A Monte Carlo test of preference independence

Theil (1987) recently developed distribution-free Monte Carlo testing procedures for demand homogeneity and Slutsky symmetry which do not use asymptotic theory. In this paper we introduce a similar procedure to test preference independence. The application of this procedure is illustrated with Dutch data.     

Testing for normality in linear regression models using regression and scale equivariant estimators

In this paper we provide a general solution to the problem of controlling the probability of a type I error in normality tests for the disturbances in linear regressions when using robust-regression residuals. We show that many classes of well-known robust regression estimators belong to the class of regression and scale equivariant estimators. It is these equivariance properties that are used to reduce the nuisance parameter space under the null, from which we develop Monte Carlo and Maximized Monte Carlo tests for the null of disturbance normality. Finally, we illustrate in a simulation experiment the potential power gains from using robust-regression residuals in testing this null hypothesis.     

More on the correct use of omnibus tests for normality

This Monte Carlo study compares the small sample properties of some commonly used omnibus and directional tests, based on the standardized third and fourth moments, for assessing the normality of random variables: the omnibus D'Agostino K² test and the directional components, and three versions of the Jarque–Bera test.     

Regime-switching purchasing power parity in Latin America: Monte Carlo unit root tests with dynamic conditional score

We suggest a Monte Carlo simulation-based unit root test of the purchasing power parity theory for Latin American countries. Under the null hypothesis, we use a Markov regime-switching (MS) model with unit root in the conditional location and MS volatility dynamics. Under the alternative hypothesis, the proposed test incorporates Markov regime-switching autoregressive moving average (MS-ARMA) plus MS volatility dynamics. Under both the null and alternative hypotheses, one of the volatility models estimated is Beta-t-EGARCH, which is a recent dynamic conditional score volatility model. We use data on real effective exchange rate time series for 14 Latin American countries. For each country, we estimate by Monte Carlo simulation the critical values of the unit root test. We provide an economic discussion of the unit root test results and also study the robustness of MS-ARMA plus MS volatility with respect to smooth transition autoregressive models with Fourier function.     

Optimization of Monte Carlo Procedures for Value at Risk Estimates

This paper proposes a methodology which improves the computational efficiency of the Monte Carlo simulation approach of value at risk (VaR) estimates. Principal components analysis is used to reduce the number of relevant sources of risk driving the portfolio dynamics. Moreover, large deviations techniques are used to provide an estimate of the minimum number of price scenarios to be simulated to attain a given accuracy. Numerical examples are provided and show the good performance of the methodolgy proposed.
(J.E.L.: C15, G1).     

Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications

Testing for cointegration in the presence of nonlinear adjustments or structural breaks is important for examining the equilibrium relationship among economic variables. It is known that standard cointegration tests perform poorly when a cointegration relationship has nonlinear adjustments or structural breaks. However, it is not clear how some cointegration tests allowing for nonlinearity perform under other classes of nonlinear cointegration models. This paper investigates which cointegration tests help detect a cointegration relationship with nonlinear adjustments or structural breaks. Our Monte Carlo simulation results demonstrate that the cointegration test with threshold adjustment generally has better power performance under most cointegration relationships with nonlinearity. We also provide empirical applications to the money demand and term structure of the U.S. interest rates. The empirical results show that the test allowing for threshold adjustment provides strong evidence of the cointegration relationships of money demand and the term structure of interest rates.     

Testing for serial correlation and random effects in a two-way error component regression model

In this paper, we test the existence of serial correlation and random effects in a two-way error component regression model with panel data. Under moment conditions alone, we suggest several easily implemented tests based on the parameter estimators for artificial autoregressions modeled by the differences in residuals. Under the null hypotheses, the tests for serial correlation are two-sided and asymptotically chi-square distributed, whereas those for random effects are one-sided, and are asymptotically standard normally distributed variables. Moreover, these methods can also be used similarly to construct tests for both serial correlation and individual effects jointly, whether or not time effects are present. The proposed tests are able to detect local alternatives that are distinct from the null at the parametric rate. Monte Carlo simulations and real data applications are carried out for purposes of illustration.     

Spurious deterministic seasonality and autocorrelation corrections with quarterly data: Further Monte Carlo results

Following recent work of Franses, Hylleberg and Lee (FHL), this paper analyses the consequences of fitting a deterministic seasonal model to a quarterly time series which can be (at least approximately) described by a seasonal unit root(s) model. Besides the distribution of the coefficient of determination, the empirical distributions of two commonly used statistics are also investigated through Monte Carlo experiments for small, moderately large and large samples. FHL's work is also extended allowing the possibility of residual autocorrelation corrections. The main conclusion that emerges from the results is that one should not try to measure the importance of deterministic seasonality nor test for its presence in the context of such (static) regression models, even when using some form of residual autocorrelation correction. A simple empirical application is provided to illustrate our results. First version received: July 1997/final version received: July 1998     

A Monte Carlo study of a factor analytical method for fixed-effects dynamic panel models

In a recent article Bai (2013a) proposes a new factor analytical method (FAM) for the estimation of fixed-effects dynamic panel data models, which has the unique and very useful property that it is asymptotically bias free. In this paper we provide Monte Carlo evidence of the good small-sample performance of FAM, that complement Bai’s theoretical study.     

Monte Carlo simulation of macroeconomic risk with a continuum of agents: the symmetric case

Summary. Suppose a large economy with individual risk is modeled by a continuum of pairwise exchangeable random variables (i.i.d., in particular). Then the relevant stochastic process is jointly measurable only in degenerate cases. Yet in Monte Carlo simulation, the average of a large finite draw of the random variables converges almost surely. Several necessary and sufficient conditions for such “Monte Carlo convergence” are given. Also, conditioned on the associated Monte Carlo -algebra, which represents macroeconomic risk, individual agents' random shocks are independent. Furthermore, a converse to one version of the classical law of large numbers is proved. Received: October 29, 2001; revised version: April 24, 2002 RID="*" ID="*" Part of this work was done when Yeneng Sun was visiting SITE at Stanford University in July 2001. An early version of some results was included in a presentation to Tom Sargent's macro workshop at Stanford. We are grateful to him and Felix Kübler in particular for their comments. And also to Marcos Lisboa for several discussions with Peter Hammond, during which the basic idea of the paper began to take shape. Correspondence to: P.J. Hammond     

Monte Carlo simulation of macroeconomic risk with a continuum of agents: the general case

In large random economies with heterogeneous agents, a standard stochastic framework presumes a random macro state, combined with idiosyncratic micro shocks. This can be formally represented by a random process consisting of a continuum of random variables that are conditionally independent given the macro state. However, this process satisfies a standard joint measurability condition only if there is essentially no idiosyncratic risk at all. Based on iteratively complete product measure spaces, we characterize the validity of the standard stochastic framework via Monte Carlo simulation as well as event-wise measurable conditional probabilities. These general characterizations also allow us to strengthen some earlier results related to exchangeability and independence. Parts of this work were done while Yeneng Sun was visiting Stanford University in July 2003, March–May 2005 and July 2006, and while Peter Hammond was visiting the National University of Singapore in March–April 2004. An early version was presented at the World Congress of the Econometric Society in 2005.     

One-directional adjacency matrices in spatial autoregressive model: A land price example and Monte Carlo results

In the context of spatial econometrics, we discuss the specification of one-directional effects, not mutual dependencies. Using an empirical study (a spatial autoregressive model of land price data in Fukui Prefecture, Japan) and Monte Carlo simulation results (contiguity matrices built based on regular lattices using the rook criteria), we show that spatial dependencies might not be recognized if such dependencies are assumed to be reciprocal.     

Measuring uncertainty of the euro area NAIRU: Monte Carlo and empirical evidence for alternative confidence intervals in a state space framework

This paper investigates uncertainty around point estimates of the euro area NAIRU in a state space framework. The relative accuracy of alternative measures of uncertainty for state space models are compared using Monte Carlo simulations. A direct bootstrap method yields confidence intervals with lower coverage probability than confidence intervals based on mean squared errors (MSE) approximations. The degree of uncertainty of the euro area NAIRU is estimated with a trivariate state space model. The direct bootstrap method shows the narrowest confidence interval compared with the MSE approximations. However, the wider intervals based on MSE approximations are narrow enough for the identification of some periods in time where observed unemployment and the NAIRU differ significantly.     

Purchasing power parity and country characteristics: Evidence from panel data tests

We examine long-run purchasing power parity (PPP) using panel data methods to test for unit roots in US dollar real exchange rates of 84 countries. We find stronger evidence of PPP in countries more open to trade, closer to the United States, with lower inflation and moderate nominal exchange rate volatility, and with similar economic growth rates as the United States. We also show that PPP holds for panels of European and Latin American countries, but not for African and Asian countries. Our findings demonstrate that country characteristics can help explain both adherence to and deviations from long-run PPP.     

Productivity growth and inflation in Europe: Evidence from panel cointegration tests

In this paper we investigate the productivity growth – inflation nexus in fifteen European countries over the period 1961–1999 using panel unit root and panel cointegration tests. Emphasis is placed on the distinction between long-run and short-run causality using recently developed tests appropriate for heterogeneous panel. The empirical results are relevant for the role of the EMU and the Treaty of Maastricht in catching up, real convergence, and the future growth prospects of Europe. The policy implications of the findings are discussed in detail.     

The performance analysis of chart patterns: Monte Carlo simulation and evidence from the euro/dollar foreign exchange market

We investigate the existence of chart patterns in the euro/dollar intra-daily foreign exchange market. We use two identification methods of the different chart patterns: one built on 5-min close prices only, and one based on both 5-min low and high prices. We look for twelve types of chart patterns and we study the detected patterns through two criteria: predictability and profitability. We run a Monte Carlo simulation to compute the statistical significance of the obtained results. We find an apparent existence of some chart patterns in the currency market. More than one half of detected charts present a significant predictability. Nevertheless, only two chart patterns imply a significant profitability which is however too small to cover the transaction costs. The second extrema detection method provides higher but riskier profits than the first one.

资本市场配置效率与劳动收入份额——来自股权分置改革的证据

本文利用股权分置改革作为自然实验来研究资本市场配置效率对中国上市公司劳动收入份额的影响。股权分置改革一方面会激励管理者通过"高薪引才"来应对更加激烈的外部竞争,从而提高劳动收入份额;另一方面,资本流通性的增强会减少"工资侵蚀利润"现象,从而降低劳动收入份额。实证结果表明,后者的作用占据主导,改革显著降低了上市公司的劳动收入份额。进一步分析表明,改革显著降低了国有企业的劳动收入份额,对非国有企业影响不显著;改革对劳动密集型行业影响显著,对资本密集型行业和技术密集型行业影响不显著;改革显著降低了普通雇员的劳动收入份额,但并没有影响管理者的劳动收入份额。     

Reduction of acidification from electricity — Generating industries in Taiwan by Life Cycle Assessment and Monte Carlo optimization

Life Cycle Assessment (LCA) is a rather common tool for reducing environmental impacts while striving for cleaner processes. This method yields reliable information when input data is sufficient; however, in uncertain systems Monte Carlo (MC) simulation is used as a means to compensate for insufficient data. The MC optimization model was constructed from environmental emissions, process parameters and operation constraints. The results of MC optimization allow for the prediction of environmental performance and the opportunity for environmental improvement. The case study presented here focuses on the acidification improvement regarding uncertain emissions and on the available operation of Taiwan's power plants. The boundary definitions of LCA were established for generation, fuel refining and mining. The model was constructed according to objective functional minimization of acidification potential, base loading, fuel cost and generation mix constraints. Scenario simulations are given the different variation of fuel cost ratios for Taiwan. The simulation results indicate that fuel cost was the most important parameter influencing the acidification potential for seven types of fired power. Owing to the low operational loading, coal-fired power is the best alternative for improving acidification. The optimal scenario for acidification improvement occurred at 15% of the fuel cost. The impact decreased from 1.39 to 1.24 kg SO₂-eq./MWh. This reduction benefit was about 10.5% lower than the reference year. Regarding eco-efficiency at an optimum scenario level of 5%, the eco-efficiency value was − 12.4 $US/kg SO₂-eq. Considering the environmental and economical impacts, results indicated that the ratio of coal-fired steam turbine should be reduced.