Logistic Smooth Transition Regression Research Articles

The impact of value-added taxes and inflation threshold on economic growth in the Kingdom of Saudi Arabia

The paper contributes to the literature by examining the effects of inflation on economic growth in Saudi Arabia between 2010Q1 and 2022Q4 and exploring whether the VAT impacted the inflation-growth nexus. The literature on the determinants of economic growth has expanded during the last few decades. The impact of inflation on economic growth has received much attention, with contradictory findings. The study employs the Logistic Smooth Transition Regression (LSTR) model to determine the optimal inflation rate and estimate the repercussions of inflation in each regime. The findings show that the optimal inflation rate is 2.47%. Inflation is demonstrated to improve growth if it remains below 2.47%. Nevertheless, as the inflation rate is beyond this threshold, the effect becomes negative. Subsequently, the study accounts for the influence of Value- Added Tax on the link between inflation and growth. According to the empirical estimation, the threshold level of the inflation rate is determined to be 2.88%. In addition, the adverse implications of inflation on growth are higher when we consider the implementation of VAT. Therefore, the study concludes that there are nonlinear effects of the inflation rate on economic growth in Saudi Arabia. The empirical results of the study have important policy implications for Saudi Arabia.

Smooth transition regression model relating inflation to economic growth in Tunisia

Since its independence, Tunisia has embarked on monetary and financial sector reforms aimed at boosting economic growth. However, these reforms have not been effective due to inflation pressures in this country. Thus, this paper examines the nature of the relationship between financial development and economic growth between 1965 and 2019, using the non-linear logistic smooth transition regression model and considering inflation as a threshold financial development. The results show the existence of a non-linear abrupt relationship with an inflation threshold equal to 3.63%. Specifically, when inflation is below 3.63%, all variables, including inflation, have a significant and positive impact on economic growth. However, when inflation exceeds the estimated threshold, inflation has a significant and negative impact with an elasticity equal to − 0.365. To effectively manage inflation, the Tunisian authorities are encouraged to set and embrace specific inflation targets as a goal. This approach aims to mitigate inflationary pressures and foster a favourable environment for financial development in Tunisia, thereby promoting economic growth. Hence, it becomes imperative to implement such measures that alleviate inflationary pressures and drive economic growth through the facilitation of development finance by the banking sector.

Inflation Threshold in the Context of Structural Breaks: Evidence from Egypt Using the Logistic Smooth Transition Regression Approach

The disparities in structure and nature of developing economies, in terms of their level of development, yield mixed results on the linear/nonlinear relationship between inflation and real GDP (i.e., the inflation threshold), which should be revisited. No study examined the effects of inflation on real GDP in Egypt, particularly after the 2011 revolution and the November 2016 exchange rate floating. This study empirically investigates the inflation threshold in Egypt over the period 1976-2019. In other words, it seeks to determine the optimal level of an inflation rate such that beyond it, inflation becomes a destructive element to economic growth. To this end, the logistic smooth transition regression (LSTR) has been performed. Considering structural breaks, this study checked the stationarity of the variables with the Kapetanios unit root test. Results of the LSTR technique highlight a statistically significant positive relationship between inflation and GDP growth in the lower regime at 9.32%. Beyond this threshold, inflation harms GDP growth, indicating an asymmetric relationship between the two variables. Such a specified estimated inflation threshold could assist monetary authorities in setting their inflation target to be below that threshold. This study recommends that policymakers restrict inflation and keep it below the estimated threshold, considering that inflation in the Egyptian economy is derived from the aggregate supply, demand factors and the production apparatus's inelasticity.

The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume

This study investigated the asymmetric contemporaneous and lead-lag relationships between return and trading volume as well as return volatility and volume on the Bitcoin futures by analyzing the threshold and smooth transition effects based on the logistic smooth transition regression (LSTR) model with the logistic smooth transition GJR-GARCH framework. The main findings demonstrated that the asymmetric and smooth transition effects existed in both the contemporaneous and lead-lag return-volume and volatility-volume relationships. There were also a one-trading-day to three-trading-day delayed effects from volume to return and volatility. The implication of the findings suggests that there are arbitrage opportunities for investors in the Bitcoin futures market. Furthermore, we confirm that the Mixture of Distribution Hypothesis (MDH) was a better theory than the Sequential Information Arrival Hypothesis (SIAH) in explaining the volatility-volume relationships on the Bitcoin futures. Our findings also showed that the speed of information transmission increased as it got closer to the current trading day. This evidence supported the MDH in the Bitcoin futures market.

Exchange rate pass‐through to consumer prices in India – nonlinear evidence from a smooth transition model

AbstractUsing a backward‐looking Philips curve framework and a Logistic Smooth Transition Regression (STR) model, this article examined the exchange rate pass‐through (ERPT) to final consumer prices in the case of India. Specifically, the nonlinearity of ERPT has been investigated with respect to the level of economic activity and the rate of inflation by considering two alternative measures of exchange rate. For the first transition variable, results are markedly different between high and low‐activity regimes and signify the favourable impact of economic activity on the extent of pass‐through. In the short run, 1% exchange rate appreciation would decrease the price level by 0.244% when the economy is in a low‐activity regime; however, the same appreciation increases the price level by 0.485% when there is a boom in the economy. In the long run, ERPT in the low‐activity regime is −0.358, and in the high‐activity regime, it is 0.039. In both regimes, pass‐through coefficients differ from zero and one, implying that neither local currency pricing nor producer currency pricing characterizes the ERPT mechanism. The impact of other important determinants of inflation is also well according to theoretical expectations. Finally, for the rate of inflation, we did not report any evidence favouring the Taylor hypothesis in the case of India.

Nonlinear Impact of Inflation on Economic Growth in Nepal: A Smooth Transition Regression Approach

Macroeconomic stability is one of the most important macroeconomic goals for all countries. In recent years, the Nepalese economy has been experiencing inflation twice the economic growth rate, posing a threat to macroeconomic stability. Thus, careful investigation of the relationship between inflation and economic growth is required. This study assesses the existence of a nonlinear relationship between inflation and economic growth in the Nepalese context. The assessment is performed by considering the possibility of smoothly changing slope parameters to maximise economic growth or minimise growth losses and to determine the optimal inflation level along with the speed of inflation response to economic growth. The study applies a Logistic Smooth Transition Regression model that Terasvirta (1998, 2004) developed to estimate an optimal inflation rate using annual time-series data from 1976 to 2019. The study finds a nonlinear relationship between inflation and economic growth and estimates an optimal inflation threshold of 6.38±1.36 per cent for the Nepalese economy. It also shows that inflation positively correlates with economic growth up to the threshold level and that the association becomes negative beyond that level. This study suggests that inflation beyond the threshold level harms economic growth, while inflation below has a favourable effect. The findings assist policymakers in making an informed decision, providing leverage in inflation control and creating an atmosphere encouraging economic growth.

Exploring Okun's law asymmetry: An endogenous threshold logistic smooth transition regression approach*

AbstractUsing a novel methodology, we offer new evidence that a threshold relationship exists for Okun's law (the well‐known output–unemployment co‐movement). We use a logistic smooth transition regression (LSTR) model where threshold endogeneity is addressed using copula transformations of the threshold variable. We also suggest a test of the linearity hypothesis against the LSTR model. In line with Okun's insight (and that of the subsequent literature) that the trade‐off can be affected by different margins, we consider several potential threshold variables. We find mainly a combination of structural and policy‐related variables accounts for changes in the Okun's law trade‐off for the United States in recent decades. This conclusion is bolstered by combing these threshold candidates into a single factor. Accordingly, we find that the unemployment gap is increasingly associated with a smaller output gap. Notably, while the Great Recession accelerated that rise, the bulk of the change occurred beforehand.

Nonlinear analysis of technological innovation and electricity generation on carbon dioxide emissions in China

By using Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, this paper explores the influence of electricity generation and technological innovation on CO2 emissions, as well as that of population and economic growth, in China during the period of 1985–2019. Econometric methods of smooth transition regression (STR) are used to calculate the threshold effects of the linkage among the selected variables. The conversion function of LSTR(1) (logistic STR with one regime switch) is selected by F-statistics based on third-order Taylor approximation. The results of the diagnostic test indicate that when the growth rate of electricity generation exceeds 8.914%, the relationships become nonlinear. Moreover, 1% increases of electricity generation and technological innovation may lead to increases of 2.91664% and 0.31016% in China's CO2 emissions, respectively, while a 1% increase in economic growth can decrease China's CO2 emissions by 1.16441%. Finally, some implications are suggested, such as strengthening the publicity surrounding environmental protection to reduce the footprint of CO2 emissions, extending the green GDP even further, and building a more diversified power supply structure.

Does ICT change the relationship between total factor productivity and CO2 emissions? Evidence based on a nonlinear model

Tunisia is experiencing rapid social and economic expectations. The penetration of information and communication technology (ICT) might be useful for green economic development. This paper takes a novel use of the logistic smooth transition regression model in studying the pattern from the link between economic growth and CO2 emissions over the period 1970–2018. The results indicate that the pattern of CO2 emissions follows a nonlinear model with ICT as a transition variable affecting the relationship between total factor productivity (TFP) and CO2 emissions. More ICT use improves carbon efficiency. The results showed that ICT could boost economic growth and mitigate climate change.

Tracking safe haven properties of cryptocurrencies during the COVID-19 pandemic: A smooth transition approach

The study aims to examine the hedge and safe-haven properties of three heavyweight cryptocurrencies—Bitcoin, Ripple, and Ethereum—against the stock, commodity, and foreign exchange markets. The study sample covers the period of August 2011 to September 2020 and therefore includes the current coronavirus disease-2019 (COVID-19) crisis. Using a logistic smooth transition regression model (LSTR2), the study findings indicate the ability of monitored cryptocurrencies to act as safe-haven assets, but such behavior differs across markets. Interestingly, during the pandemic period, Ethereum provides the strongest safe haven function for the commodity market. According to our findings, we are mindful of that the COVID-19 outbreak provides an exciting opportunity to advance our knowledge of the prominence of new coins such as Ethereum that are gradually gaining supremacy in the cryptocurrency market to the detriment of traditional cryptocurrencies like Bitcoin.

IN- AND OUT-OF-SAMPLE PERFORMANCE OF NONLINEAR MODELS IN INTERNATIONAL PRICE DIFFERENTIAL FORECASTING IN A COMMODITY COUNTRY FRAMEWORK

This paper presents an analysis of a group of small commodity-exporting countries' price differentials relative to the US dollar. Using unrestricted self-exciting threshold autoregressive models (SETAR), we evaluate the sixteen national Consumer Price Indexes (CPI) differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is estimated through calculation of mean absolute errors measures based on the monthly rolling window and recursive forecasts, and this estimation is extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive nonlinear autoregressive model (AAR), and a simple neural network model (NNET). Our preliminary results confirm the presence of some form of nonlinearity in most of the analyzed countries. The parsimonious AR(1) model does not appear to perform any worse than any nonlinear model in the rolling sample exercise. However, in terms of a longrun equilibrium driven by purchasing power parity, its validity is undermined by the results of the recursive estimates and the outcome of the Diebold-Mariano type tests, which favor generally the Heckscher commodity points theory. As a policy advice to commodity-exporting countries, we find no apparent reason to suggest commodity export price pegging as a generalized foreign exchange policy.

Threshold Effect in Residential Water Demand: Evidence from Smooth Transition Models

This paper aims to empirically examine the presence of nonlinear behavior in residential water demand for the case of Tunisia. We specifically explore the existence of nonlinearity with respect to the magnitude of water price changes through a logistic smooth transition regression (LSTR) framework and an increasing multi-step water pricing scheme. Using quarterly time series for the period 1980–2007 which describes residential water consumption and its main determinants, our results provide strong evidence that water consumption responds nonlinearly to the extent of price changes for the two consumption blocks considered. Water price elasticities are found to be higher when variation in tariffs surpasses a given threshold. More precisely, we find a unit elastic water demand for lower block consumers (low-income households) when price changes exceed a threshold of roughly 5%. For the upper block consumers (high-income households), water consumption is less elastic in comparison to low-income households, but still significant when the price variation exceeds a threshold of 2.6%. Our findings imply that increasing the length of the lower block of consumption may help achieve goals of social equity, while increasing tariff progressivity, at least for upper block consumers, helps promote water saving.

Estimating the Price Elasticity of Peak Residential Demand using High Frequency Data

This paper studies the price elasticity of the peak electricity demand of the residential sector in Korea using high frequency data collected by AMR (Automatic Meter Reading) system. The main purpose of this paper is to estimate the price elasticity by allowing the nonlinear relationship between price and temperature in the short-run residential electricity demand curve. Specifically, we consider a Logistic Smooth Transition Regression model with functional coefficients to capture the temperature-dependent price elasticity of residential peak demand in Korea. We show conclusive evidence that the non-economic variables influence the price elasticity of peak residential demand in Korea. Our estimation results show that the price elasticity is dependent upon temperature, and peak demand becomes more sensitive when the weather is very hot or cold.

Export Hysteresis, Capacity Constraints, and Uncertainty: A Smooth Transition Analysis for Euro Area Member Countries

We argue that, under certain conditions described by a sunk cost hysteresis model, firms consider exports as a substitute for domestic demand. This is valid also on the macroeconomic level where the switch from the domestic market to the export market and vice versa takes place in a smooth manner. Areas of weak reaction of exports to changes in domestic demand are widened by uncertainty. Our econometric model for six euro area countries suggests domestic demand and capacity constraints as additional variables for export equations. We apply the exponential and logistic variant of a smooth transition regression model and find that domestic demand developments and uncertainty are relevant for short-run export dynamics particularly during more extreme stages of the business cycle. A substitutive relationship between domestic and foreign sales can most clearly be found for France, Greece, and Ireland (exponential smooth transition regression (ESTR) model) and France, Portugal, and Italy (logistic smooth transition regression (LSTR) model), providing evidence of the importance of sunk costs and hysteresis in international trade between the member states of the Economic and Monetary Union (EMU). What is more, our empirical results are robust to the inclusion of a variable measuring European policy uncertainty. In some cases (Italy, Greece, and Portugal) the results underscore the empirical validity of the export hysteresis under uncertainty model.

Does Military Spending Nonlinearly Affect Economic Growth in South Africa?

Using annual data collected from 1988 to 2015, this study provides evidence of a non-linear relationship between military spending, economic growth and other growth determinants for the South African economy. The empirical study is based on estimates of a logistic smooth transition regression (LSTR) model and our empirical results point to an inverted U-shaped relationship between military spending and economic growth for the data. Furthermore, our empirical results suggest that the current levels of military spending, as a component of total government expenditure, are too high in the South African economy and need to be transferred towards more productive non-military expenditure in order to improve the performance of economic growth and other growth determinants.

The Nonlinearity of the New Keynesian Phillips Curve: The Case of Tunisia

This article seeks to check the nonlinearity of the Phillips curve in Tunisia for the 1993–2012 period, relying on a hybrid new Keynesian Phillips curve modeled via a Logistic Smooth Transition Regression (LSTR) model with endogenous variables. We estimate this model using the nonlinear instrumental variables. The empirical results corroborate the new Keynesian assumption ofprice rigidity and show that the response of inflation to the output gap tends to be significant only if the inflation rate tends to be relatively high and exceeds a certain threshold. For a low inflation rate, the price rigidity dominates. This result is particularly evident in Tunisia, especially for the years following the 2011 revolution during which the elasticity of inflation rate to an excess demand has become highly important and the inflation rate experienced record levels.

Examining asymmetric effects in the South African Phillips curve: evidence from logistic smooth transition regression models

This study contributes to the foregoing literature by investigating asymmetric behaviour within the South African short-run Phillips curve for three versions of the Phillips curve specification namely; the New Classical Phillips curve, the new Keynesian Phillips curve and the hybrid new Keynesian Phillips curve. To this end, we employ a logistic smooth transition regression (LSTR) econometric model to each of the aforementioned versions of the Phillips curve specifications for quarterly data spanning from 1970:01 to 2014:01 and thus endeavour into determining which version of the Phillips curve best suites the employed data. Our empirical results indicate that both the marginal-cost-based as well as the output gap-based versions of the hybrid new Keynesian Philips curve provide a good fit for South African data. Therefore, our empirical results indicate that monetary policy in South Africa has an influence on the demand side of the economy through inflation inertia and inflation expectations whilst appearing to exhibit no significant effects on the supply side of the economy.

Estimating Interest Rate Setting Behavior in Brazil: A LSTR Model Approach

Given limited research on monetary policy rules in emerging markets, this paper challenges the applicability of a nonlinear Taylor rule in characterizing the monetary policy behavior of the Brazilian Central Bank. It also investigates whether and how the process of setting interest rates has been developed in response to contingencies and special events. We extend the linear Taylor rule to a regime-switching framework, where the transition from one regime to another occurs in a smooth way, using a Logistic Smooth Transition Regression (LSTR) approach. In this sense, we empirically analyze the movement of the nominal short term interest rate of the Brazilian Central Bank using quarterly data, covering the period 1994.Q4–2012.Q2. We find that the nonlinear Taylor rule provides a better description of the Brazilian interest rate setting and is consistent with historical macroeconomic events. In particular, our results show that adopting a nonlinear specification, instead of the linear, leads to a costs reduction in terms of fit: 190 basis points in 1995 and 140 basis points in the mid-2002 presidential election campaign in Brazil. Moreover, the Brazilian monetary policy exhibits nonlinear patterns that better captures special events and may contain relevant information rendering it applicable to unusual conditions, i.e., a financial crisis, which require disconnection from the automatic pilot rule and use of judgement to make decision.

The state-dependent time variation in the value premium

It is well documented that asset and strategy returns are generally exposed to identifiable risk factors. Moreover, the exposure to these systematic risk factors tends to be time varying. Examples of strategies that exhibit such regime-dependent variability include value investing in equity markets and carry in foreign exchange markets. The literature proposes several macro-based explanations for this time variation such as liquidity or cyclicality risk depending on the strategy and horizon under consideration. This project relies on developments in the academic literature in the area of state-dependent asset pricing models (see, for example, survey by van Dijk et al, 2002). Under such a framework, a strategy return is modelled using a multi-factor asset pricing equation where the risk sensitivities (or betas) to the pre-defined factors are allowed to vary according to one or multiple state variables. The approach considered here uses a logistic smooth transition regression methodology applied to the value strategy, where the state variables have an economic meaning (see Christiansen et al, 2011 application to foreign exchange carry strategy). By distinguishing between low and high state regimes (in a continuous space), the aim is to first investigate improvements in the explanatory power of the model relative to the linear (no-state dependency) approach. In particular, we study the performance of the value factor during turbulent periods, and identify economic variables driving its time-varying behaviour. We also present an algorithm that allocates dynamically to a long–short value overlay portfolio conditioned on regime transitions. We show that the performance of this dynamic portfolio is superior to a portfolio with a static value overlay.

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