A dymimic model of housing price determination

This paper presents and estimates a model of the resale housing market. The data are a cross-section of monthly time series obtained from the multiple-listing service for a suburb of San Diego. The model is specified and estimated as a dynamic multiple indicator multiple cause system of equations where the capitalization rate is taken to be an unobservable time series to be estimated jointly with the unknown parameters. These are estimated by maximum likelihood using an EM algorithm based upon Kalman filtering and smoothing.The specification of the model features hedonic equations for each house sale and a dynamic equation for the capitalization rate which is constrained to make the expectation of prices equal the present value of the net returns to home ownership whenever the economic variables stabilize at steady state values. Out of steady state, the capitalization rate slowly adapts to new information.The model attributes a large portion of housing price increases of the 1970's to a fall in the capitalization rate which in turn was driven by rental inflation, tax rates and mortgage rates. Post-sample simulations indicate an initial flattening of housing inflation rates and later a fall brought on by the increase in steady state capitalization rates. In-sample simulations show that although both Proposition 13 and the inflation induced rise in the marginal income tax rates provided partial explanations for the fall in capitalization rates, the single most important factor was the acceleration in price of housing services which interacted with the tax treatment of home ownership to produce an amazing 18% average annual rate of price increase over the last seven years of the 1970's.     

The long-run implications of the production smoothing model of inventories: An empirical test

This paper provides an empirical test of the long-run implications of the production smoothing model of inventories, the dominant framework for inventory investment research in the past. Intertemporal models of a firm holding inventories of finished goods predict a long-run relationship between inventories, shipments, factor input prices, and the real interest rate which is tested here using cointegration test procedures. These tests provide little support for the predictions of the production smoothing model. In most of the data sets used, test statistics indicate that inventories, shipments, factor input prices, the nominal interest rate, and the inflation rate maintain a long-run equilibrium relationship but parameter estimates of cointegrating vectors are often implausible, typically rejecting hypotheses implied by structural models of the production smoothing motive for holding inventories.     

Fitting Observed Demographic Rates with the Multiexponential Model Schedule: An Assessment of Two Estimation Programs

The process of finding the best fitting model can often be very time consuming and tedious. Most computer programs are very specialized, and many require initial parameter estimates to fit a particular curve. Those that are most useful are ones that are versatile in applications, and ones that allow inputs of "rough" parameter estimates for finding the optimal ones. This paper focuses on current approaches for fitting observed age-specific demographic data with the multiexponential model schedule and uses two curve-fitting computer programs: MODEL and TableCurve2D. These two programs are assessed according to how well, and how simply, they can be used to fit age-specific fertility, mortality, and migration rates.     

Incorporating a tracking signal into a state space model

It is a common practice to complement a forecasting method such as simple exponential smoothing with a monitoring scheme to detect those situations where forecasts have failed to adapt to structural change. It will be suggested in this paper that the equations for simple exponential smoothing can be augmented by a common monitoring statistic to provide a method that automatically adapts to structural change without human intervention. The resulting method, which turns out to be a restricted form of damped trend corrected exponential smoothing, is compared with related methods on the annual data from the M3 competition. It is shown to be better than simple exponential smoothing and more consistent than traditional damped trend exponential smoothing.     

Optimal automobile emission control: A household utility model

Traditionally, benefit-cost studies value mortality changes using equivalent capital approaches. That is, the social benefit of decreased mortality is related to an increased income stream. A utility model which accounts for mortality changes as changes in individual or household risks is proposed, it is then employed to analyze automotive emission control. The utility model selects optimal national emission control levels for individual households by maximizing a combination of risk and consumption. The social optimum is taken as the median levels of all households. The control levels for three pollutants are more stringent than those calculated by a more typical benefit-cost method but less strict than the present statutory standards.

The data has been taken for several sources. The risk data is from an epidemiology study that associates age specific mortality rates with various factors including pollution levels. Economic benefits are derived from several estimates of the association of pollution with vegetation and material damages and soiling. Automobile control costs are from EPA and auto company estimates.     

组合模型在公路客运量预测中的应用

针对公路客运量预测问题,在建立灰色GM(1,1)模型和三次指数平滑模型的基础上,利用最优加权系数法建立组合模型来对客运量进行预测。以此来避免单一模型的不足,从而提高模型的预测精度。通过2005-2009年安顺到贵阳的贵黄高速客运量模型的检验,证明组合模型是合理、可行的,并且具有较高的预测精度。     

Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals

This paper evaluates the performances of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state space models for exponential smoothing, and Harvey’s structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and Australia. The mean coverage rates and widths of the alternative prediction intervals are evaluated in an empirical setting. It is found that all models produce satisfactory prediction intervals, except for the autoregressive model. In particular, those based on the bias-corrected bootstrap perform best in general, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

Seasonal exponential smoothing with damped trends : An application for production planning

This paper reviews a spreadsheet-based forecasting approach which a process industry manufacturer developed and implemented to link annual corporate forecasts with its manufacturing/distribution operations. First, we consider how this forecasting system supports overall production planning and why it must be compatible with corporate forecasts. We then review the results of substantial testing of variations on the Winters three-parameter exponential smoothing model on 28 actual product family time series. In particular, we evaluate whether the use of damping parameters improves forecast accuracy. The paper concludes that a Winters four-parameter model (i.e. the standard Winters three-parameter model augmented by a fourth parameter to damp the trend) provides the most accurate forecasts of the models evaluated. Our application confirms the fact that there are situations where the use of damped trend parameters in short-run exponential smoothing based forecasting models is beneficial.     

Automatic selection of unobserved components models for supply chain forecasting

For many companies, automatic forecasting has come to be an essential part of business analytics applications. The large amounts of data available, the short life-cycle of the analysis and the acceleration of business operations make traditional manual data analysis unfeasible in such environments. In this paper, an automatic forecasting support system that comprises several methods and models is developed in a general state space framework built in the SSpace toolbox written for Matlab. Some of the models included are well-known, such as exponential smoothing and ARIMA, but we also propose a new model family that has been used only very rarely in this context, namely unobserved components models. Additional novelties include the use of unobserved components models in an automatic identification environment and the comparison of their forecasting performances with those of exponential smoothing and ARIMA models estimated using different software packages. The new system is tested empirically on a daily dataset of all of the products sold by a franchise chain in Spain (166 products over a period of 517 days). The system works well in practice and the proposed automatic unobserved components models compare very favorably with other methods and other well-known software packages in forecasting terms.     

Stochastic methods in population forecasting

"This paper presents a stochastic version of the demographic cohort-component method of forecasting future population. In this model the sizes of future age-sex groups are non-linear functions of random future vital rates. An approximation to their joint distribution can be obtained using linear approximations or simulation. A stochastic formulation points to the need for new empirical work on both the autocorrelations and the cross-correlations of the vital rates. Problems of forecasting declining mortality and fluctuating fertility are contrasted. A volatility measure for fertility is presented. The model can be used to calculate approximate prediction intervals for births using data from deterministic cohort-component forecasts. The paper compares the use of expert opinion in mortality forecasting with simple extrapolation techniques to see how useful each approach has been in the past. Data from the United States suggest that expert opinion may have caused systematic bias in the forecasts."     

A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series

Time series data are often subject to statistical adjustments needed to increase accuracy, replace missing values and/or facilitate data analysis. The most common adjustments made to original observations are signal extraction (e.g. smoothing), benchmarking, interpolation and extrapolation. In this article, we present a general dynamic stochastic regression model, from which most of these adjustments can be performed, and prove that the resulting generalized least square estimator is minimum variance linear unbiased. We extend current methods to include those cases where the signal follows a mixed model (deterministic and stochastic components) and the errors are autocorrelated and heteroscedastic.     

Nonparametric/semiparametric estimation and testing of econometric models with data dependent smoothing parameters

We consider nonparametric/semiparametric estimation and testing of econometric models with data dependent smoothing parameters. Most of the existing works on asymptotic distributions of a nonparametric/semiparametric estimator or a test statistic are based on some deterministic smoothing parameters, while in practice it is important to use data-driven methods to select the smoothing parameters. In this paper we give a simple sufficient condition that can be used to establish the first order asymptotic equivalence of a nonparametric estimator or a test statistic with stochastic smoothing parameters to those using deterministic smoothing parameters. We also allow for general weakly dependent data.     

Selecting stochastic mortality models for the Italian population

The future revision of capital requirements and a market-consistent valuation of non-hedgeable liabilities lead to an increasing attention on forecasting longevity trends. In this field, many methodologies focus on either modeling mortality or pricing mortality-linked securities (as longevity bonds). Following Lee–Carter method (proposed in 1992), actuarial literature has provided several extensions in order to consider different trends observed in European data set (e.g., the cohort effect). The purpose of the paper is to compare the features of main mortality models proposed over the years. Model selection became indeed a primary task with the aim to identify the “best” model. What is meant by best is controversial, but good selection techniques are usually based on a good balance between goodness of fit and simplicity. In this regard, different criteria, mainly based on residual and projected rates analysis, are here used. For the sake of comparison, main forecasting methods have been applied to deaths and exposures to risk of male Italian population. Weaknesses and strengths have been emphasized, by underlying how various models provide a different goodness of fit according to different data sets. At the same time, the quality and the variability of forecasted rates have been compared by evaluating the effect on annuity values. Results confirm that some models perform better than others, but no single model can be defined as the best method.     

Nonparametric estimation of conditional distribution functions with longitudinal data and time-varying parametric models

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies. We propose in this paper an estimation approach based on time-varying parametric models. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model, but the parameters are smooth functions of time. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Asymptotic properties, including the asymptotic biases, variances and mean squared errors, are derived for the local polynomial smoothed estimators. Applicability of our two-step estimation method is demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through simulation study.     

Effects of social and cultural processes on health1

Future advances in the planning and evaluation of health service systems are highly dependent upon the development of a methodology that permits the empirical validation of hypotheses concerning the effects of social and cultural processes on health. In this paper such a methodology has been proposed. A structural model has been developed and analyzed, using the causal modeling technique of path analysis, in which social, economic, and demographic characteristics of New Mexico counties have been related to the infant mortality rate as an index of health. Path coefficients have been estimated from census data and vital statistics. Dynamic programming has been utilized to predict the combined direct and indirect effects on the health of New Mexico's population that would result from changes in the composition of the population.Analysis of the model suggests that these structural characteristics are causally interrelated, with economic factors a major determinant of infant mortality. While urbanization and migration also significantly affect infant mortality rates, education surprisingly is not important causally when compared to other variables, nor is proportion nonwhite when other social and economic factors are taken into account. In contrast, the proportion of Spanish-Americans residing in a county has a rather unexpected effect in that infant mortality rates decline as the proportion of this ethnic group rises. This finding suggests that it is important in health planning to consider cultural factors that may be unique to particular ethnic groups.In general the model building techniques and the algorithms presented here provide a valuable means of generating and testing hypotheses regarding the effects of social and cultural processes on health.     

Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

Optimal transformations for categorical autoregressive time series

This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze categorical time series. The method uses an alternating least squares algorithm that consists of two main steps: estimation and transformation. Nominal, ordinal and numerical data can be analyzed. Some alternative applications of the general idea are highlighted: intervention analysis, smoothing categorical time series, predictable components, spatial modeling and cross-sectional multivariate analysis. Limitations, modeling issues and possible extensions are briefly indicated.