Does Distance Matter in Convergence Among Indian States?

Introduction Chapter 2 used the standard AR model for the inference on unit roots. However, one may contemplate testing for a unit root using different model specifications, and doing so might result in different inferential results. This chapter introduces inferential procedures for a unit root that use model specifications that depart from the standard AR model. The first section of this chapter addresses unit root tests under structural changes in the non stochastic regressors added to the AR model. This research area, initiated by Perron (1989), received much attention because the presence of structural changes in the parameters of non stochastic regressors sometimes yields different inferential results. It also provided further challenges for econometric theorists and resulted in various new inferential procedures. The second section examines unit root tests with conditional heteroskedasticity. Because conditional heteroskedasticity is known to be prevalent in high-frequency financial time series, it is natural to consider unit root testing in its presence. As is discussed, the presence of conditional heteroskedasticity brings some complications in devising unit root tests. This chapter then addresses unit root tests in the presence of additive and innovational outliers. It is well-known that outliers can affect inferential results in a significant manner. This section discusses the effects of outliers on unit root testing and the methods of unit root testing in the presence of outliers. The fourth topic of this chapter encompasses unit root distributions and tests under fat-tailed distributions. This discussion show that fat-tailed distributions introduce nuisance parameters in the limiting distributions of Dickey and Fuller's (1979) and Phillips and Perron's (1988) test statistics. The last topic of this chapter is unit root tests against nonlinear alternatives. This discussion considers threshold AR and smooth-transition AR models, which are representative nonlinear models in time series analysis. In addition, it introduces inference methods using random-coefficient AR models. A summary and further remarks conclude the chapter.

1. Introduction 2. Key Challenges 2.1. Macroeconomic Challenges 2.1. Center-State and inter-state relations 2.3. Growth Drivers 2.4. Conclusion 3. Growth Performance 3.1. Growth Performance 3.2. Sectoral Growth Performance 3.3. Sectoral Contribution to Growth 3.4. variability of Output 3.5. Contribution of States to Growth in GDP and Population 3.6. Conclusion Annex 3.1. Data Issues Annex 3.2. Setcoral Growth 2000-03 Annex 3.3. Setcoral Growth 2004-08 Annex 3.4. Setcoral Growth 2009-12 Annex 3.5. Setcoral Growth 2000-12 Annex 3.6. Setcoral Shares 2000-03 Annex 3.7. Setcoral Shares 2004-08 Annex 3.8. Setcoral Shares 2009-12 Annex 3.9. Setcoral Shares 2000-12 Annex 3.10. Share of States in Combined SDP and Population Annex 3.11. Contribution to Growth in Combined Output and Population Annex 3.12. Variability in Sectoral Output Annex 3.13. Sectoral Contribution to Growth 4. Income Inequality 4.1. Consumption Based Inequality 4.2. Behaviour of Per Capita Income 4.3. Inequality Measures 4.4. Convergence Amongst Indian States 4.5. Conclusion Annex 4.1 Estimates of Absolute Convergence Behaviour in Cross section and Panel Dimension 5. Infrastructure and Growth 5.1. Approach to Provision of Infrastructure 5.2. Recent initiatives for infrastructure push 5.3. Infrastructure Index 5.4. Infrastructure Index-Relative Position of States 5.5. Infrastructure and Growth 5.6. Conclusion Annex 5.1. Causality between Social and Economic Infrastructure for General Category States Annex 5.2. Causality between Social and Economic Infrastructure for Special Category States Annex 5.3. Causality between growth in SDP and growth in Economic Infrastructure for GCS Annex 5.4. Causality between growth in SDP and growth in Social Infrastructure for GCS Annex 5.5. Causality between growth in SDP and growth in Overall Infrastructure for GCS Annex 5.6. Causality between growth in SDP and growth in Economic Infrastructure for SCS Annex 5.7. Causality between growth in SDP and growth in Social Infrastructure for SCS Annex 5.8. Causality between growth in SDP and growth in Overall Infrastructure for SCS Annex 5.9. Overall Infrastructure Index - General Category States (2001-2010) Annex 5.10. Overall Infrastructure Index-Special Category States (2001-2010) Annex 5.11. Infrastructure Index with Qualitative Dimensions for General Category States (2004-2010) Annex 5.12. Infrastructure Index with Qualitative Dimensions for Special Category States (2004-2010) 6. Health and Growth 6.1. Review of Literature 6.2. Stylised Facts 6.3. Empirical Methodology 6.4. Results 6.5. Concluding Observations Annex 6.1 Panel Unit Root test Annex 6.2 Panel-Cointegration tests 7. Credit and Growth 7.1. Growth of Credit and Credit Allocation Across Sectors 7.2. Credit-Output growth at the State level 7.3. Shares of different sectors in credit and output 7.4. Methodology and Empirical Results 7.5. Conclusion Annex 7.1. Growth of Sectoral Credit and Output 2001-2004 Annex 7.2. Growth of Sectoral Credit and Output 2001-2004 Annex 7.3. Growth of Sectoral Credit and Output 2001-2004 Annex 7.4. Growth of Sectoral Credit and Output 2001-2011 Annex 7.5. Sectoral Shares in Credit and Output 2001-2004 Annex 7.6. Sectoral Shares in Credit and Output 2005-2008 Annex 7.7. Sectoral Shares in Credit and Output 2009-2011 Annex 7.8. Unit Root Tests-General Category States Annex 7.9. Unit Root Tests - Special Category States Annex 7.10. Panel-cointegration Tests Annex 7.11. FMOLS Estimates of Responsiveness Between Credit and Output

Stationarity is an important property of time series. To determine the stationarity of time series is the premise and basis for further study. Unit root test or Stationarity test is the important method in testing series' stationarity in time series analysis, and the commonly used are DF, ADF and PP unit root test, KPSS stationarity test and Breitung nonparametric unit root test. In this paper, the principles and ideas of these commonly used unit root test (or stationarity test) are summarized, which provides theoretical reference and basis for the correct application of the unit root test or stationarity test method. It has been found that all these unit root (or stationary) tests have nonstandard asymptotic distributions and the critical values need to be obtained by simulation.

PurposeThe purpose of this paper is to examine the time series properties of 26 macroeconomic variables in Papua New Guinea (PNG) over the period 1970‐2006.Design/methodology/approachBoth unit root and stationarity tests without a structural break and the Lagrange Multiplier (LM) unit root test with one and two structural breaks developed by Lee and Strazicich are applied to each of the 26 macroeconomic variables in PNG. Compared to popular ADF‐type endogenous unit root tests such as those proposed by Zivot and Andrews and Lumsdaine and Papell, the LM unit root test with one and two structural breaks has the advantage that it is unaffected by breaks under the null.FindingsThe unit root and stationarity tests without structural breaks find at best mixed evidence of mean reversion and/or trend reversion for most variables. This result is likely to reflect the failure of these tests to allow for structural breaks, given the power to find stationarity declines if the data contain a structural break that is ignored. When the LM unit root test with one and two structural breaks is applied, it is found that at least 23 of the 26 macroeconomic variables are trend stationary.Originality/valueThe time series properties of macroeconomic variables have important implications for several macroeconomic theories. There are, however, few studies of the time series properties of macroeconomic variables in developing countries and no comprehensive studies for any of the Pacific Island countries. This paper begins to fill this gap as the first to provide a systematic examination of the time series properties of macroeconomic variables in Paua New Guinea.

Background Tobacco use contributes significantly to deaths and diseases globally. In India, it accounts for nearly two million adult deaths and costs about USD 27.5 billion annually. India has made notable strides in tobacco control ratifying the World Health Organization Framework Convention on Tobacco Control and enacting the Cigarettes and Other Tobacco Products Act, 2003. While there has been some decline in tobacco use prevalence over time, the enforcement of tobacco control laws remains suboptimal and inconsistent across Indian states. India, being a democracy with union of states, states remain crucial in implementing tobacco control laws. This study aims to inform a framework for assessing tobacco control law enforcement systems at the state level in India. Methods We used document analysis to identify tobacco control laws and guidance for their enforcement in India. We used a rapid review of literature to identify key elements of effective law enforcement by reviewing major relevant frameworks, indices, and tools. Building on the findings from these exercises and using a system thinking lens, we identified key themes that can inform a framework for assessing tobacco control law enforcement systems in Indian states. Results We identified four major themes (leadership and governance; human resources; finances; enforcement tools and materials) and several specific elements relevant to Indian contexts that could inform development of a meaningful and pragmatic framework for assessing tobacco control law enforcement systems in Indian states in order to strengthen tobacco control law enforcement. Conclusion It is important and timely to focus on tobacco control law enforcement at sub-national (state) level in India using a systems thinking approach that is informed by lessons about effective enforcement systems from across sectors.

Introduction This chapter introduces inferential procedures for seasonal unit roots. Seasonality is a unique feature of time series, but it was ignored in the previous chapters, which implicitly assumed that there is no seasonality in the data. However, when it is present in the data, it is important to know how we can find an appropriate model for the data and how we perform inference on the chosen model. A popular model for seasonal time series is the seasonal ARIMA model due to Box and Jenkins (1976). One of the key elements of this model is seasonal differencing, which is required when seasonal unit roots are present. In this sense, testing for seasonal unit roots is an essential step in Box and Jenkin's modeling of seasonal time series. Albeit less popular than the seasonal ARIMA model, periodic autoregressive (PAR) models are also useful for modeling seasonal time series. Properties of the PAR model depend on the presence or absence of a unit root, so that testing for a unit root and for the null hypothesis of stationarity is important for the PAR model. This chapter starts from Dickey, Hasza, and Fuller (1984; DHF hereafter), who use the AR(S) model (S denotes the number of seasons) to test for seasonal unit roots, and it discusses its extensions. Then, the testing procedures of HEGY and their extensions are introduced. HEGY's advantage over DHF is that it can test the presence of positive, negative, and complex unit roots separately. Seasonal stationarity tests that complement the seasonal unit root tests are introduced next. Seasonal unit root and stationarity tests under structural changes are also discussed. In addition, this chapter introduces several methods that can be used to test for a unit root in the PAR model. Last, empirical studies examining seasonal unit roots are discussed. Testing for Seasonal Unit Roots A time series { y t }, observed at S equally spaced time intervals per year, is said to have seasonal unit roots if {(1 − B S )y t } is a stationary process.

SummaryMotivationEmpirical studies on income convergence for India indicate that Indian states exhibit divergence in income. The literature, however, has neglected the convergence dynamics of the main sources of output. A pertinent question is whether divergence in income is due to divergence in factor inputs (capital and labour) and/or in total factor productivity (TFP) in India, a low middle‐income country with high dispersion of per capita income across its states.PurposeThis article examines the sources of income divergence in Indian states by analysing convergence in TFP, labour, and capital.Methods and approachWe test for stochastic convergence hypothesis by employing state‐level data for 19 Indian states during the period 2001–2015. Given the small sample dimension of the Indian state‐level income data, we benefit from the recent developments in non‐stationary panel data literature; and conduct novel panel stationarity and unit root tests with a fixed time dimension. We further carry out a robustness analysis to account for cross‐section dependency across the states.FindingsTFP as well as the factor inputs (labour and capital) exhibit divergence, implying persistence in income inequality across Indian states. Divergence in labour across the states reflects the fact that migration is primarily an intra‐district phenomenon. Poor investment climate in low‐income states acts as a barrier for convergence in capital. TFP and capital stock are found to be correlated, and thereby lower investment in poorer states may be responsible for divergence in TFP across the states.Policy implicationsMigration in India is primarily an intra‐district phenomenon. There is a need to study the reasons which are discouraging inter‐district and inter‐state migration for better utilization of the labour resource. Government intervention has not been adequate to improve the infrastructure position and encourage capital inflows to low‐income states. Low‐income states should improve their business climate and create support infrastructure to earn the confidence of investors. Increasing investment in low‐income states would help to increase their TFP and catch up with the high‐income states.

The consolidated measures of budget deficits and public debt levels for India's central and state governments are well above the average of other emerging economies. In contrast to previous studies of India's budgetary position, which focus on the central government's budget, this paper examines fiscal sustainability at the level of India's states, which differ widely in terms of their level of economic development. After comparing fiscal performance in the states, key formulae for examining public debt sustainability at the sub-national level in India are derived. These formulae are then applied, firstly to identify states where public debt has stabilized as a percent of Gross State Domestic Product, and secondly to gauge the size of the primary budget balances needed to achieve a 25% public debt to GSDP ratio within three, five and ten year horizons.

Mean reversion properties of per capita SDP of 31 Indian states and Union territories have been analyzed using panel unit root test assuming cross sectional independence among Indian states and later relaxing this assumption. The first generation panel unit root test assuming cross sectional independence shows that Indian per capita GDP data contains unit root. The second generation panel unit root test, relaxing the cross sectional independence assumption, also provides no evidence for mean reversion (stationarity) of Indian per capita GDP. Our results indicate that Indian output data is not reverting back to the natural rate and stabilization policies are required to bring the economy to the equilibrium path.

PurposeThe present paper makes an attempt to investigate the determinants that affect FDI inflows distribution among Indian states. Together with traditional determinants, the impact of institutional determinants on state-level FDI inflows distribution in India has been analysed.Design/methodology/approachThe study uses panel data for a period of 20 years (2000–2019) for 17 groups of Indian states (29 states and 7 UTs). The empirical evidence is based on the panel data method and the findings support Dunning's OLI theory. As the data for some indicators for the institutional environment is not available at the state level, hence we used component analysis to arrive at the single component for the institutional factor. The study takes into account corruption, legal system, industrial disputes, man-days lost, labour availability, political risk, protection of IPR and agglomeration as potential macroeconomic and institutional determinants.FindingsResults show that FDI inflows into Indian states is driven mainly by institutional environment. From our analysis, the author infers that the institutional variables such as legal system, IPR, corruption, political instability play an important role in determining the distribution of FDI inflows at the state level in India. Together with that GFCF and agglomeration are also important determinants of state-wise FDI inflows.Research limitations/implicationsThe major limitation of the study is that it doesn't include moderated impact of economic and institutional determinants of FDI inflows in Indian states, which can be an avenue for future research. Future research can also carried out taking district-level data to further examine the determinants at district level in India.Originality/valueThe contribution of the present paper is three-fold, first, the author constructs a measure of different institutional variables, after normalization of data for the period 2000–2019, and the author choose the highest explaining factor with the highest variance explained then we constructed the indices for select variable, which further has been used in the panel data analysis technique. The author has found that macroeconomic variables, as well as institutional variables, are significant to attract FDI at the state level in India. The paper shows that corruption, political risk, IPR and legal system are the major institutional determinants of FDI inflows in India at the state level. States with higher domestic investment attract more FDI inflows, moreover, agglomeration is a very important determinant as the investors are more confident in investing at the same location, the reason behind this may be that the investors want to avoid the registration procedure for new land, administrative formalities or they feel more secure at the same place and keen to invest at the same place again.

Weekly series of agricultural prices usually exhibit seasonal variations and the stationarity of these variations should be taken into account to analyse price relationships. However, unit root tests at seasonal frequencies are unlikely to have good power properties. Furthermore, movements in actual price series are often not as expected when unit roots are present. Therefore, stationarity tests at seasonal frequencies also need to be applied. In this paper, a procedure to test for the null hypothesis of stationarity at seasonal frequencies was extended to the weekly case. Once critical values were obtained by simulation exercises, unit root and stationarity tests were applied to weekly retail prices of different agricultural commodities in Spain. The most relevant finding was that many unit roots that seasonal unit root tests failed to reject did not seem to be present from the results of seasonal stationarity tests, whereas seasonal unit root tests led to the rejection of some unit roots that seemed to be present according to the results of seasonal stationarity tests. In conclusion, unit root tests should be complemented with stationarity tests before making decisions about the behaviour of seasonal patterns.

This paper presents results on the size and power of first generation panel unit root and stationarity tests obtained from a large scale simulation study. The tests developed in the following papers are included: Levin et al. (2002), Harris and Tzavalis (1999), Breitung (2000), Im et al. (19972003), Maddala and Wu (1999), Hadri (2000), and Hadri and Larsson (2005). Our simulation set-up is designed to address inter alia the following issues. First, we assess the performance as a function of the time and the cross-section dimensions. Second, we analyze the impact of serial correlation introduced by positive MA roots, known to have detrimental impact on time series unit root tests, on the performance. Third, we investigate the power of the panel unit root tests (and the size of the stationarity tests) for a variety of first order autoregressive coefficients. Fourth, we consider both of the two usual specifications of deterministic variables in the unit root literature.

The efficient market hypothesis explains the random walk hypothesis suggesting that stock prices are independent of each other, hence, it is impossible to earn abnormal profits. The positive effect of a well-functioning and highly efficient stock market on the performance of an economy motivated the Philippine Stock Exchange to pursue massive modernization initiatives. This research provides evidence of the existence of random walk in the Philippine stock market employing the Augmented Dickey-Fuller (1981) and Phillips-Perron (1988) unit root tests, the Lo-MacKinlay's (1988) conventional variance ratio test, and Chow-Denning's (1993) simple multiple variance ratio test. Results of the ADF and PP unit root tests confirm the necessary condition for a random walk. The Chow-Denning (1993) maximum /z/ statistic and the Wald test statistic as in Richardson and Smith (1991) for the joint hypotheses and the Lo and MacKinlay (1988) individual statistics variance ratio test generally accepted the null hypothesis of a random walk. That is, the unit root and variance ratio tests consistently indicate that the null hypothesis of random walk cannot be rejected. The existence of a random walk in weak-form efficiency can be attributed to market liquidity as a result of continuous development and modernization of the Philippine equity market.

Income and CO 2 emissions: Evidence from panel unit root and cointegration tests

Summary. The large sample effect of Markov switches on unit root inference is investigated. Attention is paid to semiparametric (unit root and stationarity) tests as well as to parametric (Dickey–Fuller type) tests. With respect to the existing literature, instead of relying on Monte Carlo simulation the analysis is carried out by focusing on an appropriate asymptotic theory for I(0) and I(1) processes under Markov switching. It is shown that Markov switches in the trend component can make I(0) and I(1) processes observationally equivalent and that unit root tests virtually have no power to detect stationary processes around switching trends, although autocorrelation-robust unit root tests are not affected by size distortions. Conversely, Markov switches in the mean of the transitory components do not change the usual asymptotic properties of the tests. Finally, it is shown that in large samples Markov-switching variances cause neither size distortions nor inconsistency of the tests.