Static Time Series Models and Ordinary Least Squares Estimation

2015 ◽  
pp. 51-80
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Least Squares Estimation ◽  
Time Series Models

A new vision on ordinary least squares estimation of parameters of linear model

2021 ◽  
Author(s):  
K. Lakshmi ◽  
B. Mahaboob ◽  
D. Sateesh Kumar ◽  
G. Balagi Prakash ◽  
T. Nageswara Rao
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Ordinary Least Squares ◽  
Least Squares Estimation ◽  
Estimation Of Parameters ◽  
New Vision

Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

1996 ◽  
Vol 6 ◽  
pp. 1-36 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Cross Section ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Correlated Errors ◽  
Section Data ◽  
Variable Approach ◽  
Parameter Heterogeneity ◽  
Lagged Dependent Variable

In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.

An Investigation of the Time Series Behaviour of International Tourist Arrivals

1997 ◽  
Vol 3 (2) ◽  
pp. 185-199 ◽  
Author(s):  
Kevin KF. Wong
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Preliminary Investigation ◽  
Model Misspecification ◽  
Theoretical Implication ◽  
Ordinary Least Squares ◽  
Initial State ◽  
International Tourist ◽  
Stationary Type ◽  
Parameter Estimators

Most tourism econometric models are based on conventional least squares estimation, which assumes stationarity in their data generating mechanism. However, they fail to recognize the implications of the integrated properties of the historical time series of tourism data. Such time series properties may have important consequences with regard to the theoretical implication and interpretation of these tourism models. In this paper, historical data on international tourist arrivals from six major regions and seventeen individual countries are analysed to determine whether the series is better characterized by a stationary or non-stationary type process. Based on unit root tests, the results in most cases indicate that international tourist arrivals exhibit a non-stationary stochastic process that has the tendency to fluctuate away from a given initial state as time passes. These findings imply that studies which conveniently draw standard inferences from ordinary least squares estimated tourism models based on the levels of international tourist arrivals can be very misleading since non-stationarity in the data will produce inconsistent parameter estimators and unreliable test statistics. Furthermore, model misspecification that arises from unrelated integrated series can seriously bias conventional significance tests towards the acceptance of an apparently significant relationship. In this preliminary investigation, we conclude that econometric tourism models that focus on the levels of international tourist arrivals may not be reliable since the series is non-stationary and is integrated of order one, I(1).

scholarly journals Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

2019 ◽  
Vol 11 (14) ◽  
pp. 1730 ◽  
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Spectral Band ◽  
Ordinary Least Squares ◽  
Landsat 8 ◽  
Eastern Siberia ◽  
Landscape Changes ◽  
Least Squares Regression ◽  
Spectral Bandpass ◽  
Sentinel 2

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.

scholarly journals Asymptotic Optimality of Estimating Function Estimator for CHARN Model

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Tomoyuki Amano
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Financial Time Series ◽  
Asymptotic Optimality ◽  
Time Series Models ◽  
Estimating Function ◽  
Asymptotically Optimal ◽  
Financial Time ◽  
Conditional Least Squares ◽  
Better Than

CHARN model is a famous and important model in the finance, which includes many financial time series models and can be assumed as the return processes of assets. One of the most fundamental estimators for financial time series models is the conditional least squares (CL) estimator. However, recently, it was shown that the optimal estimating function estimator (G estimator) is better than CL estimator for some time series models in the sense of efficiency. In this paper, we examine efficiencies of CL and G estimators for CHARN model and derive the condition that G estimator is asymptotically optimal.

scholarly journals EFFECTS OF FOREIGN LOANS, INTEREST RATE, AND EXPORT FOR THE FOREIGN EXCHANGE RESERVES IN INDONESIA 2002-2016

2019 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Anas Iswanto Anwar ◽  
Bayu Pamungkas Djamal ◽  
Sri Undai Nurbayani
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Least Squares ◽  
Foreign Exchange ◽  
Secondary Data ◽  
Ordinary Least Squares ◽  
Foreign Exchange Reserves ◽  
Central Agency ◽  
Foreign Loans ◽  
The Interest Rate

The aim of this research is to analyze the effect of foreign loans, interest rate, and export for the foreign exchange reserves in Indonesia during 2002-2016. This research used secondary data which tends the time-series published by Bank Indonesia, The Ministry of Trade Republic of Indonesia, Central Agency on Statistics Indonesia in the year of 2002-2016. The result of the regression by using ordinary least squares (OLS) method showed that the foreign loans and export take effect positively to the foreign exchange reserves. It indicates that the increase of foreign loans and export could affect the foreign exchange reserves in Indonesia during 2002-2016. Otherwise, the interest rate could not affect the foreign exchange reserves in Indonesia during 2002-2016.

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