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The Green Revolution transformed agriculture in the Indian State of Punjab, with positive spillovers to the rest of India, but recently the state’s economy has fallen dramatically in rankings of per capita state output. Understanding the trajectory of Punjab’s economy has important lessons for all of India. Economic development is typically associated with changes in economic structure, but Punjab has remained relatively reliant on agriculture rather than shifting economic activity to manufacturing and services, where productivity growth might be greater.

The authors empirically examine structural change in the Punjab economy in the context of structural change and economic growth across the States of India. The authors calculate structural change indices and map their pattern over time. The authors estimate panel regressions and time-varying parameter regressions, as well as performing productivity change decompositions into within-sector and structural changes.

Panel regressions and time-varying-coefficient regressions suggest a significant positive influence of structural change on state-level growth. In addition, growth positively affected structural change across India’s states. The relative lack of structural change in Punjab’s economy is implicated in its relatively poor recent growth performance. Comparisons with a handful of other states reinforce this conclusion: Punjab’s lack of economic diversification is a plausible explanation for its lagging economic performance.

This paper performs a novel empirical analysis of structural change and growth, simultaneously using three different approaches: panel regressions, time-varying parameter regressions and productivity decompositions. To the best of the authors’ knowledge, it is the only paper we are aware of that combines these three approaches.

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact method for implementing DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an inflation forecasting application. We find strong evidence of model switching. We also compare different ways of implementing DMA/DMS and find forgetting factor approaches and approaches based on the switching Gaussian state space model to lead to similar results.

This paper investigates the relationship between (1) business cycle and use of personal contacts to obtain job and (2) use of personal contacts to obtain job and wages.

For this, we use data from the Monthly Employment Survey (2002–2015) from Brazil which has detailed information on individual and job characteristics. In addition, we investigate the impact of referrals on wage using quantile regressions.

Time-varying parameter estimates indicate that the relationship between business cycle and use of personal contacts became less countercyclical over time. In general, they show that there is more evidence of a slow changing relationship between personal contacts and the business cycle over time rather than a sudden and discrete one. Using quantile regressions, we observed that, controlling for similar observable characteristics, and including unobserved heterogeneity, wage differences between workers using personal contacts versus workers using others channels disappear. The evidences indicate that workers resort to personal contacts because of valuation of non-pecuniary job characteristics.

The results suggest that, in designing subsidy or affirmative action programs, attention to network effects is important. Social networks can help labor markets run more smoothly by alleviating information frictions.

This study extends the existing literature by providing empirical evidence of the use of personal contacts for the Brazil. Although there are many studies and methods for measuring use of personal contacts, to our knowledge, there are no studies using a time-varying parameters model.

Motivated by the ongoing debate on the existence and magnitude of herding in financial markets, the purpose of this paper is to examine Eurozone stock markets for herding behavior. In the context of the present study, the authors seek for herding behavior of stock markets as a whole as opposed to previous studies that examine herding on stock level.

To this end, the authors employ data on benchmark stock market indices for a long sample starting from 2000 through 2016. The testing procedure entails the standard Capital Asset Pricing Model-based procedure along with an advanced econometric method allowing the coefficients of the model to vary over time.

Results provide evidence in favor of negative herding behavior (anti-herding) for the Eurozone as a whole with noteworthy transitions. Further analysis reveals that stock markets of the periphery exhibit scarce evidence of herding, whereas continental countries are mainly characterized by negative herding behavior.

The present study’s main contribution is twofold. First, herding is examined not in sector or stock level as previous studies but at market level. Second, the testing methodology entails a pure time-varying regression model with stochastic volatility proposed by Nakajima (2011) that has not been previously employed in stock market herding. The results entail significant implications for investors seeking for diversification across Eurozone stock markets.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

The paper aims to shed light on the effects of inflation on gold price and exchange rate in Vietnam by using time-varying cointegration.

Using cointegration techniques with fixed coefficient and time-varying coefficient, the study exams the impacts of inflation in models and compares the results through coefficient estimates.

A significant inflation impacts are found with the time-varying cointegration but not with the fixed coefficient cointegration models. Moreover, monetary policy affects exchange rate not only directly via its instruments as money supply and interest rate but indirectly via inflation. Also, interest rate is one of the determinants of gold price.

To the best of our knowledge, this paper is the first to use time-varying cointegration to analyze the impact of inflation on the gold price and exchange rate in Vietnam. Gold price and exchange rate fluctuations are always the essential and striking issues, which have been emphasized by economists and policymakers. In macroeconometric researches, cointegration models are often used to analyze the long-term relations between variables. Attentionally, applied models show a limitation when estimating coefficients are fixed. This characteristic might not really match with the data properties and the variation of the economy. Currently, time-varying cointegration models are emerging method to solve the above issue.

This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).

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.