The effects of the gender of children on expenditure patterns in rural China: a semiparametric analysis

We analyse expenditure patterns for rural China, focusing on differences between families with boys and girls. The sample includes more than 5000 nuclear families from 19 Chinese provinces. Following the existing literature, we estimate Engel curves for food and for alcohol, a typical adult good. We use a flexible, partially linear specification and allow for endogeneity of total expenditures. The results are similar to those of other studies, not providing much evidence of gender differentials. We then focus on the decision to send a child to school and on the budget share spent on educational goods. Using both parametric and semiparametric estimates, we find evidence that boys are more often sent to school and that expenditures on a boy that goes to school are larger than for a school‐going girl of the same age. Copyright © 2005 John Wiley & Sons, Ltd.     

Identification and Estimation of Engel Curves with Endogenous and Unobserved Expenditures

When dealing with the estimation of Engel curves, measurement errors in expenditure data and simultaneity are likely sources of endogeneity. In this paper we study identification of the parameters that characterize an Engel curve in the presence of both. We consider specifications where budget shares are polynomials in the logarithm of total expenditure, which is the case frequently encountered in empirical applications. We propose an estimation procedure which is an extension of that in Lewbel ( 1996 ) and exploits a control function assumption to correct for the endogeneity of the true unobserved total expenditure. Copyright © 2013 John Wiley & Sons, Ltd.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.     

Multivariate partial adjustment of financial ratios: a Bayesian hierarchical approach

In this paper we propose a multivariate extension of the partial adjustment model of financial ratios. To that end, we use a dynamic factor model which assumes that financial ratios measuring, essentially, the same economic–financial dimension of the firm evolve in a similar way, reflecting the evolution of the common factor. The proposed model is hierarchical with three levels. The first describes the relationship between each ratio and its common factor; the second describes the evolution of the common factors over time by means of Lev's ( 1969 ) partial adjustment model; and the third analyzes the similarity of firms' adjustment coefficients, taking into account their characteristics. The methodology is applied to the analysis of a set of financial ratios related to the business and financial structure of the firm. Copyright © 2008 John Wiley & Sons, Ltd.     

Zero expenditures and the estimation of engel curves

Over the brief interview periods typical of budget surveys (such as the U.K. Family Expenditure Survey) households' recorded expenditures are liable to be misleading indicators of their underlying consumption. Most starkly, a household may not be observed to purchase commodities that it nevertheless consumes. This paper considers the implications of such measurement errors for the estimation of demand systems, and derives a consistent estimator for the special case of linear Engel curves.     

The Engel curve for alcohol and the rank of demand systems

This paper shows that the quadratic shape of the Engel curve for alcohol is induced by preference heterogeneity between drinkers and abstainers in a Japanese data set. With controlling the heterogeneity, it is shown that the Engel curve for alcohol slopes monotonically downwards for drinkers, and that the probability of being a drinker is an increasing function of total expenditure. These two relationships generate a quadratic shape for the Engel curve for alcohol. Other goods in this data set appear to have nearly linear Engel curves, so if the alcohol Engel curve for drinkers is also linear, then after controlling for this preference heterogeneity the rank of this demand system would be two. Copyright © 2006 John Wiley & Sons, Ltd.     

Indirect estimation of large conditionally heteroskedastic factor models,with an application to the Dow 30 stocks

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small sample behaviour of our indirect estimators and several likelihood-based procedures through an extensive Monte Carlo experiment with empirically realistic designs. Finally, we apply our procedures to weekly returns on the Dow 30 stocks.     

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.     

Semiparametric estimation of consumer demand systems in real expenditure

Microdata concerning consumer demand typically show considerable variation in real expenditures, but very little variation in prices. We propose a semiparametric strategy for the consumer demand problem in which expenditure share equations are estimated nonparametrically in the real expenditure direction and estimated parametrically (with fixed or varying coefficients) in price directions. In our model, Engel curves are unrestricted: demands may have any rank. Because the demand model is derived from a cost function, it may be restricted to satisfy integrability and used for consumer surplus calculations. Since real expenditure is unobserved, but rather estimated under the model, we face a semiparametric model with a nonparametrically generated regressor. We show efficient convergence rates for parametric and nonparametric components. We illustrate the feasibility of our proposed strategy using Canadian expenditure and price data: Engel curves display curvature which cannot be encompassed by standard parametric models. We also find that the rationality restriction of Slutsky symmetry is rejected in the fixed‐coefficients model, but not in the varying‐coefficients model. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing for structural stability of factor augmented forecasting models

Mild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor subsequent estimation of the factors themselves (see e.g.  Stock and Watson (2009)). This result does not hold in the presence of large common breaks in the factor loadings, however. In this case, information criteria overestimate the number of breaks. Additionally, estimated factors are no longer consistent estimators of “true” factors. Hence, various recent research papers in the diffusion index literature focus on testing the constancy of factor loadings. However, forecast failure of factor augmented models can be due to either factor loading instability, regression coefficient instability, or both. To address this issue, we develop a test for the joint hypothesis of structural stability of both factor loadings and factor augmented forecasting model regression coefficients. Our proposed test statistic has a chi-squared limiting distribution, and we are able to establish the first order validity of (block) bootstrap critical values. Empirical evidence is also presented for 11 US macroeconomic indicators.     

Testing differences in efficiency of regions within a country: the case of Ukraine

In this paper we synthesize and adopt the recently developed methods in efficiency analysis to the case of comparison of regions within a country. We take Ukrainian regions as a subject of investigation, yet the same toolkit can be applied to test disputable differences in productivity for many other countries where such questions can be of national concern (e.g., Belgium, Great Britain, Spain, etc.). Contrary to common perception of economists focusing on Ukraine, we find no significant differences in distributions and aggregate efficiencies between the agricultural and industrial regions, neither between western (mostly Ukrainian speaking) and eastern (mostly Russian speaking) regions of Ukraine. However, we find strong support for a rapidly increasing gap between the capital (Kyiv) and all the regions since 2001. Using truncated regression analysis with bootstrap we also find robust evidence that the inefficiency of regions is positively related to alcohol and tobacco consumption, the amount of foreign direct investment and inversely related to criminality in the region. On the other hand, we also find strong evidence that amount of capital in the region and its wealth is positively associated with efficiency level of this region.     

Determinants of the method of payment in mergers and acquisitions

We empirically investigate the determinants of the payment form in mergers and acquisitions and introduce new variables on the target and acquirer investment characteristics to evaluate whether the concerns of target and acquirer shareholders are taken into account. Our sample encompasses mergers between publicly listed US companies from 1985 to 2004. Similarly we also consider the determinants of announcement returns using the same set of variables. We establish the relevance of a previously unreported variable for the determination of the payment form, the correlation of returns between target and acquirer, besides the more established determinants hostile takeovers, and defence mechanisms; weak evidence is found for the significance of budget constraints and no evidence for asymmetric information or tax considerations being a relevant factor. We do not find that announcement returns are explained by the variables considered.     

Do European capital flows comove?

We study the cross-section correlations of net, total, and disaggregated capital flows for the major source and recipient European Union countries. We seek evidence of changes in these correlations since the introduction of the euro to understand whether the European Union can be considered a unique entity with regard to its international capital flows. We make use of Ng’s (2006) “uniform spacing” methodology to rank cross-section correlations and to shed light on potential common factors driving international capital flows. We find that a common factor structure is suitable for equity flows disaggregated by sign but not for net and total flows. We only find mixed evidence that correlations between types of flows have changed since the introduction of the euro.     

Macroeconomic forecasting using approximate factor models with outliers

In this paper we consider estimating an approximate factor model in which candidate predictors are subject to sharp spikes such as outliers or jumps. Given that these sharp spikes are assumed to be rare, we formulate the estimation problem as a penalized least squares problem by imposing a norm penalty function on those sharp spikes. Such a formulation allows us to disentangle the sharp spikes from the common factors and estimate them simultaneously. Numerical values of the estimates can be obtained by solving a principal component analysis (PCA) problem and a one-dimensional shrinkage estimation problem iteratively. In addition, it is easy to incorporate methods for selecting the number of common factors in the iterations. We compare our method with PCA by conducting simulation experiments in order to examine their finite-sample performances. We also apply our method to the prediction of important macroeconomic indicators in the U.S., and find that it can deliver performances that are comparable to those of the PCA method.     

Quadratic Engel Curves with Measurement Error: Evidence from a Budget Survey*

This paper examines the importance of accounting for measurement error in total expenditure in the estimation of Engel curves, based on the 1994 Ethiopian Urban Household Survey. Using Lewbel's [Review of Economics and Statistics (1996 ), Vol. 78, pp. 718–725] estimator for demand models with correlated measurement errors in the dependent and independent variables, we find robust evidence of a quadratic relationship between food share and total expenditure in the capital city, and significant biases in various estimators that do not correct for correlated measurement errors.     

Consistent factor estimation in dynamic factor models with structural instability

This paper considers the estimation of approximate dynamic factor models when there is temporal instability in the factor loadings. We characterize the type and magnitude of instabilities under which the principal components estimator of the factors is consistent and find that these instabilities can be larger than earlier theoretical calculations suggest. We also discuss implications of our results for the robustness of regressions based on the estimated factors and of estimates of the number of factors in the presence of parameter instability. Simulations calibrated to an empirical application indicate that instability in the factor loadings has a limited impact on estimation of the factor space and diffusion index forecasting, whereas estimation of the number of factors is more substantially affected.     

Bayesian Calibration of p‐Values from Fisher's Exact Test

p‐Values are commonly transformed to lower bounds on Bayes factors, so‐called minimum Bayes factors. For the linear model, a sample‐size adjusted minimum Bayes factor over the class of g‐priors on the regression coefficients has recently been proposed (Held & Ott, The American Statistician 70(4), 335–341, 2016). Here, we extend this methodology to a logistic regression to obtain a sample‐size adjusted minimum Bayes factor for 2 × 2 contingency tables. We then study the relationship between this minimum Bayes factor and two‐sided p‐values from Fisher's exact test, as well as less conservative alternatives, with a novel parametric regression approach. It turns out that for all p‐values considered, the maximal evidence against the point null hypothesis is inversely related to the sample size. The same qualitative relationship is observed for minimum Bayes factors over the more general class of symmetric prior distributions. For the p‐values from Fisher's exact test, the minimum Bayes factors do on average not tend to the large‐sample bound as the sample size becomes large, but for the less conservative alternatives, the large‐sample behaviour is as expected.     

Estimating Engel curves under unit and item nonresponse

This paper estimates food Engel curves using data from the first wave of the Survey on Health, Aging and Retirement in Europe (SHARE). Our statistical model simultaneously takes into account selectivity due to unit and item nonresponse, endogeneity problems, and issues related to flexible specification of the relationship of interest. We estimate both parametric and semiparametric specifications of the model. The parametric specification assumes that the unobservables in the model follow a multivariate Gaussian distribution, while the semiparametric specification avoids distributional assumptions about the unobservables. Copyright © 2011 John Wiley & Sons, Ltd.     

A budget model of a university

Yale University operates a number of budget systems for purposes of planning and control. These include a capital budget, a cash budget, an operating budget, and an operating growth budget. The operating growth budget might be the one of most potential interest to people concerned with Operations Analysis in Education.

The operating growth budget is a model of what the University fiscal flows and structure might look like under varying conditions over an extended period, e.g., 20 years. It is composed of a set of ideas, a set of equations, and a computer program.

It is important to us to point out that this work is in a development stage. We have run many simulations, adjusted the program, refined the parameter estimates, and modified the questions we have been asking. Part of our efforts have followed a concern that we have captured the appropriate structure, part that we are investigating our own spectrum of decisions, and part that we can reflect the varying “external” conditions outside of the University's immediate influence and control.

Our work with the operating growth budget has already started to influence some of the decisions of the University including the yearly operating budget, the capital funds programs, and the endowment investment portfolio.