of variables, etc.). The stochastic simulation of the Klein I model requires about 13 seconds of CPU time for 100 replications over 21 years of the sample period.
The conditions under which aggregate CES demand behavior is consistent with polytomous choice by micro demanders are explored. Several special cases are treated in which either the relative efficiency or the relative input price is assumed to vary randomly over the micro units. In each case it is shown that the random variable must have either a log-logistic (Burr) distribution function or a generalization thereof if the aggregate and the micro behavior are to be consistent.
The purpose of this paper is to examine within the context of a patieular U.S. exammetric model the sensitivity of fiscal policy effects to alternative assumptions about the behavior of the Federal Reserve. Five cases are considered, four in which Fed behavior is exogenous and one in which Fed behavior is endogenous. In each of the four exogenous cases the Fed is assumed to control a particular variable, which is then taken to be exogenous for purposes of the fiscal-policy experiments. For the endogetmus case an estimated equation explaining Fed behavior is added to the model. and the expanded mcdel is used to perform the experiments. The rewlts of some optimal control experiments are also reported in this paper. These latter experiments are designed to examine the sensitivity of optimal fiscal policies to alternative assumptions about Fed behavior. The main conclusion of this paper is that fiscal policy effects and optimal fiscal policies are quite sensitive to assumptions about the behavior of the Fed. 1. IN-cROD”cTION MOST EXAMINATIONS OF FISCAL POLICY EFFECTS in U.S. econometric models are based on the assumption that the behavior of the Federal Reserve (henceforth called the “Fed”) is exogenous, i.e., that the behavior of the Fed is not influenced by the state of the economy. The typical procedure is to assume that the Feds has control over a particular variable in the model and then to take this variable as exogenous for purposes of the fiscal policy experiments. An alternative procedure, if one believes that the behavior of the Fed is not exogenous, is to estimate an equation explaining Fed behavior (i.e., explaining the variable that the Fed is assumed to control), add this equation to the model, and use this expanded model to perform the fiscal policy experiments. The purpose of this paper is to examine within the context of a particular U.S. econometric model the sensitivity of fiscal policy effects to alternative assumptions about Fed behavior. Five cases are considered, four in which Fed behavior is exogenous and one in which Fed behavior is endogenous. In each of the four exogenous cases the Fed is assumed to control a particular variable, which is then taken to be exogenous for purposes of the fiscal policy experiments. The control variables in the four cases are: (1) the amount of government securities outstanding; (2) the money supply; (3) nonborrowed reserves; and (4) the bill rate. For the endogenous case an estimated equation explaining Fed behavior is added to the model, and the expanded model is used to perform the fiscal policy experiments. Section 2 contains a brief description of the econometric model used for purposes of this paper. The model, which is described in detail in Fair [9], is particularly suited for examining the effects of monetary and fiscal policies ‘The research described in this paper was financed by grant SOC77-03274 from the National
Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but asymptotically inefficient estimator, specification tests are devised for a number of model specifications in econometrics. Local power is calculated for small departures from the null hypothesis. An instrumental variable test as well as tests for a time series cross section model and the simultaneous equation model are presented. An empirical model provides evidence that unobserved individual factors are present which are not orthogonal to the included right-hand-side variable in a common econometric specification of an individual wage equation.
This paper provides necessary and sufficient conditions for it to be optimal to base decisions on estimates of the parameters that characterize a decision problem (e.g., profit maximization with an estimated price elasticity of demand). We show that the separation of parameter estimation from decision making generally yields lower utility than an integrated approach which takes account of estimation uncertainty. We evaluate the decision in the parameter estimation method and show that the resulting utility loss can be substantial. MANY ACTUAL DECISIONS are based on statistical estimates of parameters that help to characterize the decision environment. For example, a firm maximizing the expected utility of profit might find that its input and output decisions depend on unknown parameters of its demand function. Econometric estimates of such parameters might then be derived and utilized in making these decisions. The first purpose of this paper is to rigorously investigate whether it is correct to make decisions in this manner; in general, it is not. The second purpose is to investigate the decis'ion bias in decisions based on commonly employed parameter estimates. We will determine, for example, whether a price setting monopolist is mistakenly setting prices too high or too low when he bases his pricing decision on the maximum likelihood estimate of his demand equation. Finally, we provide a detailed numerical example to show that basing decisions on conventional parameter estimates can lead to large losses of utility. In Section 2, we introduce all notation, explain the procedure commonly used when basing decisions on values of unknown underlying parameters, and exhibit the decision-theoretic correct alternative procedure. When the optimal decisions under these two procedures are identical, we call the proper. We use the term summary value to refer not only to standard parameter estimates but to any single substituted for an unknown parameter in order to make decisions. This generalized concept is necessary because a that is appropriate for making decisions, in a sense defined below, need not have any of the properties of conventional parameter estimators. In Section 3, we derive under general assumptions necessary and sufficient conditions for the existence of proper values that are independent of the decision maker's utility function, U( ). This independence restriction is