Research Database

One of the more significant developments in econometric modeling over the past decade has been the invention of the forecasting technique known as Bayesian vector autoregression (BVAR). This paper provides a detailed description of the process of specifying a BVAR model of quarterly time series on the U.S. macroeconomy. The postsample forecasting performance of the model is evaluated at an informal level by comparing the model's performance to certain naive forecasting methods, and is evaluated at a formal level by means of efficiency tests. Although the null hypothesis of efficiency is rejected for the model's forecasts, the accuracy of the model exceeds that of naive forecasting methods, and seems comparable to that of commercial forecasting firms for early quarter forecasts.

We add a nominal tax system to a sticky-price monetary business cycle model. When nominal interest income is taxed, the coefficient on inflation in a Taylor-type monetary policy rule must be significantly larger than one in order for the model economy to have a determinate rational expectations equilibrium. When depreciation is treated as a charge against taxable income, an even larger weight on inflation is required in the Taylor rule in order to obtain a determinate and stable equilibrium. These results have obvious implications for assessing the historical conduct of monetary policy.

This paper presents a method to perform likelihood-based inference in nonlinear dynamic equilibrium economies. This type of models has become a standard tool in quantitative economics. However, existing literature has been forced so far to use moment procedures or linearization techniques to estimate these models. This situation is unsatisfactory: moment procedures suffer from strong small samples biases and linearization depends crucially on the shape of the true policy functions, possibly leading to erroneous answers. We propose the use of Sequential Monte Carlo methods to evaluate the likelihood function implied by the model. Then we can perform likelihood-based inference, either searching for a maximum (Quasi-Maximum Likelihood Estimation) or simulating the posterior using a Markov Chain Monte Carlo algorithm (Bayesian Estimation). We can also compare different models even if they are nonnested and misspecified. To perform classical model selection, we follow Vuong (1989) and use the Kullback-Leibler distance to build Likelihood Ratio Tests. To perform Bayesian model comparison, we build Bayes factors. As an application, we estimate the stochastic neoclassical growth model.

Trend-cycle decomposition has been problematic in equilibrium business cycle research. Many models are fundamentally based on the concept of balanced growth, and so have clear predictions concerning the nature of the multivariate trend that should exist in the data if the model is correct. But the multivariate trend that is removed from the data in this literature is not the same one that is predicted by the model. This is understandable, because unexpected changes in trends are difficult to model under a rational expectations assumption. A learning assumption is more appropriate here. We include learning in a standard equilibrium business cycle model with explicit growth. We ask how the economy might react to the important trend-changing events of the postwar era in industrialized economies, such as the productivity slowdown, increased labor force participation by women, and the "new economy" of the 1990s. This tells us what the model says about the trend that should be taken out of the data before the business cycle analysis begins. Thus we use learning to address the trend-cycle decomposition problem that plagues equilibrium business cycle research. We argue that a model-consistent approach, such as the one we suggest here, is necessary if the goal is to obtain an accurate assessment of an equilibrium business cycle model.

Recent developments in business cycle theory are reviewed. The principal finding is that the growth model, which was developed to account for the secular patterns in important economic aggregates, displays the business cycle phenomena once it incorporates the observed randomness in the rate of technological advance. The amplitudes and serial correlation properties of fluctuations in output and employment that the growth model predicts match those historically experienced in the United States. Further, the model continues to display the growth facts it was developed to explain.

Randomness in individual discovery disperses productivities, whereas learning from others keeps productivities together. Long-run growth and persistent earnings inequality emerge when these two mechanisms for knowledge accumulation are combined. This paper considers an economy in which those with more useful knowledge can teach others, with competitive markets assigning students to teachers. In equilibrium, students with an ability to learn quickly are assigned to teachers with the most productive knowledge. This sorting on ability implies large differences in earnings distributions conditional on ability, as shown using explicit formulas for the tail behavior of these distributions.

In this study we contrast fixed and floating exchange rate regimes in a dynamic general equilibrium model. We find that the fundamental difference in the regimes is in the courses they imply for monetary policies. Because of policy coordination requirements, a tighter monetary policy needed to maintain a fixed exchange rate may necessitate a tightening in budget policy as well. We show that under some initial conditions voters or a social planner will favor one regime, but under other conditions they will favor the other. However, the choices of voters and a social planner are almost diametrically opposed.

We construct a model where capital competes with fiat money as a medium of exchange, and we establish conditions on fundamentals under which fiat money can be both valued and socially beneficial. When the socially efficient stock of capital is too low to provide the liquidity agents need, they overaccumulate productive assets to use as media of exchange. When this is the case, there exists a monetary equilibrium that dominates the nonmonetary one in terms of welfare. Under the Friedman Rule, fiat money provides just enough liquidity so that agents choose to accumulate the same capital stock a social planner would.

Using simulations from a multicountry neoclassical growth model, we analyze several post-Brexit scenarios. First, the United Kingdom unilaterally imposes tighter restrictions on FDI and trade from other EU nations. Second, the European Union retaliates and imposes the same restrictions on the UK. Finally, the United Kingdom reduces restrictions on other nations during the post-Brexit transition. Model predictions depend crucially on the policy response of multinationals’ investment in technology capital, accumulated know-how from investments in R&D, brands, and organizations used simultaneously in their domestic and foreign operations.