SPURIOUS REGRESSION ANALYSIS ON TIME SERIES DATA FROM FACTORS AFFECTING INDONESIAN HUMAN DEVELOPMENT INDEXS IN 1990 – 2017

In a spurious regression conditions occur linear regression equations that are not stationary on the mean and variance. If the variables are not stationary, there will be cointegration, so it can be concluded that there is a long-term equilibrium relationship between the two research variables and in the short term there is a possibility of an imbalance, so to overcome it in this study using the Error Correction Model. The purpose of this study is to apply a cointegration test to see whether there is a long-term non-equilibrium relationship between the time series between the Human Development Index and life expectancy at birth, average school year for adults aged 25 years and over and gross national income per capita. The data used in this study are time series data between 1990-2017. The statistical management is carried out using Eviews 10. Based on the results obtained, it was concluded that 81.7% and it can be said that the types of independent variables included in the model are already good, because only 18.3% of the diversity of the dependent variable is influenced by the independent variables outside this research model. Keywords: spurious regression, stationary, cointegration, error correction model, equilibrium

ARDL as an Elixir Approach to Cure for Spurious Regression in Nonstationary Time Series

In conventional Econometrics, the unit root and cointegration analysis are the only ways to circumvent the spurious regression which may arise from missing variable (lag values) rather than the nonstationarity process in time series data. We propose the Ghouse equation solution of autoregressive distributed lag mechanism which does not require additional work in unit root testing and bound testing. This advantage makes the proposed methodology more efficient compared to the existing cointegration procedures. The earlier tests weaken their position in comparison to it, as they had numerous linked testing procedures which further increase the size of the test and/or reduce the test power. The simplification of the Ghouse equation does not attain any such type of error, which makes it a more powerful test as compared to widely cited exiting testing methods in econometrics and statistics literature.

International Trade and Economic Performance in Tanzania: Further Evidence

The study investigated the role of international trade in economic performance in Tanzania for the post reform period, from 1980 to 2018. International trade is measured by disaggregated imports and exports while economic performance is measured by GDP growth. Exports are disaggregated into manufactured goods and non-manufactured goods while imports are disaggregated into capital goods and intermediate goods. To obtain robust non-spurious regression results, Dickey-Fuller (D-F) and Phillips-Peron (PP) Unit Root tests were performed. Johansen Co-integration tests were employed to investigate long-run relationships between export, imports and economic growth. The Johansen test suggested a long-run relationship between international trade and its components and economic development. In addition, the Error Correction Model (ECM) results further supported a long-run relationship between international trade and economic growth in Tanzania. This calls for further opening of the economy and further liberalisation of trade restrictions.

Spurious Factor Analysis

This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the nonstationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.

LSTUR regression theory and the instability of the sample correlation coefficient between financial return indices

Summary It is well known that the sample correlation coefficient between many financial return indices exhibits substantial variation on any reasonable sampling window. This stylised fact contradicts a unit root model for the underlying processes in levels, as the statistic converges in probability to a constant under this modeling scheme. In this paper, we establish asymptotic theory for regression in local stochastic unit root (LSTUR) variables. An empirical application reveals that the new theory explains very well the instability, in both sign and scale, of the sample correlation coefficient between gold, oil, and stock return price indices. In addition, we establish spurious regression theory for LSTUR variables, which generalises the results known hitherto, as well as a theory for balanced regression in this setting.

HAR Testing for Spurious Regression in Trend

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips 1986, 1998; Sun 2004, 2014b) the usual t test and HAC standardized test fail to control size as the sample size n → ∞ in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors K → ∞ , all three statistics, including the HAR test, diverge and fail to control size as n → ∞ . These findings are relevant to high-dimensional nonstationary time series regressions where machine learning methods may be employed.

Random Walks, Unit Roots, and Spurious Relationships

This chapter outlines pitfalls of using standard inference procedures common in cross- sectional settings in time series settings and presents alternative procedures. It also addresses the issue of spurious regression and cautions the reader against the unquestioning use of cross section tools in time series settings.

MODEL KOREKSI KESALAHAN (ECM) PADA KASUS DATA RUNTUN WAKTU INDEKS HARGA KONSUMEN DI JAWA TENGAH

Model koreksi kesalahan (ECM) berfungsi untuk membentuk hubungan jangka panjang, mengoreksi ketidakseimbangan jangka pendek, mengatasi masalah data runtun waktu yang tidak stasioner dan mengatasi masalah regresi lancung (spurious regression). Diperlukan lima langkah dalam model koreksi kesalahan yaitu melakukan uji akar unit (ADF), melakukan uji kointegrasi Engle-Granger, estimasi model koreksi kesalahan Engle Granger dan Domowitz-Elbadawi, melakukan signifikan parameter, dan pemilihan model yang terbaik dengan membandingkan kriteria nilai AIC. Penelitian ini bertujuan untuk menerapkan model koreksi kesalahan pada kasus data runtun waktu indeks harga konsumen (IHK) di Jawa Tengah dan membandingkan model koreksi kesalahan Engle Granger dan Domowitz-Elbadawi dengan menggunakan kriteria pembanding nilai AIC. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diperoleh dari BPS berupa data bulanan IHK berdasarkan empat kelompok pengeluaran dari tahun 2014 sampai dengan tahun 2017. Hasil penelitian menunjukkan bahwa penerapan model koreksi kesalahan yang digunakan adalah valid (sesuai) dan perbandingan menggunakan nilai AIC dari kedua model koreksi kesalahan diperoleh model koreksi kesalahan Engle Granger mempunyai kemampuan yang baik. Perolehan nilai AIC pada model koreksi kesalahan untuk masing-masing data IHK berdasarkan empat kelompok pengeluaran sebesar 0,4399 dan 1,1601 yang menunjukkan model koreksi kesalahan Engle Granger merupakan model yang lebih baik digunakan dari model koreksi kesalahan Domowitz-Elbadawi.Kata Kunci : Uji Akar Unit, Kointegrasi, Model Koreksi Kesalahan (ECM)