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A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (BLUP) of Goldberger in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed PUP is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals. [ABSTRACT FROM AUTHOR]

This article proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing. [ABSTRACT FROM AUTHOR]

This article describes a large, monthly frequency, macroeconomic database with the goal of establishing a convenient starting point for empirical analysis that requires “big data.” The dataset mimics the coverage of those already used in the literature but has three appealing features. First, it is designed to be updated monthly using the Federal Reserve Economic Data (FRED) database. Second, it will be publicly accessible, facilitating comparison of related research and replication of empirical work. Third, it will relieve researchers from having to manage data changes and revisions. We show that factors extracted from our dataset share the same predictive content as those based on various vintages of the so-called Stock–Watson dataset. In addition, we suggest that diffusion indexes constructed as the partial sum of the factor estimates can potentially be useful for the study of business cycle chronology. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

This article considers estimation of moving average (MA) models with non-Gaussian errors. Information in higher order cumulants allows identification of the parameters without imposing invertibility. By allowing for an unbounded parameter space, the generalized method of moments estimator of the MA(1) model is classical root-Tconsistent and asymptotically normal when the MA root is inside, outside, and on the unit circle. For more general models where the dependence of the cumulants on the model parameters is analytically intractable, we consider simulation-based estimators with two features. First, in addition to an autoregressive model, new auxiliary regressions that exploit information from the second and higher order moments of the data are considered. Second, the errors used to simulate the model are drawn from a flexible functional form to accommodate a large class of distributions with non-Gaussian features. The proposed simulation estimators are also asymptotically normally distributed without imposing the assumption of invertibility. In the application considered, there is overwhelming evidence of noninvertibility in the Fama-French portfolio returns. [ABSTRACT FROM PUBLISHER]

The method of principal components is widely used to estimate common factors in large panels of continuous data. This article first reviews alternative methods that obtain the common factors by solving a Procrustes problem. While these matrix decomposition methods do not specify the probabilistic structure of the data and hence do not permit statistical evaluations of the estimates, they can be extended to analyze categorical data. This involves the additional step of quantifying the ordinal and nominal variables. The article then reviews and explores the numerical properties of these methods. An interesting finding is that the factor space can be quite precisely estimated directly from categorical data without quantification. This may require using a larger number of estimated factors to compensate for the information loss in categorical variables. Separate treatment of categorical and continuous variables may not be necessary if structural interpretation of the factors is not required, such as in forecasting exercises. [ABSTRACT FROM AUTHOR]

This article examines the determinants of eco-friendly electronic good consumption among students at a large Australian university who have been exposed to a marketing campaign, Mobile Muster. Empirical research generally shows younger consumers to be less concerned about the environment. Similar studies demonstrate that peer pressure has a large role to play. Accordingly, using the theories of reasoned action and planned behavior, this study examines subjective norms, attitudes, perceived control, environmental concern, altruism, risk aversion, price consciousness, involvement, branding, environmental knowledge, and their relationship with purchase intentions. It is the first study to examine these effects on the purchase of eco-friendly phones, a product of high personal relevance to these consumers. Mediation effects were also assessed. Results showed that subjective norms (friends/experts) can increase intentions and mediate the relationship between information and intentions. Involvement emerged as a significant influencer on intentions, presenting new findings for the literature. Moreover, higher levels of objective knowledge increased intentions. This supported the significant role of the Mobile Muster scheme in dispersing knowledge. These findings are of practical value to educators, allowing them to determine factors that influence consumer knowledge and norms with the aim of enabling students to form more ecological relevant purchase intentions. [ABSTRACT FROM AUTHOR]

This article proposes a simple estimator that is consistent for the fraction of a panel that has an autoregressive unit root. Given such an estimate, ..., we can test the null hypothesis that θ = θ0 for any value of θ0 ε (0, 1]. The test is asymptotically standard normal and is valid whether or not the panel is cross-sectionally correlated. The main insight is that in a panel in which some units are stationary and some have unit roots, the cross-sectional variance of the mixed panel is dominated by a linear trend that grows at rate θ, where θ is precisely the fraction of the panel with a Unit root. Averaging the change in cross-sectional variance over time then gives a √N consistent estimate of θ as N, T → ∞. Simulations show that the estimator has good finite-sample properties when T ≥ 100, even with N as small as 30. [ABSTRACT FROM AUTHOR]

A widely held but untested assumption underlying macroeconomic analysis is that the number of shocks driving economic fluctuations, q, is small. In this article we associate q with the number of dynamic factors in a large panel of data. We propose a methodology to determine q without having to estimate the dynamic factors. We first estimate a VAR in r static factors, where the factors are obtained by applying the method of principal components to a large panel of data, then compute the eigenvalues of the residual covariance or correlation matrix. We then test whether their eigenvalues satisfy an asymptotically shrinking bound that reflects sampling error. We apply the procedure to determine the number of primitive shocks in a large number of macroeconomic time series. An important aspect of the present analysis is to make precise the relationship between the dynamic factors and the static factors, which is a result of independent interest. [ABSTRACT FROM AUTHOR]

This article provides tools for characterizing the extent of cross-section correlation in panel data when we do not know a priori how many and which series are correlated. Our tests are based on the probability integral transformation of the ordered correlations. We first split the transformed correlations by their size into two groups, then evaluate the variance ratio of the two subsamples. The problem of testing cross- section correlation thus becomes one of identifying mean shifts and testing nonstationarity. The tests can be applied to raw data and regression errors. We analyze data on industrial production among 12 OECI) countries, as well as 21 real exchange rates. The evidence favors a common factor structure in European real exchange rates but not in industrial production. [ABSTRACT FROM AUTHOR]

We present the sampling distributions for the coefficient of skewness, kurtosis, and a joint test of normality for time series observations. We show that when the data are serially correlated, consistent estimates of three-dimensional long-run covariance matrices are needed for testing symmetry or kurtosis. These tests can be used to make inference about any conjectured coefficients of skewness and kurtosis. In the special case of normality, a joint test for the skewness coefficient of 0 and a kurtosis coefficient of 3 can be obtained on construction of a four-dimensional long-run covariance matrix. The tests are developed for demeaned data, but the statistics have the same limiting distributions when applied to regression residuals. Monte Carlo simulations show that the test statistics for symmetry and normality have good finite-sample size and power. However, size distortions render testing for kurtosis almost meaningless except for distributions with thin tails, such as the normal distribution. Combining skewness and kurtosis is still a useful test of normality provided that the limiting variance accounts for the serial correlation in the data. The tests are applied to 21 macroeconomic lime series. [ABSTRACT FROM AUTHOR]

This paper considers the implications of mean shifts in a multivariate setting. It is shown that under the additive outlier type mean shift specification, the intercept in each equation of the vector autoregression (VAR) will be subject to multiple shifts when the break dates of the mean shifts to the univariate series do not coincide. Conversely, under the innovative outlier type mean shift specification, both the univariate and the multivariate time series are subject to multiple shifts when mean shifts to the innovation processes occur at different dates. We consider two procedures, the first removes the shifts series by series before forming the VAR, and the second removes intercept shifts in the VAR directly. The pros and cons of both methods are discussed. [ABSTRACT FROM AUTHOR]

Focuses on statistical models to analyze how tax-induced price changes affect the amounts of purchased goods by consumers. Estimation of utility parameter functions; Arguments that some statistical methods are less useful than alternatives that consider the derivatives of the regression function of consumer behavior; Disadvantages of average derivative estimation (ADE).

We analyze the choice of the truncation lag in the context of the Said-Dickey test for the presence of a unit root in a general autoregressive moving average model. It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account. In particular, we study data-dependent rules that are not constrained to satisfy the lower bound condition imposed by Said-Dickey. Akaike's information criterion falls into this category. The analytical properties of the truncation lag selected according to a class of information criteria are compared to those based on sequential testing for the significance of coefficients on additional lags. The asymptotic properties of the unit root test under various methods for selecting the truncation lag are analyzed, and simulations arc used to show their distinctive behavior in finite samples. Our results favor methods based on sequential tests over those based on information criteria, because the former show less size distortions and have comparable power. [ABSTRACT FROM AUTHOR]

The question of whether regional disparities in Canada could arise as a result of non-constant returns to scale and non-identical production functions is examined. This is accomplished by estimating and comparing the decision rules for factor demands across Canadian regions. The decision rules are based on a flexible and dynamic production technology which allows for the presence of internal adjustment costs, do not impose constant returns to scale or require hours and workers to have the same output elasticities. The model is estimated for four separate Canadian regions (Ontario, Quebec, the Western and Eastern provinces) on annual data ranging from 1965 to 1985. The primary objective of the present work is to see if the manufacturing industries in Ontario, Quebec, Western Canada and Eastern Canada exhibit constant returns to scale, and whether regional disparities might arise as a result of non-identical production functions. Regional disparities have persisted in Canada for decades. This paper examines the empirical validity of non-constant returns to scale and non-identical production functions as sources of regional disparities. With respect to the latter source, our focus is on the difference in the cost of adjusting factor inputs. According to our results, decreasing returns to scale exists in the manufacturing sector throughout the country. Significant regional differences in the speed and the costs of adjustment are also found. [ABSTRACT FROM AUTHOR]

The article offers information on the manuscript submission report of the "Journal of Business & Economics Statistics," (JBES) in 2009. It says that total number of submissions was over 390 which shows the stability of its publication backlog. Meanwhile, more than half of new submissions were declined due to insufficient contribution or unfit to be published. Furthermore, JBES deals with topics related to statistics as well as econometrics and publishes invited paper specifically research topic.

The article presents an editor's report for 2008 which reflects on the quality of submission of statistical reports and their expression of gratitude to their staff as well. It mentioned that the number of submissions to their "Journal of Business and Economic Statistics" have continued to increase and the quality of submission has risen. The authors are grateful for their team of associate editors for their hardwork.

Charts are presented on various aspects of the editing of this publication.

The article presents a report from the editors of the magazine for the year 2006. They announce that an automated system installed to keep track of journal submissions is functioning properly. The number of articles submitted for possible publication is noted and the editors state that submissions spanned a wide range of subjects related to the fields of business and economic statistics. A chart is presented illustrating the time spent on reviewing manuscripts submitted for publication for the years 2001-06.