Logistic Smooth Transition Autoregressive Research Articles

Femtocell base station clustering and logistic smooth transition autoregressive‐based predicted signal‐to‐interference‐plus‐noise ratio for performance improvement of two‐tier macro/femtocell networks

The aim of this study is to improve the performance of two-tier macro/femtocell networks using a power control approach. In wireless networks, power control plays an important role in improving a number of performance parameters such as co-channel interference and outage probability reduction, throughput increasing, and power saving. This study explores the evolution of centralised power control algorithm based on femtocell base station (FBS) clustering and predicted signal-to-interference-plus-noise ratio (SINR) of users. To reduce the computational complexity of centralised algorithm, dense deployed femtocells are considered in different clusters. In this case, femtocells inside one cluster make considerable interference to each other, while the interferences from femtocells of other clusters are negligible. Moreover, because of the non-linearity of SINR samples, non-linear logistic smooth transition autoregressive (LSTAR) model is used to model the SINR data, and then the next SINR samples are predicted from the previous samples. According to the clustered FBSs and predicted SINR, the proposed power control scheme is applied to femtocell network in the downlink. The results demonstrate that the introduced method improves the outage probability and throughput and outperforms previous methods significantly.

THE ASYMMETRIC EFFECTS OF UNCERTAINTY ON MACROECONOMIC ACTIVITY

We estimate a number of macroeconomic variables as logistic smooth transition autoregressive (LSTAR) processes with uncertainty as the transition variable. The notion is that the effects of increases in uncertainty should not be symmetrical with the effects of decreases in uncertainty. Nonlinear estimation allows us to answer several interesting questions left unanswered by a linear model. For a number of important macroeconomic variables, we show that (i) a positive shock to uncertainty has a greater effect than a negative shock and (ii) the effect of the uncertainty shock is highly dependent on the state of the economy. Hence, the usual linear estimates for the consequences of uncertainty are underestimated in circumstances such as the recent financial crisis.

Time-Varying Persistence of Inflation: Evidence from a Wavelet-Based Approach

We propose a new long-memory model with a time-varying fractional integration parameter, evolving non-linearly according to a Logistic Smooth Transition Autoregressive (LSTAR) specification. To estimate the time-varying fractional integration parameter, we implement a method based on the wavelet approach, using the instantaneous least squares estimator (ILSE). The empirical results show the relevance of the modeling approach and provide evidence of regime change in inflation persistence that contributes to a better understanding of the inflationary process in the US. Most importantly, these empirical findings remind us that a one-size-fits-all monetary policy is unlikely to work in all circumstances.

The Dynamics of Stock Price Adjustment to Fundamentals: An Empirical Essay via STAR Models in the Tunisian Stock Market

This paper examines the dynamics of stock prices adjustment to fundamental value proxied by dividend per share and earnings per share on the Tunisian stock market based on the cointegration techniques. First, the linear cointegration between stock prices and fundamental values is examined by using the Johansen’s cointegration test. The empirical results indicate a linear cointegrating relationship between stock prices and dividend per share, and not between stock prices and earnings per share, in support of a linear mean reversion of stock prices towards its fundamental value proxied by dividend. To further investigate the cointegration between stock prices and earnings per share in a nonlinear context, we have modelled the deviation of stock prices away from EPS by a logistic smooth transition autoregressive (LSTAR) model. Our results indicate that this model cannot capture the nonlinearity of this deviation, failing, then, to give evidence of a nonlinear cointegration between stock prices and EPS. These results suggest that when selecting stocks, Tunisian investors should focus on the underlying performance of stocks only in terms of their dividend per share.

Unemployment Convergence in Central and Eastern European Countries: Driving Forces and Cluster Behavior

ABSTRACTEmploying a nonlinear logistic smooth transition autoregression system and comovement analysis, we find that the German business cycle has acted as a common driver affecting the cyclical behavior of unemployment rates in Central and Eastern European countries. In addition, we identify two convergence clubs in unemployment dynamics. The first comprises the Baltic States, Hungary, and Poland, and the second group of countries is composed of the Czech Republic and Slovakia. Interestingly, this classification matches the labor market policies and institutional divergences observed among these countries.

Matching perception with the reality—Performance of Islamic equity investments

The systematic failure of the global equity markets during the recent financial crisis made investors re-evaluate their portfolio constituents. It is argued that equities that comply with the Islamic investment principles perform better than conventional equities during the declining phase of capital markets. The better performance of Islamic investments can be attributed to the Shari'ah based screening criteria that specifically forbids investment in shares of those companies that are excessively leveraged and/or engaged in lending activities. This study investigates the extent to which this claim is valid by comparing the performance of global and regional Islamic equity indices (IEIs) with conventional equity indices during the past decade. The equity indices for such analysis are preferred since it does not account for transaction costs or management skills. A logistic smooth transition autoregressive (LSTAR) model is used to investigate whether the ‘down market’ performance of IEIs differs from conventional indices. The LSTAR is superior to conventional ordinary least squares models since this allows for a smooth transition from the ‘down market’ to the ‘up market’ rather than an abrupt change. The empirical results indicate that IEIs, in general, perform better than conventional indices during the period 2000 to 2012. We do not find any abnormal returns associated with Islamic equity indices on a global basis, however, there is evidence of positive abnormal returns in the case of regional indices from Europe and Asia. Overall, IEIs exhibit lower systematic risk as compared with their benchmark suggesting that any excess performance from Islamic investments stems from the systematic risk that each investment assumes with respect to their benchmark during the declining phase of capital markets. The findings of this study are of interest to both academics and the general investing public since it provides evidence that IEIs are comparatively less risky than their conventional counterpart and thus provide hedging opportunities during the downfall of capital markets.

Asymmetric Effects of Monetary Policy in the U.S. and Brazil

We empirically test the effects of anticipated and unanticipated monetary policy shocks on the growth rate of real industrial production and explicitly test for different types of asymmetries in monetary policy implementation for two major international economies, the U.S. and Brazil. We depart from the conventional method of VAR analysis to estimate unanticipated monetary shocks and instead we use a combination of other methods. We first identify the Taylor rule that best describes the reaction of both central banks and then we test both forward looking linear and nonlinear models concluding that a Logistic Smooth Transition Autoregressive (LSTAR) forward looking model of the Taylor rule best describes the US FED Funds rate while a linear Taylor rule with the inclusion of a dummy variable best describes the reaction of the Central Bank of Brazil (BCB). We then use in-sample forecast errors in order to derive or identify the unexpected monetary shocks for both countries. In line with Cover (1992), we use these shocks to explore any asymmetries in the conduct of monetary policy on the growth rate of real industrial production. We also find asymmetries between anticipated and unanticipated monetary shocks as well as between effects of positive and negative shocks.

A SMOOTH TRANSITION AUTOREGRESSIVE APPROACH TO INVESTIGATING THE MARKET TIMING ABILITY OF FUND MANAGERS

ABSTRACT We derive a logistic smooth transition autoregressive (LSTAR) beta process to replace the dichotomous beta of Merton (1981). Fund betas follow an autoregressive process and most managers switch from one autoregressive regime to another in response to movements in the market condition indicator. We model beta directly using realized monthly betas and apply the LSTAR model and associated nonlinearity testing methodology to the realized betas. A serially correlated beta series seems to suggest passive variation in the betas with transition from one regime to another in response to the market indicator. We find that most funds are well represented by the LSTAR form and that sixteen percent of the funds exhibit the correct adjustment in their exposure to the market. Keywords Market Timing; Logistic Smooth Transition Regression

Parameter Identification in the Logistic STAR Model

We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that the threshold autoregression (TAR) is a limiting case of the LSTAR process. We demonstrate how this fact impedes numerical optimization of the original parametrization, whereas this is not the case for the new parametrization. Next, we show that information criteria provide a tool to choose between an LSTAR model and a TAR model; a choice previously basedsolely on economic theory. Reestimation of two published applications illustrate the usefulness of our findings.

Nonlinear mean-reversion in Southeast Asian real exchange rates

We find nonlinear mean reverting tendencies in Southeast Asian currencies by applying the newly developed nonlinear unit-root test by Park and Shintani (2005). First, with the US dollar as the numeraire currency, we find that 63% of the real exchange rates of Southeast Asian currencies turn out to be stationary. However, with the Japanese yen as the numeraire currency, we find no evidence in favour of Purchasing Power Parity (PPP) for most currencies in Southeast Asia, except for the Korean won and Taiwanese dollar. These findings imply that Southeast Asian currencies may not form a yen-dominated Asian exchange rate system. Second, when the dollar-based real exchange rates of Southeast Asian countries are nonlinear mean reverting, we find that the mean-reverting process could be well described by the Exponential Smooth Transition Autoregressive (ESTAR) model, rather than the Double Threshold Autoregressive (DTAR) or Double Logistic Smooth Transition Autoregressive (DLSTAR) model. Our results are reinforce...

Testing Parameter Constancy in Unit Root Autoregressive Models Against Multiple Continuous Structural Changes

This article considers tests for logistic smooth transition autoregressive (LSTAR) models accommodating multiple time dependent transitions between regimes when the data generating process is a random walk. The asymptotic null distributions of the tests, in contrast to the standard results in Lin and Teräsvirta (1994), are nonstandard. Monte Carlo experiments reveal that the tests have modest size distortions and satisfactory power against LSTAR models with multiple smooth breaks. The tests are applied to Swedish unemployment rates and the hysteresis hypothesis is over-turned in favour of an LSTAR model with two transitions between extreme regimes.

Previsão não-linear de retornos na BOVESPA: volume negociado em um modelo auto-regressivo de transição suave

Neste artigo, avalia-se a capacidade preditiva de um modelo auto-regressivo de transição logística suave (lstar) na geração de retornos estatisticamente significativos, quando se utiliza como variável de transição o volume negociado e o próprio retorno defasado, para o índice Ibovespa da Bolsa de Valores de São Paulo, analisado em termos diários entre os anos de 1996 e 2006. A justificativa para a inclusão do volume encontra-se em características do mercado e em algumas evidências de finanças comportamentais, que indicam que existe uma relação negativa entre volume e retornos futuros. O modelo possui, em geral, bom ajuste aos dados, embora não tenha a capacidade de gerar lucros reais adicionais, se os custos de transação são de 0.5% por operação. Para custos menores existe certa capacidade de previsão, embora inferior à de um modelo ar(1) e da estratégia de comprar e manter. considerando o risco, para custos de 0.035% por operação, o modelo auto-regressivo possibilita a obtenção de um índice de sharpe 20% maior do que com a estratégia de comprar e manter.

Detecting Mean Reversion in Real Exchange Rates from a Multiple Regime STAR Model

Recent studies on general equilibrium models with transaction costs show that the dynamics of the real exchange rate are necessarily nonlinear. Our contribution to the literature on nonlinear price adjustment mechanisms is threefold. First, we model the real exchange rate by a Multi-Regime Logistic Smooth Transition Auto Regression (MR-LSTAR), allowing for both ESTAR-type and SETAR-type dynamics. This choice is motivated by the fact that even the theoretical models, which predict a smooth behavior for the real exchange rate, do not rule out the possibility of a discontinuous adjustment as a limit case. Second, we propose two classes of unit-root tests against this MR-LSTAR alternative, based respectively on the likelihood and on an auxiliary model. Their asymptotic distributions are derived analytically. Third, when applied to 28 bilateral real exchange rates, our tests reject the null hypothesis of a unit root for eleven series bringing evidence in favor of the purchasing power parity.

Testing for Unit Root Against LSTAR Model: Wavelet Improvement Under GARCH Distortion

In this article, we propose a nonlinear Dickey–Fuller F test for unit root against first-order Logistic Smooth Transition Autoregressive (LSTAR) (1) model with time as the transition variable. The nonlinear Dickey–Fuller F test statistic is established under the null hypothesis of random walk without drift and the alternative model is a nonlinear LSTAR (1) model. The asymptotic distribution of the test is analytically derived while the small sample distributions are investigated by Monte Carlo experiment. The size and power properties of the test were investigated using Monte Carlo experiment. The results showed that there is a serious size distortion for the test when GARCH errors appear in the Data Generating Process (DGP), which led to an over-rejection of the unit root null hypothesis. To solve this problem, we use the Wavelet technique to count off the GARCH distortion and improve the size property of the test under GARCH error. We also discuss the asymptotic distributions of the test statistics in GARCH and wavelet environments.

Non-linear Co-integration between Nominal Interest Rates and Inflation: An Examination of the Fisher Hypothesis for Asian Countries

This paper examines the long-run relationship between nominal interest rates and inflation for a group of Asian countries over the period February 1973–April 2007. We argue that the empirical failure to find evidence supporting the Fisher effect in previous studies may be attributed to the presence of non-linearities in the long-run relationship between nominal interest rates and inflation. We present evidence that the Fisher relation contains significant logistic smooth transition autoregression (LSTAR)-type non-linearity. This type of non-linearity is consistent with inflation targeting and the opportunistic behavior of policy-makers. Applying a non-linear unit root test to the residuals obtained from the Fisher relation decisively rejects the null hypothesis of a unit root against the alternative of non-linear but globally stationary in all the cases.

Specification of Logistic Smooth Transition Auto-Regressive Model: An Alternative Representation of the Transition Function

Problem statement: In this study we proposed an alternative represent ation of Smooth Transition Auto-Regressive (STAR) model. Logistic Smooth Transition Auto-Regressive (LSTAR) model, Exponential Smooth Transition Auto-Regressive (ESTAR) models and Absolute Logistic Smooth Transition Auto-Regressive (ALSTAR) were being variously sighted and used in empirical research to model nonlinearity adjustments in the b ehavior of macro-economic variables. Approach: We showed that forecasts produced using this alternati ve model outperform most of these popular forecasting models. Results: We used LSTAR model as the benchmark. LSTAR model described asymmetrical non-linear adjustment process, while t he ESTAR model described symmetrical non- linear adjustment process. ALSTAR was introduced in the literature to modify the undesired property of asymmetry of LSTAR. The forecasting performances of these models were investigated. Conclusion: The alternative model we proposed in this study, t he Error Logistic Smooth Transition Regression (ELSTR) was shown to posses a better estimation of model parameters and performs better than those earlier ones. We demonstrated our claim with life data.

Nonlinear Phillips curve, NAIRU and monetary policy rules

The US Phillips curve is modeled with a logistic smooth transition autoregression specification that allows various nonlinear shapes. Using this, the paper derives model-consistent estimates of the NAIRU. The NAIRU is defined as the level of unemployment rate that would correspond to a forecast of no inflation changes over the policy horizon. This paper also investigates the implications of nonlinearities in the Phillips curve for deriving optimal monetary policy rules. The optimal policy rule for interest rates and implied sacrifice ratios are found to be nonlinear as well.

Testing for Granger (non‐)causality in a time‐varying coefficient VAR model

AbstractIn this paper we propose Granger (non‐)causality tests based on a VAR model allowing for time‐varying coefficients. The functional form of the time‐varying coefficients is a logistic smooth transition autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non‐causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money–output relationship using quarterly US data for the period 1952:2–2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out‐of‐sample forecasting performance for output relative to a linear VAR model. Copyright © 2008 John Wiley & Sons, Ltd.

Nonlinear Phillips curve, sacrifice ratio, and the natural rate of unemployment

This paper estimates the unemployment cost of lowering inflation and the natural rate of unemployment for the U.K. and the U.S. on a nonlinear Phillips curve. The nonlinearity is accommodated through a logistic smooth transition autoregression specification, which is flexible to allow various nonlinear shapes. Empirically, the models are shown to capture the nonlinear features present in the data well. The unemployment costs for lowering inflation vary, depending critically on the state of the economy, the size of intended inflation change, and whether policy makers seek to disinflate or prevent inflation from rising. As a further application, the natural rate of unemployment is produced using a nonlinear analog of the historical decomposition. The estimates are well consistent with the economic episodes.

Do Realised Betas Exhibit Up/Down Market Tendencies?

In this paper we employ the LSTAR (logistic smooth transition autoregressive) model to investigate potential nonlinearities, cyclical behavior, causality and duration dependence in the realized monthly betas of thirty-nine U.S. industry portfolios. Tests reject linearity for all but eight industries. The estimated nonlinear models suggest that industry betas are characterized by asymmetric cycles, with the speed of transition between the bull and bear market regimes being relatively slow for seven industries. We find duration dependence in industry betas since the probability of transition between regimes does depend on how long the market has been in an up or a down state.