An improved generalized moments estimator for a spatial moving average error model

Following Arnold and Wied (2010), we suggest an improved generalized moments estimator for the spatial moving average error model which takes explicitly into account that the moment conditions are based on OLS residuals rather than the true disturbances.     

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.     

Foreign direct investment and business cycle co-movements: The panel data evidence

The previous literature has largely overlooked the possible channels through which foreign direct investment (FDI) might influence business cycle synchronization. In this study we analyze the linkages that exist among FDI, trade and industrial dissimilarity in relation to business cycle co-movements using a panel data set taken from 77 pairs of developed countries. The error component three-stage least squares (EC3SLS) estimates from a simultaneous equations model with panel data are shown to be superior to the estimates obtained from single equation models or simultaneous equations models with cross-sectional data. Our results indicate that FDI serves as a channel of international business cycle transmission that is equally important as the channels of trade and monetary policy. On the contrary, industrial dissimilarity is identified as having an indirect impact on the business cycle correlation through trade and FDI. Furthermore, our findings suggest that in our sample FDI is of the horizontal type and tends to substitute for trade.     

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non–linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)–in–mean models are employed. The conditional errors are assumed to follow the normal and Student– t distributions. The non–linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non–linearity. Under the Student density, the extent of non–linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH–in–mean regression generated the worse out–of–sample forecasts.     

Estimation of an endogenous switching regression model with discrete dependent variables: Monte-Carlo analysis and empirical application of three estimators

The performances of alternative two-stage estimators for the endogenous switching regression model with discrete dependent variables are compared, with regard to their usefulness as starting values for maximum likelihood estimation. This is especially important in the presence of large correlation coefficients, in which case maximum likelihood procedures have difficulties to converge. Monte-Carlo simulations indicate that an estimator that corrects for conditional heteroskedasticity of the residuals is superior in almost all instances, and especially when maximum likelihood is problematic. This result is also obtained in an empirical example in which off-farm work participation equations of farm women are conditional on farm work participation status. First version received: July 1995/final version received: March 1998     

Three-dimensional panel data models with interactive effects: Estimation and simulation

This paper considers three-dimensional panel data models with unobservable multiple interactive effects, which are potentially correlated with the covariates. We propose an iterated OLS (IOLS) approach for the estimation of the static model and an iterated GMM (IGMM) method for the dynamic model. Monte Carlo simulations illustrate that our methods perform well in finite samples.     

Estimating Savings and Growth Functions in Developing Economies: A Simultaneous Equations Approach

There is a two-way influence between the savings rate on the one hand and some of its determinants on the other (i.e. a problem of simultaneity exists). This study specifies a general production function to generate a growth rate function, and estimates savings and growth equations simultaneously. A Hausman specification test examines the correct system specification, and determines whether 2SLS or 3SLS is the appropriate estimation technique. The results show that a simultaneous equations model is a better estimation technique (2SLS or 3SLS) than a single equation system (OLS), and that the full-information method (3SLS) provides better estimates than a limited-information method (2SLS). [C30]     

The asymptotic efficiency of semiparametric estimators for censored linear regression models

This paper presents numerical comparisons of the asymptotic mean square estimation errors of semiparametric generalized least squares (SGLS), quantite, symmetrically censored least squares (SCLS), and tobit maximum likelihood estimators of the slope parameters of censored linear regression models with one explanatory variable. The results indicate that the SCLS estimator is less efficient than the other two semiparametric estimators. The SGLS estimator is more efficient than quantile estimators when the tails of the distribution of the random component of the model are not too thick and the probability of censoring is not too large. The most efficient semiparametric estimators usually have smaller mean square estimation errors than does the tobit estimator when the random component of the model is not normally distributed and the sample size is 500–1,000 or more.     

Health expenditure models: A comparison using panel data

In this article, we focus on the estimation of outpatient expenditures with panel data. We model the logarithm of expenditures and consider five different models. The first two are two-part and sample selection cross-section models. Two-part panel data models turn out to be inappropriate for dealing with expenditures. We thus estimate sample selection models with panel data: one without a lagged dependent variable and two with a lagged dependent variable. These two latter models differ in their assumptions on the variance of the residuals. Modelling heteroscedasticity may indeed be important to avoid the bias due to the retransformation problem. We show that lagged dependent variables are important factors for heteroscedasticity. For the models with state dependence, we provide a new solution to the initial conditions problem by controlling for generalised residuals. We establish that panel data models highly improve the correlation explained by the model in the time-series dimension without damaging the fit in the cross-section dimension. For all indicators of fit, the model with state dependence and heteroscedasticity seems to dominate the others.     

Tobit or OLS? An empirical evaluation under different diary window lengths

Researchers analysing time-use data often estimate limited dependent variable models because time spent must be nonnegative and cannot be more than the total amount of time in a given observation period. While the traditional empirical technique applied to such cases is maximum likelihood estimation of a Tobit (censored regression) model, recent debate has questioned whether linear models estimated via Ordinary Least Squares (OLS) are preferable. On the one hand, Tobit models are deemed necessary to address the significant censoring (i.e. large numbers of zeroes) typically found in time-use data, in the face of which OLS estimators would be biased and inconsistent. Yet, optimization occurs over a longer period than that covered by the typical time diary (often a day), and thus some argue that reported zeroes represent a measurement problem rather than true nonparticipation in the activity, in which case OLS would be preferred. We provide direct empirical evidence on this question using the Australian Time Use Surveys, which record time-use information for two consecutive diary days, by estimating censored and linear versions of a parental child care model for both 24-hour and 48-hour windows of observation in order to determine the empirical consequences of estimation technique and diary length.     

Alternative Techniques for Estimation of Cross-Section Gravity Models

This paper compares four different estimators with respect to their suitability for cross‐section gravity model estimation. In many circumstances, a Hausman–Taylor approach can be recommended. This framework may provide consistent parameter estimates, when OLS or the traditional random‐effects model are biased. In contrast to the fixed‐effects approach, it allows to estimate parameters of variables such as GDP or GDP per capita, which vary only in a single dimension. The Hausman–Taylor model deserves attention in the estimation of cross‐sectional gravity models.     

Forecasting the Government Bond Term Structure in Australia

In this paper, we evaluate the performance of the dynamic Nielsen and Siegel interest rate model in forecasting Australian government bond yields. We compare a two‐stage OLS estimation procedure to a more powerful and robust state‐space framework estimated via a Kalman filter. We show that the one‐step approach generates smaller forecast errors than the two‐step procedure or a benchmark random walk model when forecasting the Australian government term structure across various horizons.     

基于地理加权回归模型的省域工业全要素生产率分析

全要素生产率(TFP)是一个国家或地区经济增长质量和技术进步、管理效率提高的重要标志。运用空间统计的Moran指数以及空间计量经济学的地理加权回归(Geographical Weighted Regression)模型方法,基于2003年中国31个省、直辖市和自治区的工业企业统计数据,对省级区域工业全要素生产率进行了测算分析。全要素生产率实证测算分析结果发现,空间Moran指数可测算省域工业生产率的空间效应,地理加权回归模型也可将影响省域全要素生产率的来源分解成各省域的局部影响,空间计量经济学模型在计量检验和测算我国31个省域工业全要素生产率中具有较好效果。     

Structural change in the yellow fats market

The superexogeneity of wage equations is tested in four major European countries. This is an important issue because while only weak exogeneity is needed for estimation purposes and for testing, and strong exogeneity for forecasting, superexogeneity is required for policy analysis. The procedure suggested by Engle and Hendry (1993) is adopted to carry out the empirical analysis. In all cases the conditional model is not affected by the first and second moments of the residuals from the marginal models. This invariance result implies that the estimated linear regression models are not subject to the Lucas critique over the period examined. Differences in wage determination between the various countries can be plausibly interpreted as resulting from the bargaining environment.     

EXAMINING THE STABILITY OF OKUN'S COEFFICIENT

The stability of Okun's law coefficient in the United States from 1949 to 2015 is examined using a regression with GARCH errors in order to capture the volatility of the series. Rolling estimations suggest that taking the volatility of the series into account yields more stable results compared to the simple OLS estimation, irrespective of the specification (gap or growth model), the data frequency (monthly or quarterly), or the length of the rolling window. The results also suggest that the persistence of shocks became much more important in explaining contemporaneous volatility when data from the recent global financial crisis were incorporated. In contrast, the feedthrough of output shocks in next period's output volatility was more important in the past, and especially during the 1970s stagflation period, but has been declining since.     

Growth regression revisited: R&D promotes convergence?

Barro and Sala-i-Martin (2004) analysed the empirical determinants of growth. They used a cross-sectional empirical framework that considered growth from two kinds of factors, initial levels of steady-state variables and control variables (e.g. investment ratio, infrastructure). Recent literature suggests that Generalized Method of Moments (GMM) estimation of dynamic panel data models produce more efficient and consistent estimates than Ordinary Least Squares (OLS) or pooled regression models. Following Cellini (1997), we also consider co-integration and error-correction methods for the growth regression. We extend the previous research for Asian countries of Kim (2009) to developed countries. Following the implications of semi-endogenous growth theory, we regressed output growth on a constant, 1-year lagged output (initial income) and the determinants of steady-state income (investment rate, population growth, the quadratic (or linear) function of Research and Development (R&D) intensity). The regression suggests faster significant convergence. This contradicts with that of Mankiw et al. (1992), which asserts that the speed is lower when considering broad concept of capital including human capital. The coefficients for the determinants of steady-state income, especially for the quadratic function of R&D intensity, are significant and occur in the expected direction. Our results suggest that adopting appropriate growth policy, an economy can grow more rapidly through transition dynamics or changing fundamentals.     

基于地理加权回归的上海市房价空间分异及其影响因子研究

利用上海市外环以内2010年12月1014个小区的平均房价数据,通过构建地理加权回归模型,并与基于全局最小二乘法(OLS)进行比较,揭示上海小区房价的空间分异和不同影响因子的影响。研究发现,每增加或减少一个单位各影响因子对房价的影响大小依次为:建成时间,到CBD距离,绿化率,到公园距离,距地铁站距离,距超市距离和距学校距离。同时,地理加权回归分解成局部参数估计优于OLS提供的全局参数估计,它可以深刻的揭示出房价和空间影响因子之间复杂的关系,而且可视化的工具可以用地图的形式更详细的呈现出城市房价的整体景观,这些都是传统OLS无法比拟的。     

Enhancing the local power of IVX-based tests in predictive regressions

IVX estimation is used increasingly often in predictive regressions with regressors of unknown persistence. While not exhibiting the second-order bias the OLS estimator has in this setup, IVX estimators have reduced rates of convergence when the regressors are highly persistent. The reduced convergence rates may sometimes lead to power losses in finite samples when testing for no predictability, for instance. The note discusses a simple way of improving the local power of IVX-based tests, consisting of augmenting the predictive regression with the lagged dependent variable. This implies a feed-back loop which strengthens the signal of the IVX instrument without changing its dynamic properties. The proposed augmentation works best when the power loss of IVX would have been maximal compared to the infeasible OLS-based test.     

Nonlinear interest rate reaction functions for the UK

We empirically analyze Taylor-type equations for short-term interest rates in the United Kingdom using quarterly data from 1970Q1 to 2006Q2. Starting from strong evidence against a simple linear Taylor rule, we model nonlinearities using logistic smooth transition regression (LSTR) models. The LSTR models with time-varying parameters consistently track actual interest rate movements better than a linear model with constant parameters. Our preferred LSTR model uses lagged interest rates as a transition variable and suggests that in times of recessions the Bank of England puts more weight on the output gap and less so on inflation. A reverse pattern is observed in non-recession periods. Parameters of the model change less frequently after 1992, when an inflation target range was announced. We conclude that for the analysis of historical monetary policy, the LSTR approach is a viable alternative to linear reaction functions.     

Teaching Regressions with a Lagged Dependent Variable and Autocorrelated Disturbances

The author attempts to rectify the unsatisfactory textbook treatment of the finite-sample properties of estimators of regression models with a lagged dependent variable and autocorrelated disturbances. He contends that the bias of the OLS estimator of a regression model with a lagged dependent variable and autocorrelated disturbances is determined by two effects, the dynamic effect and the correlation effect, which may be reinforcing or offsetting. The implications of these two effects are explored within a theoretical and a Monte Carlo framework.