Shopper City

The bulk of the literature on retail location looks at the topic from the perspective of either the retail firm or the individual shopper. Another branch of the literature examines the spatial distribution of retail activities within a city or region, drawing on either central place theory or the Lowry model, neither of which incorporates either markets or agglomeration economies. This paper looks at retail location from the perspective of a general equilibrium model of location and land use, with agglomeration economies in retailing. In particular, drawing on the Fujita-Ogawa (1982) model of non- monocentric cities, it develops a model of retail location, assuming that retail firms behave competitively, subject to spatial agglomeration economies. Locations are distinguished according to the effective variety of retail goods they offer. Shoppers are willing to pay more for goods at locations with greater effective variety, and in their choice of where to shop trade off retail price, product variety, and accessibility to home. Retail prices and land rents at different locations adjust to achieve spatial equilibrium.retail, agglomeration, variety, land use

Error-correction factor models for high-dimensional cointegrated time series

Cointegration inferences often rely on a correct specification for the short-run dynamic vector autoregression. However, this specification is unknown, a priori. A lag length that is too small leads to an erroneous inference as a result of the misspecification. In contrast, using too many lags leads to a dramatic increase in the number of parameters, especially when the dimension of the time series is high. In this paper, we develop a new methodology which adds an error-correction term for the long-run equilibrium to a latent factor model in order to model the short-run dynamic relationship. The inferences use the eigenanalysis-based methods to estimate the cointegration and latent factor process. The proposed error-correction factor model does not require an explicit specification of the short-run dynamics, and is particularly effective for high-dimensional cases, in which the standard error-correction suffers from overparametrization. In addition, the model improves the predictive performance of the pure factor model. The asymptotic properties of the proposed methods are established when the dimension of the time series is either fixed or diverging slowly as the length of the time series goes to infinity. Lastly, the performance of the model is evaluated using both simulated and real data sets

Smoothing the Nonsmoothness

To tackle difficulties for theoretical studies in situations involving nonsmooth functions, we propose a sequence of infinitely differentiable functions to approximate the nonsmooth function under consideration. A rate of approximation is established and an illustration of its application is then provided

Testing Additive Separability of Error Term in Nonparametric Structural Models

Singapore Ministry of Education for Academic Research Fun

Functional Coefficient Moving Average Model with Applications to forecasting Chinese CPI

This article establishes the functional coefficient moving average model (FMA), which allows the coefficient of the classical moving average model to adapt with a covariate. The functional coefficient is identified as a ratio of two conditional moments. Local linear estimation technique is used for estimation and asymptotic properties of the resulting estimator are investigated. Its convergence rate depends on whether the underlying function reaches its boundary or not, and asymptotic distribution could be nonstandard. A model specification test in the spirit of Hardle-Mammen (1993) is developed to check the stability of the functional coefficient. Intensive simulations have been conducted to study the finite sample performance of our proposed estimator, and the size and the power of the test. The real data example on CPI data from China Mainland shows the efficacy of FMA. It gains more than 20% improvement in terms of relative mean squared prediction error compared to moving average model

Information and Inference in Econometrics: Estimation, Testing and Forecasting

Economic and Financial phenomena convey enormous information about the underlying structure of economic and policy interest. The first objective of the thesis is mainly concerned with how to make use of information efficiently, specifically, (1) how to separate noises from useful information in the presence of large dimensional data, (2) how to incorporate prior information (economic constraint), and (3) how to employ model structure, to conduct more informed inference, and thus to understand the economic structure wisely and draw sound policy conclusions.The second dimension of information refers to the recent developments in the information theory that measure how much information content the observed data contains. The formalism of Maximum Entropy provides an information-theoretic approach to tackle economic problems, especially those with data observed in aggregate terms. Thus, the second objective of the thesis is to make use of this line of research and develop a new estimation method to measure quantities of economic interest when researchers are faced with model uncertainty

Estimation for double-nonlinear cointegration

In recent years statistical inference for nonlinear cointegration has attracted attention from both academics and practitioners. This paper proposes a new type of cointegration in the sense that two univariate time series yt and xt are cointegrated via two (unknown) smooth nonlinear transformations, further generalizing the notion of cointegration initially revealed by Box and Tiao (1977), and more systematically studied by Engle and Granger (1987). More precisely, it holds that G(yt,β0)=g(xt)+ut, where G(⋅,β0) is strictly increasing and known up to an unknown parameter β0, g(⋅) is unknown and smooth, xt is I(1), and ut is the stationary disturbance. This setting nests the nonlinear cointegration model of Wang and Phillips (2009b) as a special case with G(y,β0)=y. It extends the model of Linton et al. (2008) to the cases with a unit-root nonstationary regressor. Sieve approximations to the smooth nonparametric function g are applied, leading to an extremum estimator for β and a plugging-in estimator for g(⋅). Asymptotic properties of the estimators are established, revealing that both the convergence rates and the limiting distributions depend intimately on the properties of the two nonlinear transformation functions. Simulation studies demonstrate that the estimators perform well even with small samples. A real data example on the environmental Kuznets curve portraying the nonlinear impact of per-capita GDP on air-pollution illustrates the practical relevance of the proposed double-nonlinear cointegration