Finite sample efficiency of OLS in linear regression models with long-memory disturbances

Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures.

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PERFORMANCE OF MULTIPLE LINEAR REGRESSION AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS IN PREDICTING ANNUAL TEMPERATURES OF OGUN STATE, NIGERIA

Journal of Natural Sciences Engineering and Technology ◽

10.51406/jnset.v16i1.1802 ◽

2017 ◽

Vol 16 (1) ◽

pp. 50-62

Author(s):

I. JIBRIL ◽

J. J. MUSA ◽

P. O.O. DADA ◽

H. E. IGBADUN ◽

J. M. MOHAMMED ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Long Memory ◽

Regression Models ◽

Moving Average ◽

Arma Model ◽

Autoregressive Moving Average ◽

Linear Regression Models ◽

Ogun State ◽

Maximum Temperatures

The performance of Autoregressive Moving Average and Multiple Linear Regression Models in predicting minimum and maximum temperatures of Ogun State is herein reported. Maximum and Minimum temperatures data covering a period of 29 years (1982 -2009) obtained from the Nigerian Meteorological Agency (NiMet), Abeokuta office, Nigeria, were used for the analyses. The data were first processed and aggregated into annual time series. Mann-Kendal non-parametric test and spectral analysis were carried out to detect whether there is trend, seasonal pattern, and either short or long memory in the time series. Mann-Kendal Z-values obtained are –0.47 and –2.03 for minimum and maximum temperatures respectively, indicating no trend, though the plot shows a slight change. The Lo’s R/S Q(N,q) values for minimum and maximum temperatures are 3.67 and 4.43, which are not within the range 0.809 and 1.862, thus signifying presence of long memory. The data was divided into two and the first 20 years data was used for model development, while the remaining was used for validation. Autoregressive Moving Average (ARMA) model of order (5, 3) and Autoregressive (AR) model of order 2 are found best for predicting minimum and maximum temperatures respectively. Multiple Linear Regression (MLR) model with 4 features (moving average, exponential moving average, rate of change and oscillator) were fitted for both temperatures. The ARMA and AR models were found to perform better with Mean Absolute Percentage Error (MAPE) values of -2.89 and -1.37 for minimum and maximum temperatures, compared with the Multiple Linear Regression Models with MAPE values of 141 and 876 respectively. Results of ARMA model can be relied on in generating forecast of temperature of the study area because of their minimal error values. However, it is recommended other climatic elements that were not captured in this paper due to unavailability of information be considered too in order to see which model is best for them.

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Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

Econometrics ◽

10.3390/econometrics7020022 ◽

2019 ◽

Vol 7 (2) ◽

pp. 22 ◽

Cited By ~ 5

Author(s):

Pierre Perron ◽

Yohei Yamamoto

Keyword(s):

Linear Regression ◽

Regression Models ◽

Structural Changes ◽

Error Variance ◽

Regression Coefficients ◽

Linear Regression Models ◽

Finite Sample ◽

Finite Sample Properties ◽

Joint Approach ◽

Size Distortions

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power.

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Testing a sub-hypothesis in linear regression models with long memory covariates and errors

Applications of Mathematics ◽

10.1007/s10492-008-0007-z ◽

2008 ◽

Vol 53 (3) ◽

pp. 235-248 ◽

Cited By ~ 3

Author(s):

Hira L. Koul ◽

Donatas Surgailis

Keyword(s):

Linear Regression ◽

Long Memory ◽

Regression Models ◽

Linear Regression Models

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Comparison study on the confidence intervals in linear regression models with restricted parameter space

10.47302/jsr.2018520201 ◽

2019 ◽

Vol 52 (2) ◽

pp. 115-127

Author(s):

XIUQIN BAI ◽

WEIXING SONG

Keyword(s):

Linear Regression ◽

Empirical Likelihood ◽

Regression Models ◽

Confidence Region ◽

Estimation Procedure ◽

Regression Coefficients ◽

Comparison Study ◽

Linear Regression Models ◽

Finite Sample ◽

Confidence Estimation

This paper proposes an empirical likelihood confidence region for the regression coefficients in linear regression models when the regression coefficients are subjected to some equality constraints. The shape of the confidence set does not depend on the re-parametrization of the regression model induced by the equality constraint. It is shown that the asymptotic coverage rate attains the nominal confidence level and the Bartlett correction can successfully reduce the coverage error rate from $O(n^{-1})$ to $O(n^{-2})$, where n denotes the sample size. Simulation studies are conducted to evaluate the finite sample performance of the proposed empirical likelihood empirical confidence estimation procedure. Finally, a comparison study is conducted to compare the finite sample performance of the proposed and the classical ellipsoidal confidence sets based on normal theory.

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Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory

Journal of Time Series Analysis ◽

10.1111/jtsa.12451 ◽

2019 ◽

Vol 40 (4) ◽

pp. 493-518

Author(s):

Hira L. Koul ◽

Donatas Surgailis

Keyword(s):

Measurement Error ◽

Linear Regression ◽

Least Squares ◽

Asymptotic Distribution ◽

Long Memory ◽

Regression Models ◽

Linear Regression Models ◽

Least Squares Estimators

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FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

2018 ◽

Vol 23 (1) ◽

pp. 60-71

Author(s):

Wigiyanti Masodah

Keyword(s):

Interest Rate ◽

Linear Regression ◽

Interest Rates ◽

Regression Models ◽

Linear Regression Models ◽

Negative Impacts ◽

Main Activity ◽

The Impact ◽

The Given ◽

Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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