A Teacher's Opinions on Teaching English Composition

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Nonstationary Time-Series Modeling versus Structural Equation Modeling: With an Application to Japanese Money Demand

The issues of identification, estimation, and statistical inferences of nonstationary time series and simultaneous equation models are reviewed. It is shown that prior information matters and the advantage of dichotomization of the traditional autoregressive distributed lag model into the long-run equilibrium relation and the short-run dynamic adjustment process as an empirical modeling device may be exaggerated. A Japanese money demand study is used to illustrate that a direct approach yields a more stable long-run and short-run relationship and has better predictive power than the approach of letting the data determine the long-run relationship and modeling the short-run dynamics as an adjustment of the deviation from its equilibrium position.

Aggregate and Household Demand for Money: Evidence from the Public Opinion Survey on Household Financial Assets and Liabilities

We use data from the Public Opinion Surveys on Household Financial Assets and Liabilities from 1991 to 2002 to investigate the issues of unobserved heterogeneity among cross-sectional units and stability of the Japanese aggregate money demand function. Conditions that permit individual data and aggregate data to be modeled under one consistent format are given. Alternative definitions of money are explored through year-by-year cross-sectional estimates of the Fujiki and Mulligan (1996b) household money demand model. We find that using M3 appears to be broadly consistent with time-series estimates using the aggregates constructed from the micro data. The results appear to support the existence of a stable money demand function for Japan. The estimated income elasticity for M3 is about 0.68, and five-year bond interest rate elasticity is about -0.124.Demand for money; Aggregation; Heterogeneity

Random Coefficient Panel Data Models

This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficient models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provides a review of heterogeneous dynamic panels, testing for homogeneity under weak exogeneity, simultaneous equation random coefficient models, and the more recent developments in the area of cross-sectional dependence in panel data models.random coefficient models, dynamic heterogeneous panels, classical and Bayesian approaches, tests of slope heterogeneity, cross section dependence

Efficient Estimation of a Dynamic Error-Shock Model

This paper is concerned with the estimation of the parameters in a dynamic simultaneous equation model with stationary disturbances under the assumption that the variables are subject to random measurement errors. The conditions under which the parameters are identified are stated. An asymptotically efficient frequency-domain class of instrumental variables estimators is suggested. The procedure consists of two basic steps. The first step transforms the model in such a way that the observed exogenous variables are asymptotically orthogonal to the residual terms. The second step involves an iterative procedure like that of Robinson [13].

Design and application of stationary phase combinatorial promoters

Current bacterial synthetic circuits rely on the fast dilution and high protein expression that occurs during exponential phase. However, constant exponential phase is both difficult to ensure in a lab environment and almost certainly impractical in any natural setting. Here, we characterize the performance of 13 E. coli native σ38 promoters, as well as a previously identified σ38 consensus promoter. We then make tetO combinatorial versions of the three strongest promoters to allow for inducible delayed expression. The design of these combinatorial promoters allows for design of circuits with inducible stationary phase activity that can be used for phase-dependent delays in dynamic circuits or spatial partitioning of biofilms

Is There a Stable Money Demand Function under the Low Interest Rate Policy? A Panel Data Analysis

We use annual Japanese prefecture data on income, population, demand deposits, and saving deposits from 1992 to 1997 to investigate the issue of whether there exists a stable money demand function under the low interest rate policy. The evidence appears to support the contention that there does exist a stable money demand function with long-run income elasticity greater than one for M2 and less than one for Ml. Furthermore, we find that Japan's money demand is sensitive to interest rate changes. However, there is no evidence of the presence of a liquidity trap.

Estimation and Inference In Short Panel Vector Autoregressions with Unit Roots And Cointegration

This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normally distributed irrespective of the unit root and cointegrating properties of the underlying PVAR model. The transformed likelihood framework is also used to derive unit root and cointegration tests in panels with short time dimension; these tests have the attractive feature that they are based on standard chi-square and normal distributed statistics. Examining Generalized Method of Moments (GMM) estimation as an alternative to our proposed ML estimator, it is shown that conventional GMM estimators based on standard orthogonality conditons break down if the underlying time series contain unit roots. Also, the implementation of extended GMM estimators making use of variants of homoskedasticity and stationarity restrictions as suggested in the literature in a univariate context is subject to difficulties. Monte Carlo evidence is adduced suggesting that the ML estimator and parameter hypothesis and cointegration tests based on it perform well in small sample; this is in marked contrast to the small sample performance of the GMM estimators.Panel vector autoregressions, fixed effects, unit roots, cointegration