Testing for Serial Correlation in Regression with Missing Observations

A Supplementary Mimeo to the Paper Titled 'The Adjustment of the Yule-Walker Relations in VAR Modeling: The Impact of the Euro on the Hong Kong Stock Market'

The effects of serially correlated residuals on the accuracy of linear regression are considered, and remedies are suggested. The Cochrane‐Orcutt method specifically remedies the effects of serially correlated residuals and yields more accurate regression coefficients than does ordinary least squares. We illustrate the effects of serially correlated residuals, explain the application of the CO method, and evaluate the gains to be achieved in its use. We apply the method to an example from climate reconstruction, and we show that the effects of serial correlation in residuals are present and show the significantly improved result.

This paper examines the determinants of foreign direct investment (FDI) for the Eastern Region of China as a whole by excluding the two lesser jurisdictions and concentrating on the Big Five jurisdictions from 1993 to 2005. Robust estimates and a better understanding of FDI in Eastern China are obtained by employing robust Ordinary Least Squares (OLS), the randomeffect model, and the fixed-effect model separately on each case. Without any outliers and serial correlation in residuals, the Big Five case gives us the most reliable estimates of determinants of FDI in Eastern China. Market size and labor quality are found to have a positive, significant, and quantitatively large effect on FDI in Eastern China, whereas education and infrastructure have a statistically nonsignificant positive direct effect on FDI. As for the level of local physical investment, we found a small crowding-out effect on FDI in the Big Five case.

Sub-Sahara Africa harbours most of the Malaria burden including Nigeria. There are scanty studies that aim at modelling these cases particularly in the study area. This study therefore, focused on a multivariate time series model for malaria cases among the residents in Jimeta metropolis of Adamawa State. A secondary data on reported malaria cases for adults, pregnant women and children was collected from January 2011 to December 2020 on monthly basis from medical records at the specialist Hospital, Jimeta, Yola, Adamawa State. The vector autoregressive (VAR) model was employed for modelling. A descriptive analysis was performed on the data. The lag order selection for stationary VAR model suggest lag three as the optimal lag for VAR model with malaria cases among children, adult and pregnant women. To assess how well the model fit the data set, AIC of 26.9458 for model with lag (3) was best. The Breusch-Godfrey LM test for residual serial correlation of VAR model suggest no autocorrelation at each lag, there is no problem of autocorrelation, since the associated p-value is greater than the conventional 0.05 level of significance. Jarque-Bera test shows that the residuals are not normally distributed and the forecast made showed that, rates of malaria cases are higher among adult followed by children and then pregnant women.

Multi-scale tests for serial correlation

By counting the “peaks” (relative maxima) of a time series, it is possible to estimate the serial correlation in the series, under the assumption that the series is a first-order autoregressive process. The estimator is inefficient, but it is effective nonetheless, and since all computations can be performed mentally and no tables are required, it is suitable for the quick assessment of computer-generated diagnostic plots. It also enjoys certain robustness properties. As a test for serial correlation, the procedure is essentially the same as one proposed by Bienayme in 1874 and by Wallis and Moore in 1941, which is itself equivalent to tests based on the counts of runs up and down.

Standard serial correlation tests are derived assuming that the disturbances are homoscedastic, but this study shows that asympotic critical values are not accurate when this assumption is violated. Asymptotic critical values for the ARCH(2)-corrected LM, BP and BL tests are valid only when the underlying ARCH process is strictly stationary, whereas Wooldridge's robust LM test has good properties overall. These tests exhibit similar bahaviour even when the underlying process is GARCH (1,1). When the regressors include lagged dependent variables, the rejection frequencies under both the null and alternative hypotheses depend on the coefficientsof the lagged dependent variables and the other model parameters. They appear to be robust across various disturbance distributions under the null hypothesis.

This paper considers testing the null hypothesis that a times series is uncorrelated when the time series is uncorrelated but statistically dependent. This case is of interest in economic and finance applications. The GARCH(1, 1) model is a leading example of a model that generates serially uncorrelated but statistically dependent data. The tests of serial correlation introduced by Andrews and Ploberger (1996, hereafter AP) are generalized for the purpose of testing the null. The rationale for generalizing the AP tests is that they have attractive properties for cases for which they were originally designed: they are consistent against all nonwhite-noise alternatives and have good all-round power against nonseasonal alternatives compared to several widely used tests in the literature. These properties are inherited by the generalized AP tests.

The author considers serial correlation testing in seasonal time series models. He proposes a test statistic based on a spectral approach. Many tests of this type rely on kernel‐based spectral density estimators that assign larger weights to low order lags than to high ones. Under seasonality, however, large autocorrelations may occur at seasonal lags that classical kernel estimators cannot take into account. The author thus proposes a test statistic that relies on the spectral density estimator of Shin (2004), whose weighting scheme is more adapted to this context. The distribution of his test statistic is derived under the null hypothesis and he studies its behaviour under fixed and local alternatives. He establishes the consistency of the test under a general fixed alternative. He also makes recommendations for the choice of the smoothing parameters. His simulation results suggest that his test is more powerful against seasonality than alternative procedures based on classical weighting schemes. He illustrates his procedure with monthly statistics on employment among young Americans.

In Search for a Robust Method for Estimating Dynamic Panel Data Models of Capital Structure

Testing for random effects and serial correlation in spatial autoregressive models

Journal Article The Determinants of Equilibrium Unemployment in Britain: A Reply Get access S. J. Nickell S. J. Nickell Centre for Labour Economics, London Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 95, Issue 377, 1 March 1985, Pages 196–198, https://doi.org/10.2307/2233480 Published: 01 March 1985

As an extension of partially linear models and additive models, partially linear additive model is useful in statistical modelling. This paper proposes an empirical likelihood based approach for testing serial correlation in this semiparametric model. The proposed test method can test not only zero first-order serial correlation, but also higher-order serial correlation. Under the null hypothesis of no serial correlation, the test statistic is shown to follow asymptotically a chi-square distribution. Furthermore, a simulation study is conducted to illustrate the performance of the proposed method.

This study aims to examine the impact of asset quality on the financial health of commercial banks in Cambodia. Using an explanatory approach with panel data analysis, the research analyzes annual data from 2017 to 2023 for 57 commercial banks, sourced from the National Bank of Cambodia. The analysis explores the effects of Non-Performing Loans (NPLs), Bank Size, Capital Adequacy Ratio (CAR), Interest Rates, Inflation Rate, and GDP Growth on Return on Assets (ROA) and Return on Equity (ROE). Diagnostic tests for heteroskedasticity, serial correlation, multicollinearity, and model specification are employed to ensure the robustness of the results. The findings indicate that NPLs significantly negatively impact both ROA and ROE, while Bank Size positively influences ROE but does not significantly affect ROA. CAR has a negative impact on both profitability metrics, suggesting that higher capital ratios reduce profitability due to increased capital costs. Interest Rates and Inflation Rate also show significant negative effects on ROA and ROE, whereas GDP Growth positively impacts both measures. To enhance financial health and performance, Cambodian commercial banks should focus on improving credit risk management, optimizing CAR, developing strategies to manage interest rate and inflation risks, and leveraging economic growth. These measures will strengthen financial health, improve performance metrics, and contribute to a more stable banking sector in Cambodia. Keywords: Asset Quality, Financial Health, Commercial Banks, Non-Performing Loans (NPLs), Bank Size, Capital Adequacy Ratio (CAR), Interest Rates, Inflation Rate, GDP Growth, Return on Assets (ROA), Return on Equity (ROE).

Specification Tests for Spatial Panel Data Models