Choice f micro-mobility: Case studies of ta public bicycle sharing system in New Zealand

This study considers how to improve understanding of sustainable urban transport planning from the perspective of the Central Business District (CBD) redevelopment process for two cities, Hamilton and Christchurch in New Zealand (NZ). The most proportion of ‘Public Bicycle Share Schemes’ operate in densely populated cities as these are characterized by limited modal accessibility but high population density in the urban CBD. This situation is similar to NZ’s two medium-sized cities, in each of which the city’s population density is constantly increasing in the past years. In this study, Multinomial and Mixed Logistic regression models were used to determine the model specification, and subsequently, to test the mode choice cross-elasticities for promoting greater use of the bicycle sharing system in conjunction with public transport service. The data were gathered using stated preference surveys from 486 New Zealanders, and the modeling results indicate that the potential improvement in a modal shift towards micro-mobility, which can be enhanced by applying different policy options

TWO-STAGE QUANTILE REGRESSION WHEN THE FIRST STAGE IS BASED ON QUANTILE REGRESSION

We present the asymptotic properties of double-stage quantile regression estimators with random regressors, where the first stage is based on quantile regressions with the same quantile as in the second stage, which ensures robustness of the estimation procedure. We derive invariance properties with respect to the reformulation of the dependent variable. We propose a consistent estimator of the variance-covariance matrix of the new estimator. Finally, we investigate finite sample properties of this estimator by using Monte Carlo simulations.Two-stage estimation, Quantile regression, Endogeneity.

TWO-STAGE HUBER ESTIMATION

In this paper we study how the Huber estimator can be adapted to the presence of endogeneity in a two stage equations setting similar to that of 2SLS. We propose an estimation procedure that is at the same time relatively (i) simple, (ii) robust and (iii) efficient. Moreover, we deal with the case of random regressors and asymmetric errors, two extensions rarely present in this literature. The preliminary scale correction is implemented with median absolute deviation estimator, which is consistent with our above criteria and is a very robust estimator of scale. The resulting estimator is termed as the Two-Stage Huber (2SH) estimator. We explicitly establish the conditions for consistency and asymptotic normality of the 2SH estimator and we derive the formula of the asymptotic covariance matrix. We conduct Monte Carlo simulations whose results indicate that the 2SH estimator has smaller standard errors than the Two-Stage Least Squares (2SLS) estimator and than the Two-Stage Least Absolute Deviations (2SLAD) estimator in many situations. On the whole, the 2SH estimator appears to be a simple and useful alternative to 2SLS and 2SLAD in cases of two-stage estimation to deal with endogeneity when there are concerns for both robustness and efficiency.Two-stage estimation, Huber estimation, robustness, endogeneity

Predicting Changes in the Interest Rate: The Performance of Taylor Rules Versus Alternatives for the United Kingdom

We consider an experiment where we use the Taylor rule information set, inflation and the output gap, to predict the next change in monetary policy for the United Kingdom 1992 - 2000. To do this we use a limited dependent variable approach, where the next rate change could be `upwards', `downwards' or `no change'. A Multinomial Logit model is used to predict the next most likely change using monthly data, and these predictions are compared to the actual outturn. Against this hypothesis we compare a wider information set including more than just inflation and output gap variables. The in-sample and out-of-sample prediction tests are evaluated using forecast performance tests.The Taylor rule, monetary policy, directional forecast

Forecasting Changes in UK Interest Rates

Making accurate forecasts of the future direction of interest rates is a vital element when making economic decisions. The focus on central banks as they make decisions about the future direction of interest rates requires the forecaster to assess the likely outcome of comittee decisions based on new information since the previous meeting. We characterize this process as a dynamic ordered probit process that uses information to decide between three possible outcomes for interest rates: an increase, decrease or no-change. When we analyze the predictive ability of two information sets, we find that the approach has predictive ability both in-sample and out-of-sample that helps forecast the direction of future rates.Interest rates, monetary policy, Bank of England

Forecasting Changes in UK Interest Rates

Making accurate forecasts of the future direction of interest rates is a vital element when making economic decisions. The focus on central banks as they make decisions about the future direction of interest rates requires the forecaster to assess the likely outcome of committee decisions based on new information since the previous meeting. We characterize this process as a dynamic ordered probit process that uses information to decide between three possible outcomes for interest rates: an increase, decrease or no-change. When we analyze the predictive ability of two information sets, we find that the approach has predictive ability both in-sample and out-of-sample that helps forecast the direction of future rates.

Forecasting changes in UK interest rates

Making accurate forecasts of the future direction of interest rates is a vital element when making economic decisions. The focus on central banks as they make decisions about the future direction of interest rates requires the forecaster to assess the likely outcome of committee decisions based on new information since the previous meeting. We characterize this process as a dynamic ordered probit process that uses information to decide between three possible outcomes for interest rates: an increase, decrease or no-change. When we analyze the predictive ability of two information sets, we find that the approach has predictive ability both in-sample and out-of-sample that helps forecast the direction of future rates.