Shawn Ni

On the dynamic effects of oil price shocks: a study using industry level data ☆

Abstract This paper analyzes the effects of oil price shocks on demand and supply in various industries. The impulse responses of identified VAR models indicate that for industries that have a large cost share of oil, such as petroleum refinery and industrial chemicals, oil price shocks mainly reduce supply. In contrast, for many other industries, with the automobile industry being a particularly important example, oil price shocks mainly reduce demand. The paper suggests that oil price shocks influence economic activities beyond that explained by direct input cost effects, possibly by delaying purchasing decisions of durable goods.

An empirical analysis on the substitutability between private consumption and government purchases

Abstract This paper estimates substitutability of government purchases for private consumption using an optimal consumption model. The utility function features several types of nonseparabilities. The paper finds that GMM estimates are affected by the specification of nonseparability between private consumption and government purchases, by the restriction of time-separability, and by the measurement of real interest rates.

Noninformative priors and frequentist risks of bayesian estimators of vector-autoregressive models

In this study, we examine posterior properties and frequentist risks of Bayesian estimators based on several noninformative priors in vector autoregressive (VAR) models. We prove existence of the posterior distributions and posterior moments under a general class of priors. Using a variety of priors in this class we conduct numerical simulations of posteriors. We find that in most examples Bayesian estimators with a shrinkage prior on the VAR coefficients and the reference prior of Yang and Berger (Ann. Statist. 22 (1994) 1195) on the VAR covariance matrix dominate MLE, Bayesian estimators with the diffuse prior, and Bayesian estimators with the prior used in RATS. We also examine the informative Minnesota prior and find that its performance depends on the nature of the data sample and on the tightness of the Minnesota prior. A tightly set Minnesota prior is better when the data generating processes are similar to random walks, but the shrinkage prior or constant prior can be better otherwise.

Bayesian Estimates for Vector Autoregressive Models

This article examines frequentist risks of Bayesian estimates of vector autoregressive (VAR) regression coefficient and error covariance matrices under competing loss functions, under various noninformative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix, an asymmetric LINEX estimator does better overall than the posterior mean. No dominating estimator emerges for the error covariance matrix. We find that the choice of prior has a more significant effect on the estimates than the form of estimator. For the VAR regression coefficients, a shrinkage prior dominates a constant prior. For the error covariance matrix, Yang and Bergers reference prior dominates the Jeffreys prior. Estimation of a VAR using U.S. macroeconomic data yields significantly different estimates under competing priors.

Human capital and income taxation in an endogenous growth model

Abstract This paper examines the role of public expenditures on human capital formation using the Becker and Barro (1988) overlapping generations model with endogenous fertility. Here human capital is publicly produced and financed by income tax. In our numerical examples, if the government values the welfare of future generations as much as the dynastic head does, the optimal income tax rate for the financing of education is on the order of six to ten percent.

Bayesian analysis of vector-autoregressive models with noninformative priors

In this paper, we investigate the properties of Bayes estimators of vector autoregression (VAR) coefficients and the covariance matrix under two commonly employed loss functions. We point out that the posterior mean of the variances of the VAR errors under the Jeffreys prior is likely to have an over-estimation bias. Our Bayesian computation results indicate that estimates using the constant prior on the VAR regression coefficients and the reference prior of Yang and Berger (Ann. Statist. 22 (1994) 1195) on the covariance matrix dominate the constant-Jeffreys prior estimates commonly used in applications of VAR models in macroeconomics. We also estimate a VAR model of consumption growth using both constant-reference and constant-Jeffreys priors.

Inflation uncertainty and real economic activities

Inflation uncertainty is shown to have highly significant negative correlations with real activities if the uncertainty is measured by a state-dependent conditional variance model. The results are consistent with those obtained from survey data and cross-country data.

LONG-TERM OIL PRICE FORECASTS: A NEW PERSPECTIVE ON OIL AND THE MACROECONOMY

We examine how future real GDP growth relates to changes in the forecasted long-term average of discounted real oil prices and to changes in unanticipated fluctuations of real oil prices around the forecasts. Forecasts are conducted using a state-space oil market model, in which global real economic activity and real oil prices share a common stochastic trend. Changes in unanticipated fluctuations and changes in the forecasted long-term average of discounted real oil prices sum to real oil price changes. We find that these two components have distinctly different relationships with future real GDP growth. Positive and negative changes in the unanticipated fluctuations of real oil prices correlate with asymmetric responses of future real GDP growth. In comparison, changes in the forecasted long-term average are smaller in magnitude but are more influential on real GDP. Persistent upward revisions of forecasts in the 2000s had a substantial negative impact on real GDP growth, according to our estimates.

High Corruption Income in Ming and Qing China

We develop an economic model that explains historical data on government corruption in Ming and Qing China. In our model, officials extensive powers result in corrupt income matching lands share in output. We estimate corrupt income to be between 14 to 22 times official income resulting in about 22% of agricultural output accruing to 0.4% of the population. The results suggest that eliminating corruption through salary reform was possible in early Ming but impossible by mid-Qing rule. Land reform may also be ineffective because officials could extract the same rents regardless of ownership. High officials incomes and the resulting inequality may have also created distortions and barriers to change that could have contributed to Chinas stagnation over the five centuries 1400-1900s.

Who Benefits from Pension Enhancements

During the late 1990s public pension funds across the United States accrued large actuarial surpluses. The seemingly flush conditions of the pension funds led legislators in most states to substantially improve retirement benefits for public workers, including teachers. In this study we examine the benefit enhancements to the teacher pension system in Missouri. The enhancements resulted in large windfall gains for teachers who were close to retirement when the legislation was enacted. By contrast, novice teachers, and teachers who had not yet entered the labor force, were made worse off. The reason is that front-end contribution rates have been raised for current teachers to offset past liabilities accrued from the enhancements. Total teacher retirement compensation, net of contribution costs, is lower for young teachers today as a result of the enhancement legislation. Given sharp increases in pension costs in other states, this finding may generalize to young teachers in many other plans.