scholarly journals AN ANALYSIS OF LAND PRICES: A STRUCTURAL TIME‐SERIES APPROACH

2005 ◽  
Vol 9 (3) ◽  
pp. 145-172 ◽  
Author(s):  
Marko Hannonen
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Predictive Accuracy ◽  
Local Level ◽  
Predictive Testing ◽  
Hedonic Models ◽  
Land Prices ◽  
Structural Time Series ◽  
Time Series Modelling ◽  
Structural Time

This paper analyses spatio‐temporal variation of land prices in two single localities by means of structural time series modelling formalism that combines the flexibility of a time series model with that of the interpretation of a regression analysis. The extension of conventional hedonic models by introducing unobserved components for trend and cycle resulted to significant improvements in their post‐sample predictive accuracy. In predictive testing, for most model formulations the unobserved component approach generated only a marginal average prediction error when compared to the orthodox hedonic models, which, in contrast, yielded to a considerable amount of systematic prediction error. It therefore seems that the structural time‐series modelling paradigm offers a more viable alternative to the hedonic analysis of land prices than the conventional approach based on least squares estimates. The effect of slope component in the trend specification was found to be statistically insignificant, which implies that the elementary local level model would be the most adequate description of the long‐term land price movements.

Bayesian Structural Time Series

2020 ◽  
Vol 12 (1) ◽  
pp. 54-61
Author(s):  
Abdullah M. Almarashi ◽  
Khushnoor Khan
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Predictive Accuracy ◽  
Local Level ◽  
Real Life ◽  
Secondary Data ◽  
Real Data ◽  
Data Set ◽  
Structural Time Series ◽  
Structural Time

The current study focused on modeling times series using Bayesian Structural Time Series technique (BSTS) on a univariate data-set. Real-life secondary data from stock prices for flying cement covering a period of one year was used for analysis. Statistical results were based on simulation procedures using Kalman filter and Monte Carlo Markov Chain (MCMC). Though the current study involved stock prices data, the same approach can be applied to complex engineering process involving lead times. Results from the current study were compared with classical Autoregressive Integrated Moving Average (ARIMA) technique. For working out the Bayesian posterior sampling distributions BSTS package run with R software was used. Four BSTS models were used on a real data set to demonstrate the working of BSTS technique. The predictive accuracy for competing models was assessed using Forecasts plots and Mean Absolute Percent Error (MAPE). An easyto-follow approach was adopted so that both academicians and practitioners can easily replicate the mechanism. Findings from the study revealed that, for short-term forecasting, both ARIMA and BSTS are equally good but for long term forecasting, BSTS with local level is the most plausible option.

scholarly journals Estimation and Forecasting of Chickpea Production by Structural Time-Series Modelling

2017 ◽  
Vol 6 (7) ◽  
pp. 587-592
Author(s):  
Roshan Kumar Bhardwaj ◽  
Vandana Bhardwaj ◽  
D.P. Singh ◽  
S.S. Gautam ◽  
R.R. Saxena ◽  
...  
Keyword(s):  
Time Series ◽  
Structural Time Series ◽  
Time Series Modelling ◽  
Structural Time

scholarly journals Is radiative forcing cointegrated with temperature? A further examination using a structural time series approach

2019 ◽  
Vol 30 (5) ◽  
pp. 1203-1217
Author(s):  
Kelvin Balcombe ◽  
Iain Fraser ◽  
Abhijit Sharma
Keyword(s):  
Time Series ◽  
Radiative Forcing ◽  
Policy Design ◽  
Model Performance ◽  
Content Type ◽  
Structural Time Series ◽  
Long Run ◽  
Time Series Modelling ◽  
Time Series Approach ◽  
Structural Time

Purpose The purpose of this paper is to re-examine the long-run relationship between radiative forcing (including emissions of carbon dioxide, sulphur oxides, methane and solar radiation) and temperatures from a structural time series modelling perspective. The authors assess whether forcing measures are cointegrated with global temperatures using the structural time series approach. Design/methodology/approach A Bayesian approach is used to obtain estimates that represent the uncertainty regarding this relationship. The estimated structural time series model enables alternative model specifications to be consistently compared by evaluating model performance. Findings The results confirm that cointegration between radiative forcing and temperatures is consistent with the data. However, the results find less support for cointegration between forcing and temperature data than found previously. Research limitations/implications Given considerable debate within the literature relating to the “best” way to statistically model this relationship and explain results arising as well as model performance, there is uncertainty regarding our understanding of this relationship and resulting policy design and implementation. There is a need for further modelling and use of more data. Practical implications There is divergence of views as to how best to statistically capture, explain and model this relationship. Researchers should avoid being too strident in their claims about model performance and better appreciate the role of uncertainty. Originality/value The results of this study make a contribution to the literature by employing a theoretically motivated framework in which a number of plausible alternatives are considered in detail, as opposed to simply employing a standard cointegration framework.

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