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The purpose of this paper is to examine the relationship among employment, real wage, and output growth in Canada.

Using quarterly data from 1994q2 to 2012q3, this paper employs a vector autoregressive framework while allowing for the derivation of output from its historical maximum over the sample period to affect future output, employment, and real wage growth dynamics.

There are three main findings: output growth is significant in predicting employment growth and vice versa; real wage growth neither Granger causes employment growth nor output growth, but employment growth Granger causes real wage growth; and non-linear dynamics, captured by the current depth regression (CDR) effect term, through the sign as well as the magnitude of output changes, are important in characterizing the evolution of the relationship among output, employment, and real wage growth.

The findings of this research have significant implications for policy makers. Output and employment growth are important in forecasting each other in Canada. In contrast to the mainstream theory, real growth is insignificant in explaining the future dynamics of employment in Canada. Policies need to be formulated to encourage the growth of employment to ensure sustain output growth.

This study examines empirically the real output, real wage, and employment link in Canada. This study uses the most recently revised GDP data arising from the 2012 Historical Revision of the Canadian System of National Accounts. The econometric methodology involves the standard vector autoregression (VAR) model to which the authors introduce non-linear dynamics through a term that controls for the deviation of output from its preceding historical maximum: the CDR effect.

Capital markets thrive on information, and the information revolution has transformed these markets all over the world. Investors can now keep track of the movements of capital markets in real-time and they react to the flow of information from around the world. One of the concerns of stock market investors is whether the markets operate efficiently, independently, and with sound fundamentals. However, real market movements tend to exhibit a link as is evident from recent market movements across the world.

The assessment of interdependence between stock markets is an important aspect of international portfolio management. The aim of this chapter is to examine the shock and volatility spillover between the Standard and Poor’s 500 (S&P500) index from the United States (US) Stock Exchange and the Istanbul Stock Exchange 100 (BIST100) index from the Stock Exchange Istanbul.

S&P500 index, which is the most important index representing US markets, and BIST100 index, which is the index representing the Turkish market, were used as variables in this study. In the analysis, the causality in variance test was applied to determine the volatility spillover between these two markets. Later, multivariate GARCH (MGARCH) models were used to measure the volatility spillover in the markets. VAR(1)-GARCH (1,1)-Diagonal BEKK model was applied to the daily data to determine the shock and volatility spillover in the markets.

As a result of the variance causality test, it was found that there is a bi-directional volatility spillover between S&P500 index and BIST100 index. When the return spillover between the markets is examined, a one-way spillover from the S&P500 index to the BIST100 index emerged. Diagonal BEKK model results show that each market is affected by its own news (unexpected shocks) and volatility. Furthermore, the volatility is persistent for both markets. These findings demonstrate that the US market and the Turkish market interact with each other.

There is a lack of studies on tourism demand forecasting that use non‐linear models. The aim of this paper is to introduce consumer expectations in time‐series models in order to analyse their usefulness to forecast tourism demand.

The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self‐exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months).

Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed.

This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting.

This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non‐linear models such as self‐exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level.

The purpose of this paper is to examine whether futures markets play a dominant role in the price discovery process. The rate of convergence of information from one market to another is analyzed to infer the efficiency of futures as an effective hedging tool.

The paper uses a two‐regime threshold vector autoregression (TVAR) and a two‐regime threshold autoregression for six commodities. The regimes (or states) are defined around the expiration week of the futures contract.

This paper finds evidence for price discovery process happening in the futures market in five out of six commodities. However, the rate of convergence of information is slow, particularly in the non‐expiration weeks. For copper, gold and silver, the rate of convergence is almost instantaneous during the expiration week of the futures contract affirming the utility of futures contracts as an effective hedging tool. In case of chickpeas, nickel and rubber the convergence worsens during the expiration week indicating the non‐usability of futures contract for hedging.

This paper extends the framework developed by Garbade et al. by superimposing a two‐regime TVAR model to quantify the price discovery process. It is the first paper to analyze the differential impact of price discovery and convergence during expiration week (compared to non‐expiration weeks) for the Indian commodities market.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold autoregression with GARCH errors, TGARCH, and double threshold heteroskedastic model with auxiliary threshold variables. Further, we briefly review recent methods for Bayesian model selection, such as, reversible-jump Markov chain Monte Carlo, Monte Carlo estimation via independent sampling from each model, and importance sampling methods. Seven heteroskedastic models are then compared, for three long series of daily Asian market returns, in a model selection study illustrating the preferred model selection method. Major evidence of nonlinearity in mean and volatility is found, with the preferred model having a weighted threshold variable of local and international market news.

This study analyses agricultural land price dynamics in order to better understand price development and to improve forecast accuracy. Understanding the evolution of agricultural land prices is important when considering sound investment decisions.

This study applies threshold autoregression to model agricultural land prices. The data includes quarterly observations on Finnish agricultural land prices.

The study shows that Finnish agricultural land prices exhibit regime-switching behaviour when using past changes in prices as a threshold variable. The threshold autoregressive model not only fits the data better but also improves the accuracy of price forecasts compared to the linear autoregressive model.

The results show that a sharp fall in agricultural land prices temporarily changes the regular development of prices. This information significantly improves the accuracy of price predictions.

The purpose of this paper is to investigate the impact of changes in credit stock on real and financial indicators in Turkey with a focus on conditional and time-varying dynamics.

In addition to lag-augmented vector autoregression (LA-VAR) based time-varying Granger causality tests, threshold models and a research setting that identifies high/low states of credit growth based on 24-month moving averages are used to explore regime-dependent behavior. For investigating the asymmetric dynamics, the authors use a methodology that identifies good/bad news in credit growth based on 24-month moving averages and standard deviations.

Results strongly suggest that the impact of changes in credit stock induces conditional responses. Moreover, we find evidence for asymmetric responses. In the case of Turkey, efforts to spur growth through credit produce a strong negative byproduct, a depreciation in the exchange rate. The authors also find that changes in credit stock have become more relevant for uncertainties in inflation and exchange rate expectations, particularly in the era after mid-2018 in which credit growth volatility has increased noticeably.

This study provides a comprehensive analysis of time-varying and conditional responses to a change in credit stock in a major emerging economy. Using a moving threshold based only on the available information in the analysis of state-dependency represents a new approach.

This study aims to determine whether the transmission of monetary policy to the real economy depends on the structural conditions of financial stability. In particular, the paper shows that the effects of shocks to financial stability on output and inflation is conditional on the state of credit in the economy, measured broadly as a credit-to-GDP.

The authors use a threshold vector autoregression model with Bayesian techniques to investigate the impact of private nonfinancial sector credit on the dynamic relationship between financial conditions, monetary policy transmission mechanism and macroeconomic performance in Kazakhstan from 2005:Q1 to 2020:Q1.

In the modeled threshold vector autoregression (VAR) specification, the authors document that when the credit-to-GDP gap is low or the credit is below its trend, an increase to the interest rate leads to a short-term economic expansion. However, when the credit-to-GDP gap is high or the nonfinancial credit is above its trend, a tightening in monetary policy leads to an economic contraction with domestic financial conditions being weaker compared to a low credit environment.

The outcome is consistent with the related literature, which argues that a more sustained increase in credit is followed by a sharper economic contraction, but only when the economy is in the high credit state. These results highlight that financial stability measures (e.g. credit state) is important to take into account when conducting monetary policy in emerging economies.

This paper aims to examine the asymmetric effects of exchange rate shocks on inflation for a small open economy, namely South Africa, over the period 1970Q1–2020Q1.

A threshold vector autoregressive model that allows parameters to switch according to whether a threshold variable crosses an estimated threshold is employed to address the objective of this paper. The threshold value is determined endogenously using the Hansen (1996) test. Generalized impulse responses introduced by Koop et al. (1996) are used to study the effects of exchange rate shocks on inflation depending on their size, sign and timing to the inflation cycle. The authors also employed a Cholesky decomposition identification scheme to identify exchange rate shocks in the non-linear model.

The results show that there is a non-linearity effect of the exchange rate shock on inflation. In particular, the effects of 1 or 2 standard deviations of positive (appreciation) or negative (depreciation) exchange rate shock on inflation are small in the long run but a bit larger in the high inflation regime than the low inflation regime.

This paper contributes to the literature on the non-linear effects of exchange rate pass-through (ERPT) to inflation for Sub-Saharan African economies in general and the South African economy in particular by incorporating the size and timing of the exchange rate shocks to the inflation cycle.