Lag Length Selection Research Articles

Financial Risks in Turkish Banking Industry: A Panel Data Analaysis on Istanbul Stock Exchange

Global price movements have been affecting markets dramatically in recent years. The changes in exchange rates, interest rates, and liquidity directly affect market value of firms. These risks are called financial risks and typically affect financial institutions. Many methods are developed to compute these risks. This study has a panel data analysis on 7 banks listed on Istanbul Stock Exchange. The motivation of this study is to investigate the relationship between financial risks (interest rate risk, exchange rate risk and liquidity risk) and market value of these banks. Many tests are available in the research such as VIF, AR Roots, Lag Length Selection Criteria, Cross Section Dependence Test, Delta Test, Unit Root Tests, Model Selection Tests, Heteroscedasticity and Autocorrelation Tests. Based on the tests, two way fixed effects model is developed. The results reveal that financial risks explain 29% of all price movements of commercial banks. The model is statistically significant. There is a positive relationship between liquidity and market value and negative relationships between interest rate risk and market value, and exchange rate risk and market value. The results are also consistent with the literature. The research is unique for the Turkish Banking industry and therefore is important academically as well as for risk management practice. Results show that banks operating in Turkey don’t properly manage financial risks. Macroeconomic dynamics and maturity mismatch problems in Turkey require great attention on financial risks. It is recommended that banks should operate with more risk management instruments such as financial derivatives and corporate risk management.

An experimental study on the precursory characteristics of EP before sandstone failure based on critical slowing down

The electric potential (EP) generated during coal rock deformation and fracture reflects coal rock mass stress and damage states. However, identifying the EP signal precursory signs that characterize a coal rock mass tending toward instability and failure is difficult. In this paper, we established an acoustic-electric multiparameter test experimental system, synchronously tested and collected acoustic emission (AE), surface potential (SP), and axial strain (AS) and observed surface crack propagation during the sandstone sample failure process. Meanwhile, by introducing the critical slowing down theory, the EP signal precursory characteristics before the sandstone sample unstable failure were explored. The results showed that EP signal time series autocorrelation coefficients and variances increased abruptly and continuously before the main fracture of the sandstone samples, which could be used as sandstone failure precursory information. Compared with the autocorrelation coefficient, the variance mutation point was easy to identify and was not affected by lag length selection, which was an effective precursory point to predict sandstone sample failure. From the precursory point to the main fracture of the sandstone samples, the AE events gradually gathered at the fracture damage location, high-energy AE events gradually increased, and the crack propagation rate gradually accelerated. Compared with the AE count time series, the variance mutation point (precursory point) of the EP signal time series appeared earlier. The research results were of theoretical significance for the monitoring and early warning of coal rock mass instability and failure and for identifying precursory characteristics using the EP response.

Do Governance Indicators Matter for Economic Growth? The Case of Sri Lanka

This study attempts to identify the impact of governance indicators on economic growth using time series data for Sri Lanka from 1996 to 2016 published by the World Bank. The Phillips–Perron (PP) unit root test confirmed that all the variables are integrated in order one and suggested the use of cointegration technique to identify the long-run relationship between the variables. All the lag length selection criteria except Schwarz Information Criterion (SIC) advocated the use of one lag as an optimal lag length for this study. Johansen cointegration method detected three cointegrating relationships among the variables. Further, this technique identified a significant and positive relationship between government effectiveness (GE) and gross domestic product per capita (GDPPC) in the long run. This result is in contrast to all the three traditional approaches, such as correlation test, scatter plot and ordinary least squared (OLS), in which they do not identify any clear relationship between them. Moreover, Johansen test found a negative and statistically significant link between political stability and absence of violence (PSAV) and GDPPC in the long run, while all three traditional approaches identified a positive correlation between them. The findings of this study indicate a negative association between rule of law (ROL) and GDPPC in the long run, which coincides with theory, some of the empirical studies and with findings of all three traditional approaches used in this study. Even though OLS did not identify a significant relationship between control of corruption (COC) and GDPPC, Johansen test, correlation test and scatter plot detected a significant and negative correlation between them in the long run as expected by the theoretical evidence. Granger’s causality test identified the bidirectional causality between GE and ROL and unidirectional causality between ROL and COC. However, relationship between governance variables and GDPPC vary based on the estimation methods. These findings suggest that the policymakers need to take considerable attention on the above when they formulate and implement policy to improve GE.

The sensitivity of unit root tests to the initial condition and to the lag length selection: A Monte Carlo Simulation Study

While the recent literature has discussed the effect of the deviation of the initial observation of the economic series from its deterministic component (initial condition) on unit root tests, no studies have examined to date the effect of the selection of the lag length on unit root tests in this setting. Our study aims to fill this gap, and provides a recommendation for the practitioner. The objective is to investigate to what extent the sensitivity of the outcome of unit root tests to the initial condition changes with the use of both standard and modified data-dependent methods to select the lag length in the augmented autoregression, even for those tests that have been considered robust in the presence of uncertainty about the initial condition. To do so, we conduct a Monte Carlo simulation study to analyse the finite sample properties (size and power) of unit root tests based on alternative lag selection criteria and different magnitudes of the initial condition.

The Impact of Transportation Infrastructure on Economic Growth: Empirical Evidence from Saudi Arabia

The transport sector is an important factor of economic activity, where it contributes directly to economic activities and employment. The road has a large indirect impact on all the other sectors and activities in the economy. The study aims to investigate the causality relations between road land and economic growth in Saudi Arabia. The study is based on secondary data gained from Saudi Arabia Monetary Agency and World Bank over the period of1988 to2017. The Granger causality test was used to investigate the relationship between the variables with Akiake Lag Length Selection Information Criteria, while Vector Autoregtression (VAR) model was used in order to find the causality. The result reveals unidirectional causality form real GDP to road; however, there is no evidence to support that transportation infrastructure is the cause of economic growth. Granger causality from GDP to investment in infrastructure indicates that reinvestment in infrastructure is caused by economic growth and not vice versa. Economic growth drove pressures on existing transport infrastructure and required additional investment. The finding is in line with the commonly accepted notion advocating that economic growth or development provide necessary financial and technical support for transportation infrastructure investment and improvement.

Relationship between Money Supply, Income and Price Level in Nepal

Using the data from 1974/75 to 2017/18, this paper intended to find out the relationship between money supply, income and price level in Nepal. The paper has established the relationship between real money supply (both M1 and M2) with respect to real GDP, nominal money supply (both M1 and M2) with respect to price level and nominal GDP with respect to price level separately. The econometric tools such as ADF for unit root tests, SIC for lag length selection, bivariate Johansen Cointegration tests followed by VECM has been used for long-run causality. Further, VEC as well as VAR Granger Causality/Block Exogeneity Wald tests for short-run causality are used. The paper found bidirectional longrun causality between the real income with respect to both type of money supply in real terms. But there is no evidence of short run causation between these variables. Likewise, the study found the unidirectional long-run relationship runs from narrow money supply to consumer price. However, there is no short-run relationship from either side. Accordingly, there is no evidence of long-run as well as short-run relationship between broad money supply and consumer price level. Lastly, there is no evidence of long-run causality between nominal GDP and general price level. But the study found unidirectional short-run causality running from general price to nominal GDP. The results suggest that Nepal should focus on growth of time deposit component of broad money supply in long-run for economic growth and control of inflation.

Unrestricted Vector Autoregressive Modelling of the Interaction among Oil Price, Exchange Rate and Inflation in Nigeria (1981–2017)

The interdependence among oil prices, exchange rates and inflation rates, and their response to shocks, was a cause of concern. Unrestricted Vector Autoregression (UVAR) was employed to analyse this interactions as well as to investigate the pattern of causality among the study variable. Annual data spanning from 1981 to 2017 was sourced from the Statistical Bulletin of the Central Bank of Nigeria. Pre-estimation analysis showed that all variables were integrated of order one 1(1), and there no cointegrating relationship. The inverse root of AR characteristic polynomial showed a stable VAR model. All lag length selection criteria chose a lag length of 1. The UVAR estimates and the test of significance particularly the granger causality test indicated significant influence and uni-directional effect from oil price to exchange rates. The Wald statistics, showed significant own shocks, and the impulse response showed that all variables were instantaneously affected by own shocks. Exchange rate was instantaneously affected by oil price; however, it ruled out the response in inflation rate to contemporaneous shocks in oil price. The variance decomposition further showed that at least 93.1%, 97.1% and 92.4% of the impulse response in oil price, exchange rate, and inflation rate respectively were from own shocks in the long run. The post estimation analysis showed that the VAR model was multivariate normal, the residual was homoscedastic, and there was no serial autocorrelation. It was recommended that the government should diversify the national income stream and consider policies that will control inflation.

Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models

The academic system incentivizes p-hacking, where researchers select estimates and statistics with statistically significant p-values for publication. We analyze the complete process of Granger causality testing including p-hacking using Monte Carlo simulations. If the degrees of freedom of the underlying vector autoregressive model are small to moderate, information criteria tend to overfit the lag length and overfitted vector autoregressive models tend to result in false-positive findings of Granger causality. Researchers may p-hack Granger causality tests by estimating multiple vector autoregressive models with different lag lengths and then selecting only those models that reject the null of Granger non-causality for presentation in the final publication. We show that overfitted lag lengths and the corresponding false-positive findings of Granger causality can frequently occur in research designs that are prevalent in empirical macroeconomics. We demonstrate that meta-regression models can control for spuriously significant Granger causality tests due to overfitted lag lengths. Finally, we find evidence that false-positive findings of Granger causality may be prevalent in the large literature that tests for Granger causality between energy use and economic output, while we do not find evidence for a genuine relation between these variables as tested in the literature.

Direct multiperiod forecasting for algorithmic trading

AbstractThis paper examines the performance of iterated and direct forecasts for the number of shares traded in high‐frequency intraday data. Constructing direct forecasts in the context of formulating volume weighted average price trading strategies requires the generation of a sequence of multistep‐ahead forecasts. I discuss nonlinear transformations to ensure nonnegative forecasts and lag length selection for generating a sequence of direct forecasts. In contrast to the literature based on low‐frequency macroeconomic data, I find that direct multiperiod forecasts can outperform iterated forecasts when the conditioning information set is dynamically updated in real time.

Unit Roots and Structural Breaks

This special issue deals with problems related to unit roots and structural change, and the interplay between the two.[...]

Crude Oil Price Fluctuation And Inflation In Nigeria

This research empirically investigated the relationship between inflation and oil price fluctuations in Nigeria with quarterly data over the period 1980:1 to 2015:4.Using secondary data obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin 2015 and Energy Information Administration (EIA) short-term outlook, December, 2016. Vector autoregressive model econometric technique was adopted in analysing the data for this study with the following analysis carried out; the Lag length selection, VAR stability tests, VAR LM test for serial correlation, the Impulse Response Function and the Forecast Error Variance Decomposition. The study revealed that the response of inflation to oil price fluctuation is that as oil prices falls inflation fall also and a stable and positive oil price results in a stable negative inflation rate, thus the relationship between them( inflation and oil price fluctuation) is negative This study therefore recommends that the cost channels transmission should be weakened by further diversification of the Nigeria economy and enhancement of import substitution strategies. To facilitate the monetary authority in achieving lower inflation and stable economic growth targets counter-cyclical fiscal policies and effectively binding fiscal rules should be formulated and implemented.

Testing for Explosive Bubbles in the Presence of Autocorrelated Innovations

We analyze an empirically important issue with recursive right-tailed unit root tests for bubbles in asset prices. First, we show that serially correlated innovations, which is a feature that is present in most financial series used to test for bubbles, can lead to severe size distortions when using either fixed or automatic (based on information criteria) lag-length selection in the auxiliary regressions underlying the test. Second, we propose a sieve-bootstrap version of these tests and show that this results in tests which control size well across a number of simulation designs both with and without highly autocorrelated innovations. We also find that these improvements in size come at a relatively low cost for the power of the tests. Finally, we apply the bootstrap tests on the housing market of OECD countries, and generally find much weaker evidence of housing bubbles compared to existing evidence.

Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data.

Stationarity‐Based Approach for the Selection of Lag Length in Cointegration Analysis Used for Structural Damage Detection

AbstractCointegration has been recently brought to structural health monitoring (SHM) as a new methodology for dealing with the problem of environmental and/or operational variability in monitored structures. However, it is well known that the choice of lag length in cointegration analysis has a strong influence on damage detection results. The article presents a new approach for optimal lag length selection in cointegration analysis used for structural damage detection. This new method is based on stationarity analysis of data representing undamaged condition. The proposed method is validated using Lamb wave data under the effects of temperature variations and vibroacoustic data obtained from nonlinear vibroacoustic modulation experiments with different low‐frequency vibration (or modal) excitations. The results demonstrate the effectiveness of the method for structural damage detection based on SHM data heavily affected by environmental or operational conditions.

Determining the Stationarity Distance via a Reversible Stochastic Process.

The problem of controlling stationarity involves an important aspect of forecasting, in which a time series is analyzed in terms of levels or differences. In the literature, non-parametric stationary tests, such as the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, have been shown to be very important; however, they are affected by problems with the reliability of lag and sample size selection. To date, no theoretical criterion has been proposed for the lag-length selection for tests of the null hypothesis of stationarity. Their use should be avoided, even for the purpose of so-called ‘confirmation’. The aim of this study is to introduce a new method that measures the distance by obtaining each numerical series from its own time-reversed series. This distance is based on a novel stationary ergodic process, in which the stationary series has reversible symmetric features, and is calculated using the Dynamic Time-warping (DTW) algorithm in a self-correlation procedure. Furthermore, to establish a stronger statistical foundation for this method, the F-test is used as a statistical control and is a suggestion for future statistical research on resolving the problem of a sample of limited size being introduced. Finally, as described in the theoretical and experimental documentation, this distance indicates the degree of non-stationarity of the times series.

The Lag-length Selection and Detrending Methods for HEGY Seasonal Unit-root Tests Using Stata

The article extends the previous Hylleberg, Engle, Granger, and Yoo seasonal unit-root test commands (Baum and Sperling, 2001, https://ideas.repec.org/c/boc/bocode/s416502.html ; Depalo, 2009, Stata Journal 9: 422–438), which allow for the use of both quarterly and monthly data. It is also possible to choose between ordinary least-squares and generalized least-squares detrending (Rodrigues and Taylor, 2007, Journal of Econometrics 141: 548–573) procedures to deal with the deterministic part of the process. The command allows for the use of the sequential method proposed by Hall (1994, Journal of Business and Economic Statistics 12: 461–470) and Ng and Perron (1995, Journal of the American Statistical Association 90: 268–281), the adaptation of the modified Akaike information criteria to the case of seasonal unit-root tests (del Barrio Castro, Osborn, and Taylor, 2016, Econometric Reviews 35: 122–168) as well as the inclusion of Akaike information and Bayesian information criteria to determine the order of augmentation of the serial correlation in the augmented Hylleberg, Engle, Granger, and Yoo regression. Finally, the use of the command is illustrated with an empirical application to the case of monthly passenger airport arrivals to Palma de Mallorca.

(WHEN) DO LONG AUTOREGRESSIONS ACCOUNT FOR NEGLECTED CHANGES IN PARAMETERS?

To construct forecasts for time series exhibiting breaks, the paper examines long autoregressions, where the number of lags is growing with T, and possible breaks are simply ignored. The paper shows that the OLS estimators are still elementwise consistent for the true autoregressive coefficients when neglecting a break in mean, but the sum of the estimators converges to unity. Thanks to this unit-root like behavior of the fitted model, the resulting conditional forecasts are consistent for the true values. As long as the dynamic structure is invariant, the robustness property of the forecasts holds a) under data-dependent lag length selection, b) for a piecewise smoothly varying mean function, and c) under general autoregressive dynamics of possibly infinite order including stationary long memory. Under breaks in the dynamic structure, however, estimators are asymptotically biased, and the forecasts from long autoregressions are biased themselves even in the limit.

Relationships among Per Capita GDP, Agriculture and Manufacturing Sectors in India

The objective of the present paper is to examine causal relationship among per capita GDP, agriculture and manufacturing sector output in India using time series data collected from CSO for the period 1970 to 2013. The study conducts an econometric investigation by applying methodologies, viz., Stationary tests, and Johansen’s Cointegration test, Johansen’s Vector Error Correction Model (VECM) in VAR and Impulse Response Function and Variance Decomposition Analysis. We have carried out the ADF and KPSS unit root tests. According to AIC and HQC criteria, the appropriate lag length is 2. Both the variables in log terms being I (1), Johansen’s co-integration test confirms one long run relationships among the variables at 5% significance level. It reveals that there exists bidirectional causality between agriculture sector and per GDP, while the unidirectional causality between the manufacturing sector and per capita GDP and between the agriculture and manufacturing sector. As the VECM involve selection of appropriate lag length. According to AIC criterion and Hannan-Quinn criterion; two lag lengths is considered to the present analysis. The signs of both the coefficients of the cointegrated equations are as expected and their magnitudes are responsible. However, results based on vector error correction model indicate a weak association between the sectors in the short run. Dynamic causality results show that the agriculture sector affects Per Capita GDP and manufacturing sector, while Per Capita GDP affects manufacturing sector strongly in the long run, thus causality seems to run from agriculture to PCGDP and manufacturing and from PCGDP to Manufacturing sector. It is also evident from impulse response function that any innovation in agriculture sector increases its own growth as well as growth of manufacturing sector in India. In consistence with the above finding, the present study argues that shocks originated from agriculture sector spill over to Per Capita GDP and Manufacturing sector in long run in India.

Lag length specification in Engle-Granger cointegration test: a modified Koyck mean lag approach based on partial correlation

The Engle-Granger cointegration test is highly sensitive to the choice of lag length and the poor performance of conventional lag selection criteria such as standard information criteria in selecting appropriate optimal lag length for the implementation of the Engle-Granger cointegration test is well-established in the statistical literature. Testing for cointegration within the framework of the residual-based Engle-Granger cointegration methodology is the same as testing for the stationarity of the residual series via the augmented Dickey-Fuller test which is well known to be sensitive to the choice of lag length. Given an array of candidate optimal lag lengths that may be suggested by different standard information criteria, the applied researchers are faced with the problem of deciding the best optimal lag among the candidate optimal lag lengths suggested by different standard information criteria, which are often either underestimated or overestimated. In an attempt to address this well-known major pitfall of standard information criteria, this paper introduces a new lag selection criterion called a modified Koyck mean lag approach based on partial correlation criterion for the selection of optimal lag length for the residual-based Engle-Granger cointegration test. Based on empirical findings, it was observed that in some instances over-specification of lag length can bias the Engle-Granger cointegration test towards the rejection of a true cointegration relationship and non-rejection of a spurious cointegration relationship. Using real-life data, we present an empirical illustration which demonstrates that our proposed criterion outperformed the standard information criteria in selecting appropriate optimal truncation lag for the implementation of the Engle-Granger cointegration test using both augmented Dickey-Fuller and generalized least squares Dickey-Fuller tests.

Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility

A number of recent papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, taken the lag length in the unit root test regression to be a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. We investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent lag selection methods in augmented Dickey–Fuller type regressions and propose new lag selection criteria which allow for unconditional heteroskedasticity. Standard lag selection methods are shown to have a tendency to over-fit the lag order under heteroskedasticity, resulting in significant power losses in the (wild bootstrap implementation of the) augmented Dickey–Fuller tests under the alternative. The proposed new lag selection criteria are shown to avoid this problem yet deliver unit root tests with almost identical finite sample properties as the corresponding tests based on conventional lag selection when the shocks are homoskedastic.