Neoclassical versus Frontier Production Models? Testing for the Skewness of Regression Residuals*

The empirical literature on production and cost functions is divided into two strands. The neoclassical approach concentrates on model parameters, while the frontier approach decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose a theoretical justification for the skewness of the inefficiency term, arguing that this skewness is the key testable hypothesis of the frontier approach. We propose to test the regression residuals for skewness in order to distinguish the two competing approaches. Our test builds directly upon the asymmetry of regression residuals and does not require any prior distributional assumptions.     

Model Selection in Spatial Autoregressive Models with Varying Coefficients

Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.     

Bootstrap Testing in Nonlinear Models

Bootstrap testing of nonlinear models normally requires at least one nonlinear estimation for every bootstrap sample. We show how to reduce computational costs by performing only a fixed, small number of Newton or quasi-Newton steps for each bootstrap sample. The number of steps is smaller for likelihood ratio tests than for other types of classical tests and smaller for Newton's method than for quasi-Newton methods. The suggested procedures are applied to tests of slope coefficients in the tobit model and to tests of common factor restrictions. In both cases, bootstrap tests work well, and very few steps are needed.     

The Hot Versus Cold Effect in a Simple Bargaining Experiment

In this paper, the strategy method's impact on behavior in sequential bargaining games is investigated. Besides the decision procedure (hot versus cold), we varied the second mover punishment costs (high versus low). Significant impacts of both treatment variables were observed. For example, second movers punished significantly more often in the hot version of the low cost game. Furthermore, first mover behavior was significantly different in the hot and cold versions of both games. In the hot games, first mover behavior suggests an expectation of decreased rewards and/or punishments from second movers. We observed, however, no decrease in reward and an increase in punishment. The hot cold variable only informs first movers that the decision procedure used by second movers has changed. Therefore, first mover behavior must be shaped by their perceived assessment concerning how second movers make decisions. We argue that first mover behavior can be explained by the interaction of two well-known psychological effects: the consensus and positive self-image effects.     

A Frequency Decomposition of Approximation Errors in Stochastic Discount Factor Models

This article extends the work of Hansen and Jagannathan by showing how to decompose approximation errors in stochastic discount factor models by frequency. This decomposition is applied to a number of consumption-based discount factor models in order to investigate how well they fit at low frequencies. There is some evidence of improved fit at low frequencies, but only in models with high degrees of risk aversion. In models with low degrees of risk aversion, approximation errors at low frequencies are just as severe as those at high frequencies.     

On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets

The examination for the possible existence of predictive power in the moving average trading rule has been used extensively to test the hypothesis of weak form market efficiency in capital markets. This work focuses mainly on the study of the variation of the moving average (MA) trading rule performance as a function of the length of the longer MA. Empirical analysis of daily data from NYSE and the Athens Stock Exchange reveal high variability of the performance of the MA trading rule as a function of the MA length and on some occasions the series of successive trading rule total returns is non‐stationary. These findings have direct implications in weak form market efficiency testing. Indeed, given this high variability of the performance of the MA trading rule, by just finding out that trading rules with some specific combinations of MA lengths can or cannot beat the market, as is the case in most of the published work thus far, is not enough evidence for or against the existence of weak form market efficiency. Results also show that on average in about three out of four cases trading rule signals are false, a fact that leaves a lot of space for improved trading rule performance if trading rule signals are combined with other information (e.g. filters, or volume of trade). Finally, some evidence of enhanced trading rule performance for the shorter MA lengths was found. This enhanced performance is partly attributed to the higher probability that a trading rule signal is not a whipsaw, as well as to the larger number of days out‐of‐the‐market which are associated with shorter MA lengths.     

计量经济学应用研究的总体回归模型设定

本文从计量经济学应用研究中总体回归模型设定的任务和目标出发,通过对总体模型设定的研究目的导向、经济学理论导向、数据关系导向的分析与评价,提出总体模型设定的唯一性、一般性、现实性和统计检验必要性原则;最后,提出总体回归模型设定的"经济主体动力学关系导向"原则和框架。     

Testing the Induced Innovation Hypothesis in a Cointegrating Regression Model

This study proposes a method for testing the induced innovation hypothesis using a cointegrating regression model. An error correction model of the share equations derived from a translog cost function model is used for the estimation. Cointegration among the variables ensures the existence of the innovation possibility curve. An error correction term of the model represents the technological change process. A difference in the elasticities of factor substitution along the isoquant curve and the innovation possibility curve implies the induced technological innovation. An empirical application is also carried out using time-series data of rice production in Japan.
JEL Classification Numbers: O33, Q10, C32.     

Testing Commitment Models of Monetary Policy in China

In the past decade Chinese inflation was not high on average, but it was quite volatile. Back in the 1980s and 1990s, high inflation was a very real problem. What explains the inflationary dynamics in China? In particular, does monetary policy account for the substantial run-ups of inflation, followed by the equally substantial dis-inflation? In the absence of commitment technologies, the monetary authorities may create surprise inflation to achieve higher growth, while private agents would anticipate that and adjust their decisions accordingly, leading to accelerated inflation without a real impact. Do these types of simple time-inconsistency models of monetary policy explain the dynamic pattern of inflation in China? I show that the long-run and short-run restrictions imposed by discretionary policy, when the time-inconsistent policymaker has a desire to push output above potential, are largely rejected by the data. The estimates of the inflation bias under discretion when the policymaker is asymmetrically averse to recessions are not statistically significant either. The analysis contributes to the understanding of Chinese monetary policy and its inflationary implications and also points to the need of further investigation of inflationary behavior during the economic transition.     

A Bayesian Approach To Testing for Markov-Switching in Univariate and Dynamic Factor Models

Though Hamilton's (1989) Markov-switching model has been widely estimated in various contexts, formal testing for Markov-switching is not straightforward. Univariate tests in the classical framework by Hansen (1992) and Garcia (1998) do not reject the linear model for GDP. We present Bayesian tests for Markov-switching in both univariate and multivariate settings based on sensitivity of the posterior probability to the prior. We find that evidence for Markov-switching, and thus the business cycle asymmetry, is stronger in a switching version of the dynamic factor model of Stock and Watson (1991) than it is for GDP by itself.     

Bootstrap and Pretest J-Type Non-Nested Tests for Orthogonal Regression Models: Some Monte Carlo Evidence

This paper compares the size and power of two J-type tests for weakly correlated or nearly orthogonal non-nested regression models: a bootstrap and a pretest test. The latter seems to outperform the former in terms of its size characteristics, especially when the alternative model has more non-nested regressors and the orthogonality between the two sets of regressors is severe. The bootstrap test does better in terms of power.

     

Deriving and Testing Efficient Estimates of WTP Distributions in Destination Choice Models

Estimation of welfare measures is often a dominant driver in the empirical literature on nonmarket valuation. To this end, qualitative choice models based on random utility theory have been widely employed in outdoor recreation studies. A frequent goal of applied studies has been the estimation of welfare changes associated with site attribute changes at recreation sites in order to inform regulatory policy and resource management. We review the evolution of the methodology of random utility theory in this field with a focus on taste heterogeneity models and then focus on the recent proposal of specifying utility in the WTP-space (Train K, Weeks M (2005) Discrete choice models in preference space and willing-to-pay space. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics, chapter 1. Springer, Dordrecht, pp 1–16). Our empirical application is on outdoor alpine recreation data. We emphasize the efficiency and direct testing that using the maximum simulated likelihood estimator affords to practitioners using the WTP-space approach, and illustrate these with examples.     

A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in this paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate- or large-sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. We provide results concerning the large and small sample properties of this estimator.     

A Logical Framework for Coalitional Effectivity in Dynamic Procedures

Effectivity frames are introduced as a model of what groups of agents can achieve by coordinated action in dynamic processes such as extensive games with or without simultaneous moves. Local effectivity is distinguished from different kinds of global and terminal effectivity, respectively what groups of players can maintain throughout and what they can achieve eventually. Examples are provided of how effectivity frames can be used as a tool (1) to model complex multi‐agent processes such as voting procedures, (2) to investigate the interplay between local and global properties of these dynamic procedures, and (3) to examine whether a particular effectivity function can be implemented or realized by a dynamic procedure of a specific type. Finally, a modal logic for local and global coalitional effectivity is presented, and it is shown how, for example, a realization question can be translated into this logical framework yielding a satisfiability problem.     

二阶自回归效应有限时间最优相对保费稳健性

在汽车保险奖惩系统相对保费研究中,需要考虑随机效应的动态异质性。在假设随机效应是一个二阶自回归随机序列的条件下,李俊海、赵振英、常沙沙(2011)给出了有限时间下最优相对保费计算公式,但是没有研究该公式的稳健性。在相同条件下,可以证明该文保费公式的稳健性。     

Errors in variables in a random-effects probit model for panel data

In this paper we consider a probit model estimated on panel data with random person-specific effects when an independent variable is measured with error. For normally distributed measurement error, a structural maximum-likelihood estimate from a likelihood that is conditional on the observed independent variable but marginal on the incidental parameter is studied. A minimum-distance estimator without the use of external instruments is proposed.     

Testing for Granger non-causality in heterogeneous panels

This paper proposes a very simple test of Granger (1969) non-causality for heterogeneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, the semi-asymptotic distribution of the average statistic is characterized for a fixed T sample. A standardized statistic based on an approximation of the moments of Wald statistics is hence proposed. Third, Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence.