ساخت و برآورد یک مدل بهینه واقع گرایانه از سیاست های پولی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|24536||2000||31 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Monetary Economics, Volume 45, Issue 2, April 2000, Pages 329–359
A dynamic stochastic general-equilibrium (DSGE) model with real and nominal, both price and wage, rigidities succeeds in capturing some key nominal features of U.S. business cycles. Additive technology shocks, as well as multiplicative shocks, are introduced and shown to be crucial. Monetary policy is specified as an interest rate targeting rule following developments in the structural vector autoregression (VAR) literature. The interaction between real and nominal rigidities is essential to reproduce the liquidity effect of monetary policy. The model is estimated by maximum likelihood on U.S. data, and its fit is comparable to that of an unrestricted first-order VAR. Besides producing reasonable impulse responses and second moments, this model replicates a feature of U.S. business cycles, never captured by previous research with DSGE models, that an increase in interest rates predicts a decrease in output two to six quarters in the future.
The comovement of monetary and real aggregates and the inverse relation between the movements of money growth and nominal interest rates are two prominent nominal features of business cycles in the United States and many other countries.1 In this paper, we will try to explain these features through the two channels of monetary policy – the output effect and the liquidity effect. The output effect, defined here as the positive response of aggregate output to expansionary monetary policy, has been a key question for economists who have searched for a monetary explanation of the business cycle. The liquidity effect, defined as the decrease in interest rates in response to monetary expansion, has been an important issue in empirical macroeconomics.2 Stimulated by Kydland and Prescott (1982) and Long and Plosser (1983), dynamic stochastic general-equilibrium (DSGE) models have become a useful tool for macroeconomic analysis, especially for business cycle analysis. Previous work using a flexible-price competitive DSGE models have provided a reasonable description of the data on real variables. One stream of recent work incorporates outside money in a flexible-price competitive DSGE model. Money is introduced in a cash-in-advance economy by Cooley and Hansen (1989) to study the effects of inflation. Sims (1994) introduces money through a transaction-cost framework. Using a simple money-in-the-utility-function model without niminal rigidities, Benassy (1995) shows analytically that the dynamics of the real variables are exactly the same as those in a model without money. Such models do not provide a good description of the money–output correlation and cannot reproduce reasonable impulse responses to the shocks in monetary policy, because of the following generic implication. If money growth displays a positive persistence, then shocks to the growth rate of money drive output down and nominal interest rate up through an anticipated inflation effect. To generate the output effect, nominal rigidities are introduced into DSGE models.3 There are two alternative ways of introducing nominal rigidities. The first is to replace an equilibrium equation matching demand and supply with an equation describing the determination of price and/or wage. The staggered contract theory of Fischer (1977) is usually adopted. Cho (1993), Cooley and Hansen (1995), Benassy (1995) and Cho and Cooley (1995) study the implications of nominal wage contracts for the transmission of monetary shocks, and Yun (1994) explains the comovement of inflation and output with a staggered multi-period price setting model. Leeper and Sims (1994) also experiment with both price and wage rigidities by postulating equations describing price and wage movements. Another way of introducing nominal rigidities is via adjustment costs. For an economic agent to have control over the price and/or wage, some form of imperfect competition is needed. Following Blanchard and Kiyotaki (1987), the monopolistic competition framework has been widely used. Hairault and Portier (1993) show that monetary shocks are necessary to reproduce some stylized facts of business cycles, and Rotemberg (1996) presents a model that is consistent with a variety of facts concerning the correlation of output, prices and hours of work. All the above models of nominal rigidities do indeed generate the output effect of monetary policy. However, as shown in Kimball (1995), the presence of nominal rigidities by themselves does not produce the liquidity effect. To generate the liquidity effect, I assume real rigidities in the form of adjustment costs for capital. This conjecture is found in King (1991) and implemented in Novales (1992), Dow (1995), and King and Watson (1996). This paper constructs and estimates a DSGE model of monetary policy. It tries to build a realistic and usable model, in the spirit of Leeper and Sims (1994). The model features the two effects of monetary policy.4 It also captures many interesting U.S. business cycle facts. Section 2 presents the model and the solution. Households maximize their utility and firms maximize their profit. Government action is characterized by monetary and fiscal policies. There are two real and two nominal shocks affecting the economy. Section 3 illustrates the model's qualitative and quantitative properties. Because the time-series and policy implications critically depend on the choice of the parameters, maximum likelihood estimates are computed. The impulse responses evaluated at the estimated parameters feature the two effects of monetary policy: the output and the liquidity effects. The fit of the model and its variance decompositions are comparable to those from vector autoregression (VAR) models. Cyclical implications are considered by comparing the second moments of the model with those of the data. Finally, one of the challenges in King and Watson (1996) is satisfactorily met. According to the estimated model, an increase in interest rates in the current period predicts a decrease in real economic activity two to six quarters in the future. None of their three models captures this feature of the U.S. business cycle. Section 4 concludes.
نتیجه گیری انگلیسی
Previous work using a flexible-price, competitive DSGE model has provided reasonable descriptions of the data on real variables. However, such work has not captured the nominal features of business cycles adequately. Typically, expansionary monetary policy produces neither a positive response of aggregate output nor a negative response of interest rates. This paper aims at filling this gap with a DSGE model extended to allow for real and nominal rigidities. The introduction of additive technology shocks and the endogeneity of money are important in analyzing monetary business cycles. In order to select the magnitude of the effects of monetary policy in a data-dependent manner, the model is estimated using maximum likelihood on U.S. data. The estimated model exhibits reasonable impulse responses and its forecasts produce second moments similar to those of the data. As a by-product, it also reproduces the fact that an increase in interest rates in the current period predicts a decrease in real economic activity two to six quarters in the future, a feature of U.S. business cycles which has never been captured by previous research using DSGE models. It would be interesting to estimate the model for a sub-period and see how well the estimated model explains the out-of-sample data. This is particularly interesting since monetary policy regimes are said to have changed several times, e.g. the October 1979 Volker disinflation. Fig. 2 shows that the model's implications for money and the price level fall apart after 1990. It would be more helpful to randomize the policy regimes. For further research, adding more structure into the model and using more data for estimation are likely to produce a better-behaving estimated model. Since this paper deals with only four variables as the data and three out of the four are nominal variables, it would be particularly interesting to add more real variables in the estimation and to see how well an augmented model could explain the additional variables. This exercise would evaluate additive technology shocks in explaining real variables. Introduction of additive technology shocks, which is a key factor in monetary business cycles as shown in this paper, could help or hurt the behavior of real variables.